KR102496286B1 - Methods for Tracking, Predicting, and Proactively Correcting Malocclusion and Related Problems - Google Patents

Abstract

Methods and systems for predicting future dental or orthodontic conditions are provided. In one aspect, a computer-implemented method for calculating the future position of an intraoral object in a patient's oral cavity is provided. The method may include receiving first and second digital data representative of an actual condition of the oral cavity at first and second points in time. The method may include processing data comprising the first and second digital data to determine a velocity of an intraoral object of the oral cavity over first and second time points. A future position of the object in the oral cavity at a future time point may be determined based on the velocity.

Description

Methods for Tracking, Predicting, and Proactively Correcting Malocclusion and Related Problems

Conventional methods and devices for identifying and characterizing the status of orthodontics may not be ideal in at least some cases. For example, conventional practice may reactively diagnose and treat orthodontic conditions only after the patient has already experienced symptoms (eg, pain, malocclusion), and proactively predict future conditions that have not yet occurred. It may not be ideal for identifying and treating. Additionally, although in some cases a medical professional can confirm the status of orthodontics by visual inspection, the amount and rate of change of a patient's dentition may not be easily characterized or communicated. To name a few examples, medical professionals note that patients' teeth are continually becoming more crooked, spaces between teeth are continually increasing, and wear to the enamel on the teeth continues to deteriorate (e.g., bruxism). The patient may have a blocked jaw, the patient has trouble sleeping (e.g., in the case of sleep apnea), or needs gum surgery (e.g., recession of gingiva). confirmed) can be indicated. However, medical professionals may have difficulty determining the specific, subtle changes that will occur, as well as future conditions that may occur as a result of these changes. Also, conventional methods may not be ideal for monitoring and predicting changes in a patient's dentition (eg, changes to the roots of the teeth) that cannot be detected by visual inspection.

Conventional techniques for characterizing and reporting the subtle changes associated with many orthodontic conditions may not be ideal in at least some cases. For example, prior art does not allow a medical professional or patient to visualize (eg, in three dimensions) any expected progression of an orthodontic condition. Other techniques will only provide a static view of orthodontic status and will not provide predictive information.

In view of the foregoing, it would be desirable to provide improved methods and devices for tracking, predicting and proactively preventing dental or orthodontic conditions such as malocclusion. Ideally, such methods and devices would allow changes to a patient's dentition to be accurately identified, predicted, quantified, and visualized.

International Publication WO2014/037778 (2014.03.13) Japanese Unexamined Patent Publication No. 11-047095 (1999.02.23)

Embodiments of the present disclosure provide systems, methods, and apparatus for predicting a patient's future dental and orthodontic status. In some embodiments, digital data representative of a patient's intraoral cavity at a plurality of different points in time is received and used to create a predicted digital representation of the oral cavity at future points in time. For example, digital data can be used to determine and extrapolate changes to one or more intraoral objects (e.g., teeth, gums, airways) to future time points, thereby making predictions with improved accuracy compared to predictions based on visual inspection alone. provides A patient's future unwanted tooth or orthodontic condition can be determined based on the predicted digital representation, so that it can be diagnosed and treated preferentially before the condition actually occurs or develops to a more advanced condition. In addition, the systems, methods, and devices herein may be used to generate one or more treatment options for a predicted condition to facilitate decision making by patients and/or physicians. Optionally, a digital representation of the predicted outcome obtained with the selected treatment option may be generated and displayed to provide further guidance to the patient and/or physician. Advantageously, the approaches provided herein allow for proactive diagnosis and correction of a variety of dental or orthodontic conditions, which can be beneficial in reducing the cost, duration, and/or difficulty of treatment.

In one aspect, a computer-implemented method for calculating a future position of an intraoral object in a patient's oral cavity includes receiving first digital data representative of an actual state of the oral cavity at a first time point, a second time point different from the first time point. and receiving second digital data representing the actual state of the oral cavity at the point in time. The method may include processing data comprising first and second digital data to determine a velocity of an intraoral object of the oral cavity over first and second time points. A future position of the object in the oral cavity at a future time point may be determined based on the velocity. The future position may be determined before the object in the oral cavity is in the future position.

Other objects and features of this disclosure will become apparent upon review of the specification, claims, and accompanying drawings.

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

The novel features of the present invention are set forth in detail in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description and the accompanying drawings, which set forth exemplary embodiments in which the principles of the present invention are employed.
1 is a front view illustrating an anatomical relationship of a jaw of a patient according to various embodiments.
2A shows details of a patient's lower jaw and provides a general indication of how the teeth move, according to various embodiments.
FIG. 2B shows the single tooth of FIG. 2A and defines how the distance of movement of the tooth may be determined, according to various embodiments.
3 illustrates a system for predicting a patient's future dental or orthodontic status, in accordance with various embodiments.
4A shows a schematic diagram of a set of teeth, according to various embodiments.
4B shows a schematic diagram of the set of teeth of FIG. 4A moved, in accordance with various embodiments.
4C shows a schematic diagram of the set of teeth of FIG. 4A compared to the set of teeth of FIG. 4B to determine a change in position of the set of teeth, according to various embodiments.
4D shows a schematic diagram of the expected future position of the set of teeth of FIG. 4B based on previously determined trajectories and magnitudes of changes, according to various embodiments.
5A shows a schematic diagram of a set of teeth including roots, in accordance with various embodiments.
5B shows a schematic diagram of the set of teeth of FIG. 5A moved, in accordance with various embodiments.
5C shows a schematic diagram of the set of teeth in FIG. 5A compared to the set of teeth in FIG. 5B to determine a change in position for the set of teeth based on root movement, in accordance with various embodiments.
5D shows a schematic diagram of an expected future position of the set of teeth of FIG. 5B based on a previously determined trajectory and magnitude of change, according to various embodiments.
6A shows a schematic diagram of a tooth, according to various embodiments.
FIG. 6B shows a schematic diagram of the tooth of FIG. 6A with a shape and size that changes over time, according to various embodiments.
6C shows a schematic diagram of the teeth of FIG. 6A compared to the teeth of FIG. 6B to determine shape and size changes of a set of teeth, according to various embodiments.
FIG. 6D shows a schematic diagram of an expected future shape of the tooth of FIG. 6B based on previously determined trajectories and magnitudes of shape and size changes, according to various embodiments.
7A shows a schematic diagram of a gum line, in accordance with various embodiments.
FIG. 7B shows a schematic diagram of the gum line of FIG. 7A with a position and shape that changes over time, according to various embodiments.
7C shows a schematic diagram of the gum line of FIG. 7A compared to the gum line of FIG. 7B to determine position and shape changes of the gum line, according to various embodiments.
7D shows a schematic diagram of the expected future location and shape of the gum line of FIG. 7B based on previously determined trajectories and magnitudes of shape changes, according to various embodiments.
8A shows a schematic diagram of linear extrapolation of tooth trajectories, according to various embodiments.
8B shows a schematic diagram of non-linear extrapolation of tooth trajectories, according to various embodiments.
9 illustrates a method of generating a predicted digital representation of a patient's oral cavity to determine the patient's future state, in accordance with various embodiments.
10A illustrates an algorithm for predicting and treating a patient's future condition, in accordance with various embodiments.
10B shows a continuation of the algorithm of FIG. 10A, according to various embodiments.
11A-11G illustrate a user interface for predicting a patient's future dental or orthodontic status, according to various embodiments.
12 shows a schematic diagram of a system for predicting a patient's future dental or orthodontic status, in accordance with various embodiments.
13 illustrates a method for calculating a change in an intraoral object to determine a future state of the intraoral object, according to various embodiments.

The present disclosure provides improved and more effective methods and systems for early detection and/or prediction of dental or orthodontic conditions. The methods and devices disclosed herein can be combined in many ways and used to diagnose or treat one or more of a number of oral conditions. In some embodiments, the methods and devices herein detect and predict various types of dental or orthodontic conditions that may occur in a patient, determine suitable treatment products and/or procedures to prevent or correct the condition; and/or to indicate a predictive outcome of administering a therapeutic product and/or procedure. The prediction method may include comparing sub-surface data and/or surface scan data for a tooth at a plurality of time points to determine changes in the position and/or shape of the tooth over time. and, for example, based on the determined change, to generate a prediction about the future position and/or shape of the tooth.

Such methods may provide improved prediction accuracy compared to methods relying only on visual inspection or static data. Changes to the oral cavity may be difficult to assess visually based on photographs, study casts, and/or notes from previous clinical examinations, as changes may be subtle and/or slow progressing among other issues. Because the differences between scans are very subtle, even changes based on intraoral scanning can be difficult to detect visually without the aid of digital geometric evaluation. However, these subtle changes over time are cumulative and, given enough time, can develop into bigger and more important problems. These problems may be prevented, reduced, or resolved based on use of embodiments of the present disclosure.

With the methods and systems of the present disclosure, dental or orthodontic conditions can be identified and predicted before they become more severe and/or less amenable to treatment. For example, in children, potentially dense teeth (e.g., because teeth are not yet strongly attached to the bone structure of the mouth, or growing teeth are smaller or less large) as primary teeth are removed and permanent teeth replace them. Crowded teeth) are easily identified, treated in a timely manner, and little force can be applied. Contrary to conventional responsive treatment approaches, the predictive methods of the present disclosure can detect dental or orthodontic conditions before the condition actually occurs or progresses to a more serious condition, allowing medical professionals to anticipate and proactively treat treatment at an early stage. to get you started on Advantageously, treatment for an early-stage orthodontic or dental condition is less difficult, less aggressive, less expensive, less time-consuming, and/or less painful than for conditions detected at a later stage. it's disgusting

In one aspect, a computer-implemented method of calculating a future position of a patient's oral cavity relative to an object in the oral cavity comprises receiving first digital data representative of an actual state of the oral cavity at a first point in time and a second point different from the first point in time. and receiving second digital data representing the actual state of the oral cavity at the point in time. The method may include processing data comprising the first and second digital data to determine a velocity of the oral cavity relative to an intraoral object over first and second time points. A future position of the object in the oral cavity at a future time point may be determined based on the velocity. The future position can be determined before the object in the oral cavity is in the future position.

In some embodiments, the first and second digital data may each include surface data or sub-surface data for one or more oral cavity.

In some embodiments, the method further comprises displaying a graphical representation of the intraoral object at a future location of the intraoral object on a user interface presented on the display.

In some embodiments, the method includes determining, based on the first and second digital data, a change in position relative to the intraoral object between the first and second time points and evaluating whether the change in position exceeds a predetermined threshold. It includes steps to The predetermined threshold may be received or determined in a variety of ways. For example, a predetermined threshold may be input by a user. Alternatively or in combination, the predetermined threshold may be determined based on one or more of one or more user preferences, patient characteristics, or values from dental or orthodontic literature. For example, a predetermined threshold may indicate an undesirable tooth or orthodontic condition.

If the position change exceeds a predetermined threshold, various actions may be performed. In some embodiments, the method further includes outputting an alert to the user in response to an assessment that the change in position exceeds the predetermined threshold. Alternatively or in combination, the method may further include generating a plurality of options for producing a desired dental or orthodontic result in response to an assessment that the positional change exceeds a predetermined threshold. A plurality of options may be displayed on a user interface presented on a display. The plurality of options may include a plurality of treatment options for unwanted dental or orthodontic conditions. In some embodiments, displaying the plurality of options includes displaying one or more of price information, treatment time information, complication treatment information, or insurance reimbursement information associated with each of the plurality of treatment options.

The present disclosure is applicable to various types of objects in the oral cavity located in and/or associated with the oral cavity. For example, an intraoral object may include one or more of a crown, root, gum, airway, palate, tongue, or jaw. The method may further include processing the data comprising the first and second digital data to determine a rate of change of one or more of the shape, size, or color of the object within the oral cavity. For example, objects in the oral cavity may include teeth, and the rate of change may include a rate of change in tooth shape. In another example, an intraoral object may include gums, and the rate of change may include a rate of change in gum shape.

The systems, methods, and devices presented herein can be used to predict linear and/or non-linear movement of objects within the oral cavity. In some embodiments, determining a future position of the object in the oral cavity includes determining a movement trajectory of the object in the oral cavity based on the velocity. The movement trajectory may be linear. In some embodiments, the method further includes determining a movement trajectory of the intraoral object based on the velocity over the first, second, and third time points. The movement trajectory may be non-linear. For example, the non-linear movement trajectory may include one or more of a change in movement direction or a change in movement speed. The future position of the object in the oral cavity may be determined based on the non-linear movement trajectory. Optionally, the method further comprises processing the data comprising the first, second, and third digital data to determine a force vector associated with the intraoral object over the first, second, and third time points. do.

In some embodiments, the method further comprises generating a predicted digital representation of the oral cavity at a future time point after the first and second time points based on the future position of the object in the oral cavity.

In some embodiments, determining a future position of the object within the oral cavity comprises extrapolating the velocity to a future time point using linear or non-linear extrapolation.

In some embodiments, the method further includes determining a future state of the oral cavity based on a future position of the subject within the oral cavity. Future conditions may include unwanted dental or orthodontic conditions predicted to occur at a future point in time if the oral cavity is left untreated. The future state can be determined before the future state occurs.

In some embodiments, one or more of receiving the first digital data, receiving the second digital data, processing the data, or determining a future location is performed with the aid of one or more processors.

In another aspect, a computer system for calculating a future position of a patient's oral cavity relative to an intraoral object includes one or more processors and memory. The memory, when executed by one or more processors, causes the system to receive first digital data representative of an actual state of the oral cavity at a first point in time and to receive second digital data representing an actual state of the oral cavity at a second point in time different from the first point in time. It may contain instructions to do so. The instructions may cause the system to process data comprising the first and second digital data to determine a velocity of the oral cavity relative to an intraoral object over the first and second time points. The instructions may cause the system to determine a future position of the intraoral object at a future time point based on the velocity, the future position being determined prior to the intraoral object being at the future position.

In another aspect, a computer-implemented method for calculating a change in position of a patient's oral cavity relative to an object in the oral cavity over time comprises receiving first digital data representative of an actual state of the oral cavity at a first point in time that differs from the first point in time. and receiving second digital data representing the actual state of the oral cavity at a second different time point. The method may include processing data comprising the first and second digital data to determine a change in position of the subject within the oral cavity between the first and second time points. The method may include evaluating whether the position change exceeds a predetermined threshold.

In some embodiments, the method further includes outputting an alert to the user in response to an assessment that the change in position exceeds the predetermined threshold. Optionally, the method comprises generating a plurality of options for producing a desired dental or orthodontic result in response to an assessment that the change in position exceeds a predetermined threshold and presenting the plurality of options on a user interface presented on a display. steps may be included. The predetermined threshold may be determined based on one or more of user preferences, patient characteristics, or values from dental or orthodontic literature.

In another aspect, a computer system for calculating a change in position of a patient's oral cavity relative to an intraoral object over time includes one or more processors and memory. The memory, when executed by one or more processors, causes the system to receive first digital data representative of an actual state of the oral cavity at a first point in time and to receive second digital data representing an actual state of the oral cavity at a second point in time different from the first point in time. It may contain instructions to do so. The instructions may cause the system to process data comprising the first and second digital data to determine a change in position relative to the intraoral object between the first and second time points. The command may cause the system to evaluate whether the position change exceeds a predetermined threshold.

In another aspect, a method for generating a predicted digital representation of a patient's oral cavity to determine the patient's future condition is provided. The method may include receiving first digital data representative of the oral cavity at a first point in time and receiving second digital data representative of the oral cavity at a second point in time different from the first point in time. A predicted digital representation of the oral cavity at a future time point after the first and second time points may be generated based on the first and second digital data. The future state of the oral cavity can be determined in response to the predicted digital representation. Future conditions may include unwanted dental or orthodontic conditions predicted to occur at a future point in time if the oral cavity is left untreated. A future state may be determined prior to the occurrence of the future state.

Various types of digital data are suitable for use with the present disclosure. In some embodiments, the first and second digital data each include three-dimensional data of the oral cavity. Alternatively or in combination, the first and second digital data may each include two-dimensional data of the oral cavity. In some embodiments, the first and second digital data each include one or more scans of the oral cavity. The first and second digital data may each include surface data of the oral cavity, and the surface data may include scan data representing a 3D surface topography of the oral cavity.

Alternatively or in combination, the first and second digital data may each include sub-surface data of the oral cavity. The sub-surface data may include one or more of X-ray data, cone beam computed tomography (CBCT) data, CAT scan data, magnetic resonance imaging (MRI) data, or ultrasound data. For example, the sub-surface data may include representations of one or more patient's tooth roots. Accordingly, the predicted digital representation of the oral cavity may include a predicted digital representation of one or more roots at a future time point based on the sub-surface data, and a future state may be determined based on the predicted digital representation of the one or more roots. can Optionally, the sub-surface data includes a representation of one or more of the patient's airway, jaw, or bone.

In some embodiments, the first and second time points differ by at least 1 month, at least 3 months, at least 6 months, or at least 1 year. The future time point may be at least 1 month, at least 3 months, at least 6 months, at least 1 year, at least 2 years, or at least 5 years after the first and second time points.

Digital data from a plurality of different time points can be acquired and analyzed to monitor the progress of the patient's oral cavity over time and predict its future state. For example, in some embodiments, the method further includes receiving third digital data representative of the oral cavity at a third time point different from the first and second time points. A predicted digital representation may be generated based on the first, second, and third digital data.

The predictive approach herein may use other types of data in addition to oral digital data. In some embodiments, the method further comprises receiving additional data of the patient, and a predicted digital representation is generated based on the additional data. Additional data may include one or more of demographic information, lifestyle information, medical information, medical history, family history, or genetic factors.

The predictive techniques discussed herein can be implemented in many ways. In some embodiments, generating the predicted digital representation includes generating a comparison of the first and second digital data. Generating the comparison may include registering the first and second digital data with each other in a common coordinate system. Optionally, generating the comparison comprises measuring a property of the intraoral subject at a first time point, measuring a property of the intraoral object at a second time point, and measuring a property of the intraoral object between the first and second time points. and determining a change to the property. For example, an intraoral object may include one or more teeth or gums, and a characteristic may include one or more of the position, orientation, shape, size, or color of the teeth or gums.

In some embodiments, the method includes comparing the measured characteristic at one or more of the first or second time points with the measured characteristic from the patient information database. Comparing the characteristic measured at one or more of the first or second time points with the measured characteristic from the patient information database comprises one of demographic information, lifestyle information, medical information, medical history, family history, or genetic factors. can be based on the above.

Changes to one or more objects in the oral cavity can be used as a basis for predicting the future condition of the patient's oral cavity. For example, in some embodiments, the method further comprises predicting a future change in a characteristic of the intraoral object in response to the determined change, and the predicted digital representation is generated based on the future change. Future changes can be predicted in response to selected time intervals.

In some embodiments, the predicted digital representation is generated in response to the determined change in a characteristic of the intraoral object between the first and second time points. Determining the change to the characteristic may include determining one or more of a rate of tooth movement, a rate of change in tooth shape, a rate of change in tooth size, or a rate of change in gum shape. For example, generating the predicted digital representation may include determining a rate of change of a characteristic of the intraoral object based on the determined change between the first and second time points. The rate of change can be extrapolated to future time points in order to predict properties of objects in the oral cavity at future time points.

The present disclosure may be used to predict the future state of various types of objects in the oral cavity, such as the future position of one or more teeth. In some embodiments, for example, the predicted digital representation represents the patient's teeth in a predicted arrangement at a future time point. Accordingly, generating a predicted digital representation includes generating a first digital model representing the patient's teeth in a first configuration at a first time point, and a second digital model representing the patient's teeth in a second configuration at a second time point. and calculating a tooth movement speed for one or more teeth between the first and second arrangements. One or more teeth of the second digital model may be repositioned according to the speed of tooth movement to create a predicted arrangement of the patient's teeth at a future time point. Optionally, repositioning the one or more teeth of the second digital model includes detecting a collision occurring between the one or more teeth during the repositioning of the one or more teeth and changing the speed of tooth movement in response to the detected collision. can include

Many different types of teeth or orthodontic conditions can be predicted using the examples provided herein. In some embodiments, the unwanted tooth or orthodontic condition includes one or more of: malocclusion, tooth decay, loss of one or more teeth, root resorption, periodontal disease, gum recession, temporomandibular joint disorder, teeth grinding. , airway obstruction, or sleep apnea. A future condition may be determined by measuring one or more parameters of a predicted digital representation representing an undesirable tooth or orthodontic condition. The one or more parameters may include one or more of the following: amount of over bite, amount of under bite, amount of tooth tipping, amount of protrusion of teeth, amount of intrusion of teeth, amount of rotation of teeth, amount of movement of teeth, teeth It may include the amount of spacing, amount of compaction of teeth, amount of wear on teeth, amount of gum recession, jaw width, or palate width. In some embodiments, the method further comprises comparing one or more parameters to one or more thresholds to determine whether the undesirable tooth or orthodontic condition is predicted to occur at a future time. An alarm can be raised if an anomaly is detected in one or more parameters. Optionally, the method may further include generating a user interface presented on the display, the user interface configured to display one or more parameters.

The results of the predictive techniques presented herein may be displayed to the user. In some embodiments, the method further includes generating a user interface presented on the display. The user interface may be configured to display a predicted three-dimensional model representing a predicted digital representation of the oral cavity at a future point in time. The user interface may be configured to display a first 3D model representing the oral cavity at a first viewpoint and a second 3D model representing the oral cavity at a second viewpoint. Optionally, the user interface is configured to display an overlay of two or more of the first three-dimensional model, the second three-dimensional model, or the predicted three-dimensional model.

Once the future condition is identified, potential treatment options can be created and displayed to the user. Accordingly, in some embodiments, the method further includes generating one or more treatment options for the future condition and displaying the one or more treatment options on a user interface presented on the display. One or more treatment options may include a list of one or more treatment products or procedures for a future condition. The listing of one or more treatment products or procedures may include one or more of price information, treatment time information, complication treatment information, or insurance reimbursement information. Optionally, the method may further include generating a comparison of one or more treatment options and presenting the comparison on a user interface presented on the display. Generating the comparison may include comparing one or more of treatment efficiency, cost, duration, or predicted outcome.

Some embodiments of the present disclosure provide predictions of treatment outcomes that may be achieved by administering one or more treatment options to a patient. In some embodiments, the method includes generating a second predicted digital representation of the oral cavity after administering at least one of the one or more treatment options, using a user interface presented on a display over time to the second predicted digital representation; and displaying a second predicted three-dimensional model representing the digital representation.

In some embodiments, the method includes receiving user input selecting at least one treatment option from among one or more treatment options via a user interface presented on a display, and selecting at least one treatment option in response to the received user input. It further includes the step of generating an order for. Optionally, the order is created based on one or more of the first or second digital data.

In another aspect, a system for generating a predicted digital representation of a patient's oral cavity to determine the patient's future condition is provided. A system may include one or more processors and memory. The memory may include instructions executable by one or more processors to cause the system to receive first digital data representing the mouth at a first point in time and second digital data representing the mouth at a second point in time different from the first point in time. there is. The instructions may cause the system to generate a predicted digital representation of the oral cavity at a future time point after the first and second time points based on the first and second digital data. The command may cause the system to determine a future state of the oral cavity in response to the predicted digital representation. The future condition may include an unwanted tooth or orthodontic condition predicted to occur at a future point in time if the oral cavity is left untreated. The future state can be determined before the future state occurs.

In some embodiments, the system further includes a display, and the commands cause the display to create a user interface. The user interface may be configured to display one or more of the following: a predicted three-dimensional model representing a predicted digital representation of the oral cavity, a first three-dimensional model representing the oral cavity at a first viewpoint, and representing the oral cavity at a second viewpoint. A second three-dimensional model, or one or more treatment options for a future condition. In some embodiments, the user interface is configured to display one or more treatment options and to receive user input selecting at least one treatment option from the one or more treatment options. Optionally, the user interface is configured to display a second three-dimensional model representing a second predicted digital representation of the oral cavity, at least after implementation of the treatment option. In some embodiments, the user interface is configured to display a comparison of one or more treatment options.

Although certain embodiments herein are presented in the context of predicting future dental arrangements, this is not intended to be limiting, and the systems, methods, and devices of the present disclosure may also be used to: However, it will be appreciated that it can be applied to extrapolate future conditions for other types of tissues or objects in the oral cavity.

Referring now to the drawings, FIG. 1 shows a skull 10 having an upper jawbone 22 and a lower jawbone 20 . The lower jawbone 20 is hinged at a joint 30 to the skull 10. Joint 30 is called the temporomandibular joint (TMJ). Upper jawbone 22 is associated with upper jaw 101 and lower jawbone 20 is associated with lower jaw 100 .

A computer model of the jaws 100, 101 can be created, and the computer simulation models the interactions between the teeth on the jaws 100, 101. The computer simulation allows the system to focus on motion involving contact between the teeth mounted on the jaw. Computer simulations enable the system to render physically accurate actual jaw motion when jaws 100, 101 are in contact with each other. In addition, the model is “tooth guided” in which the protrusive motion, lateral motion, and path of the lower jaw 100 are guided by tooth contact rather than by the anatomical limits of the jaws 100, 101. “It can be used to simulate jaw movements that include movement. Movement can be determined for one jaw, but can also be determined for both jaws representing a bite.

Referring now to FIG. 2A , lower jaw 100 includes, for example, a plurality of teeth 102 . At least some of these teeth may be moved from an initial tooth arrangement to a subsequent tooth arrangement. As a frame of reference representing how the tooth has been moved, an arbitrary center line (CL) can be drawn through the tooth. Referring to the centerline CL, each tooth movement can be tracked in orthogonal directions represented by axes 104, 106, and 108 (104 being the centerline). The centerline can be rotated about axis 108 (root angle) and axis 104 (torque) as indicated by arrows 110 and 112, respectively. Also, the teeth can be rotated about the centerline. Thus, all possible free form motions of the teeth can be tracked. These motions include translation (e.g., in one or more of the X-axis or Y-axis), rotation (e.g., movement about the Z-axis), projection (e.g., around the Z-axis), to name a few. , movement in the Z-axis), or tipping (eg, movement about one or more of the X-axis or the Y-axis). In addition to tooth movement, movement of gum line 114 may be tracked using models such as model 100 . In some embodiments, the model includes X-ray information of the jaws so that the movement of the roots of the teeth can be well tracked.

2B shows how any amount of tooth movement can be defined in terms of the maximum linear movement of any point P on tooth 102 . Each point P ₁ may undergo cumulative movement as the tooth is moved in any orthogonal or rotational direction defined in FIG. 2A . That is, the points generally follow a non-linear path, but there may be linear distances between any points on the teeth if determined twice arbitrarily during treatment. Thus, any point P ₁ can undergo in fact true side-to-side movement as indicated by arrow d ₁ , and a second arbitrary point P ₂ follows an arcuate path. may result in a final movement (d ₂ ). Many aspects of the present disclosure can be defined in terms of the maximum allowable movement of a point P ₁ induced on any particular tooth. This maximum tooth movement can be defined as the maximum linear movement of that point P ₁ on the tooth which in turn has the maximum movement relative to that tooth at any treatment stage.

The present disclosure provides systems, methods, and devices for monitoring and tracking changes to one or more structures of the oral cavity, including but not limited to teeth, gums, jaws, TMJ joints, tongue, palate, and airways. Examples of such changes include one or more tooth movements (eg, protrusion, depression, rotation, torque, tipping, or translation); a change in size, shape, and/or color to one or more teeth; changes in size, shape, and/or color of gums associated with one or more teeth; changes to the bite relationship between the upper and lower jaws (“bite”); changes in the width of the jaw and/or palate; change in the position of the tongue; or a change in the shape of the airway.

In some embodiments, changes in the patient's oral cavity may result in and/or indicate one or more teeth or orthodontic conditions. As used herein, the term “condition” refers to a disease, disorder, or other undesirable, abnormal, and/or dysfunctional condition present in a patient. Examples of such conditions include, but are not limited to: malocclusion (e.g., tooth crowding, tooth spacing, overbite, overjet, underbite ( underbite, crossbite, open bite), tooth decay, loss of one or more teeth, root resorption, periodontal disease (e.g., gingivitis, periodontitis), gum recession, temporomandibular joint disorders, teeth grinding, airway obstruction, and sleep apnea. Condition, as used herein, is defined as an unsatisfactory or unsuccessful outcome of treatment or other therapeutic intervention (e.g., deviation of a tooth from its defined dental alignment in an orthodontic treatment plan, failure of sleep apnea treatment to achieve its intended outcome, etc.) can be distinguished.

In some embodiments, certain changes in the oral cavity are indicative of and/or associated with future undesirable dental or orthodontic conditions that have not yet occurred. For example, certain tooth movements, if allowed to proceed, may result in future malocclusions. As another example, certain changes to the shape of the gums may indicate future gum recession. As another example, insufficient width of the patient's dental arch and/or palate may be associated with an increased likelihood of sleep apnea due to, for example, posterior displacement of the tongue. In another example, changes to the coloration of teeth and/or gums may indicate tooth decay and/or periodontal disease.

Accordingly, the present disclosure provides systems, methods, and apparatus for predicting a patient's future condition by monitoring and tracking changes in the oral cavity over time. In some embodiments, oral data is captured over multiple different time points to detect changes in the oral cavity. Based on the detected changes, predictions can be made about the condition of the oral cavity at a future time point (eg, the location, shape, size, etc., of one or more objects in the oral cavity). A predicted future condition may occur at a future time, such as future malocclusion, tooth decay, loss of one or more teeth, root resorption, periodontal disease, gum recession, temporomandibular joint disorder, teeth grinding, airway obstruction, and/or sleep apnea. can be analyzed to identify any future teeth or orthodontic conditions in the teeth. Once a future condition is identified, potential treatment options (eg, to prevent or correct the condition) can be created and provided to the physician and/or patient for review. In some embodiments, this approach is implemented using computer-based methods with digital modeling to predict future conditions and detect changes to the oral cavity with improved sensitivity and accuracy compared to conventional visual inspection.

In some embodiments, the systems, methods, and apparatus of the present disclosure are used to predict future states prior to their occurrence. For example, some embodiments of the present disclosure may be used to predict future malocclusion of a patient's teeth even if the patient's current tooth arrangement is normal. As another example, some embodiments herein can be used to predict that a patient will suffer from sleep apnea in the future, even if the patient is not currently experiencing any sleep apnea. The approaches herein may allow predicting future dental or orthodontic conditions months or years before the condition actually appears in a patient, allowing such conditions to be treated proactively and prophylactically.

3 shows a system 300 for predicting a patient's future dental or orthodontic status. The system 300 provides a plurality of different digital data 302 representing the oral cavity at a first time point and second digital data 304 representing the oral cavity at a second time point (eg, after the first time point). It includes a plurality of data sets representing the oral cavity of a patient at a time point. If desired, digital data for additional points in time (eg, third digital data for a third point in time, fourth digital data for a fourth point in time, etc.) may also be included. Digital data is the actual state of one or more objects in the mouth (e.g., teeth, gums, jaws, TMJ joints, tongue, palate, etc.), such as a representation of the object's position, shape, size, color, etc. expression can be provided. For example, first digital data 302 may include a three-dimensional model representing the arrangement of one or more teeth and/or gums at a first point in time, and second digital data 304 may include one or more teeth and/or gums at a second point in time. It may include a three-dimensional model representing the arrangement of teeth and/or gums. Alternatively or in combination, the digital data may provide data for other parts of the oral cavity other than the teeth and surrounding tissue, such as the patient's jaw or airways. These data will provide a more complete understanding of how the various structures of the oral cavity interact to create a tooth or orthodontic condition, as well as how these interactions can be corrected to reduce or treat a tooth or orthodontic condition. can For example, treatment of sleep apnea includes alignment of the patient's jaw and mouth (e.g., forward movement of the mandible to tighten the tissues of the airway), as well as correction of the position of the teeth (e.g., moving the tongue). expansion of the palate for forward movement). Data representative of the actual condition of the oral cavity is data representing an expected, desired, or ideal state of the oral cavity, e.g., a state desired to be achieved by the practice of teeth or orthodontic treatment, such as the targeted arrangement of teeth in an orthodontic treatment plan. It should be understood that it can be distinguished from data.

of various types of data, such as two-dimensional data (eg, photographs, radiographs, or other types of images) or three-dimensional data (eg, scan data, surface topography data, and three-dimensional models constructed from two-dimensional data). Digital data is suitable for use with the embodiments presented herein. Digital data can be static (eg images) or dynamic (eg video). The digital data may provide a representation of the size, shape, and/or surface topography of one or more intraoral objects, such as a scan or image showing the position and orientation of a patient's teeth, gums, and the like. In addition, the digital data can be used for different intraoral objects, such as bite registration data indicating the occlusal relationship between the upper and lower jaws, cephalometric analysis data, facebow measurement data indicating the position of the TMJ relative to the dental arch, and the like. It may also provide representations of spatial relationships with each other (eg, relative positions and orientations). A number of different types of digital data can be combined with each other to form a digital model that accurately represents the surface and/or sub-surface cavities in the oral cavity of a patient at a particular point in time. For example, two-dimensional data may be combined with three-dimensional data, as discussed further herein.

In some embodiments, the digital data includes scan data of the patient's oral cavity, such as one or more three-dimensional scans. A three-dimensional intraoral scan can, for example, confocalize an array of beams to determine surface topography (e.g., the iTero™ and iOC™ scanning systems available from Align Technology, Inc. of San Jose, CA). This can be done using an intraoral scanner using a confocal lens. The scan data may provide a digital representation of the three-dimensional surface topography of intraoral objects such as teeth and/or gums. For example, with a three-dimensional intraoral scan, small changes in the dentition can be accurately captured and visualized in a relatively non-invasive manner. Three-dimensional intraoral scans may not use ionizing radiation, making the procedure very safe compared to other techniques that can use X-rays (eg cone beam computed tomography (CBCT) or CAT scans). A three-dimensional intraoral scan also has sufficient accuracy and accuracy to detect minor or subtle changes that may be difficult to detect with alternative methods (e.g., dental impressions such as silicone or alginate, visual inspection). resolution (eg, 20-50 microns). In some embodiments, the scan data is segmented to isolate individual teeth from each other and/or from the gums to provide an operable three-dimensional representation of each tooth. Alternatively, unsegmented scan data may be used.

In some embodiments, the digital data provides surface data representing one or more visible external surfaces of the oral cavity (eg, tooth surfaces located above the gum line, such as crowns, gum surfaces, tongue, etc.). Surface data can be obtained using intraoral scanning as discussed herein and above. Alternatively or in combination, the digital data may include one or more sub-surface structures of the oral cavity not visible in the surface scan data (e.g., the portion of the tooth located below the gum line, such as root, bone, muscle, jaw, airway, etc.) Indicative sub-surface data can be provided. Sub-surface data may include X-ray data (eg bitewings, periapical X-rays, cephalographs, panography), CBCT data, CAT scan data, magnetic resonance It may include at least one of image (MRI) data and ultrasound data. Sub-surface data can be two-dimensional (eg, image) or three-dimensional (eg, volumetric data).

In some embodiments, sub-surface data may be combined with surface data to create a three-dimensional digital representation of the oral cavity that includes both surfaces and sub-surface structures. For example, surface data and sub-surface data may be combined to create a three-dimensional representation of the entire tooth structure including crown, root, and gum. Root data can help improve understanding of tooth movement, especially with respect to non-linear changes in the speed and/or direction of movement, and thus will be useful for more accurately predicting future tooth movement. can For example, a change in root shape due to root collision, erosion, or resorption may affect tooth movement. Some types of tooth movement may be "root-first" movements in which the root drives the corresponding movement of the crown. In some embodiments, three-dimensional surface data for one or more crowns may be obtained using scanning. Three-dimensional sub-surface data for one or more tooth roots corresponding to a crown may be obtained using CBCT scan data. Alternatively or in combination, two-dimensional X-ray images or other images can be stitched together to form a three-dimensional representation of the root. Optionally, the crown data and root data may be each segmented into individual tooth components so that the components can be individually manipulated. The crown data and root data can be digitally combined to create a three-dimensional model of the entire tooth using, for example, surface matching. In some embodiments, a surface matching algorithm uses crown surface data to match and orient root data to the correct location of each tooth. Matching may be performed in a three-dimensional space based on landmarks (eg, gum edges, occlusal ridges). The accuracy and speed of matching can vary depending on the amount of surface data used in the matching process. Once a root is matched and positioned correctly, an algorithm can sample the root's data to generate root surface data. The root surface data can be stitched to the crown surface to create a tooth model.

The point in time at which digital data is generated and/or acquired can be varied as desired. For example, each time point is at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, at least 7 months, at least 8 months, at least 9 months, at least 10 months, at least 11 months , at least 1 year, at least 2 years, at least 5 years, or any other extended time interval sufficient for accurate detection of changes and/or predictions. The interval between each time point can be the same or different, as desired. Optionally, the interval between time points may be shorter for patients expected to undergo more changes (eg pediatric patients) and less likely for patients expected to undergo less change (eg adult patients). can be long In some embodiments, each set of digital data is obtained at a different point in time, while in other embodiments, at least some sets of digital data may be obtained at the same point in time. In some embodiments, the digital data is obtained during regular dental examinations (eg, annual or semi-annual examinations) so that the timing corresponds to the time of the examination. Optionally, the digital data may be obtained before and/or after a surgical procedure on the patient's oral cavity, in which case it may be advantageous to obtain the digital data at very short time intervals for more accurate monitoring.

Optionally, system 300 may include one or more sets of additional data 306 . Additional data 306 may include demographic information (eg age, gender, race), lifestyle information (eg physical activity level, smoking status, medication status, alcohol consumption status, dietary habits, oral hygiene). habits), medical information (eg, height, weight, body mass index (BMI)), medical history, family history, and/or genetic factors, and any data of the patient potentially related to dental or orthodontic health. can For example, these patient-specific factors may affect the likelihood that a particular dental or orthodontic condition will occur. The additional data may be obtained at a single point in time or at a plurality of different points in time, and a point in time may or may not correspond to a point in time for the digital data 302, 304.

In some embodiments, the digital data and/or additional data used to generate predictions as discussed herein includes one or more of the following items: a two-dimensional image, a three-dimensional image, generated from one or more two-dimensional images. 3D image, CBCT data, 3D scan data, video data, cephalometric analysis data, plaster model analysis data, growth prediction, bite relationship, similar patient and/or treatment history data, race , gender, age, dietary habits, whether the patient has experienced sleep disorders, whether the patient snores, sleep apnea diagnostic data, titration test data for oral sleep appliances, bruxism, and/or analytical data from therapists.

The first digital data 302 , the second digital data 304 , and/or the additional data 306 are used to create a predicted digital representation 308 of the patient's oral cavity. The predicted digital representation 308 may be a two-dimensional or three-dimensional model of the patient's oral cavity at future time points after the first and second time points. At least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, at least 7 months, at least 8 months, at least 9 months, at least 10 months, at least 11 months, at least 1 year , at least 2 years, at least 5 years, at least 10 years, at least 20 years, or any other desired length of time since the last time the digital data was obtained. In some embodiments, the predicted digital representation 308 represents a predicted state (eg, position, shape, size, color, etc.) of one or more intraoral objects at a future time point.

The predicted digital representation 308 can be generated in a variety of ways. In some embodiments, a comparison of oral digital data (eg, first digital data 302 and second digital data 304 ) at multiple time points is generated to determine changes in the oral cavity over time. For example, one or more characteristics (e.g., position, orientation, shape, size, color) of one or more intraoral objects (e.g., teeth, gums, jaws, TMJ, palate, airways) can be obtained using digital data. It can be measured at each of a plurality of different time points. By comparing measurements obtained at different time points, the rate, magnitude, direction, location, etc., of changes to objects in the oral cavity can be determined. These changes can be extrapolated to future time points to predict the future state of the subject in the oral cavity. Thus, a predicted digital representation 308 of the oral cavity can be generated by repeating this process for each intraoral object of interest. An example method for generating the predicted digital representation 308 is described in more detail below.

Alternatively or in combination, the predicted digital representation 308 may be similar to patient data (eg, digital data, additional data, measured characteristics, determined changes) (eg, stored in a patient's information database). Can be generated based on comparison of historical data of. For example, the historical data may be data of a patient whose characteristics are similar or closely matched to the current patient's situation. Similarities may include occlusion, tooth position, tooth shape, tooth movement speed, and tooth shape change rate, to name a few. Optionally, similarities may be based on additional patient-specific factors described herein, such as patients having similar demographic information, lifestyle information, medical information, medical history, family history, and/or genetic factors. The determined changes to the patient's oral cavity can be compared to data relating to similar changes in similar patients available from patient databases. In some embodiments, the determined rate, magnitude, direction, and/or location of change to one or more intraoral objects may be adjusted based on past patient data. Past patient data can be used to predict future outcomes for changes in the patient's oral cavity to create a predicted digital representation 308 .

The predicted digital representation 308 is used to predict the future state 310 of the patient's oral cavity. As discussed herein and above, future condition 310 is an unwanted dental or orthodontic condition predicted to occur at a future time if the oral cavity is left untreated (e.g., malocclusion, tooth decay, loss of one or more teeth, root resorption, periodontal disease, gum recession, TMJ disorder, teeth grinding, airway obstruction, sleep apnea, etc.). As used herein, "untreated" refers to the absence of treatment for a particular condition, and does not necessarily mean that the patient is not receiving treatment for another condition. For example, the future state 310 may be a malocclusion predicted to occur in the future if the patient does not receive treatment to correct or prevent the malocclusion. As another example, future condition 310 may be gum recession predicted to occur in the future if the patient does not receive treatment to correct or prevent gum recession.

In some embodiments, the future state 310 is predicted by analyzing the predicted digital representation 308 to identify whether any undesirable teeth or orthodontic conditions are present at a future time point. For example, the position of one or more teeth of the predicted digital representation 308 can be evaluated to determine if a malocclusion exists. As another example, the position of the gum line in the predicted digital representation 308 can be evaluated to determine if an excessive amount of gum recession has occurred. Optionally, one or more parameters indicative of the undesirable state may include an amount of overbite, an amount of underbite, a tooth tipping amount, a tooth protrusion amount, a tooth intrusion amount, a tooth rotation amount, a tooth movement amount, a tooth spacing amount, a tooth It can be measured using a predicted digital representation, such as crowding amount, tooth wear amount, gum recession amount, jaw width, and/or palate width. The measured parameter may be compared to an expected range and/or threshold for the parameter to determine if the undesired condition will occur at a future point in time.

Based on the predicted future condition 310 , one or more treatment options 312 may be generated. Treatment options may include products and/or procedures for correcting and/or preventing predicted dental or orthodontic conditions. Exemplary therapeutic products include orthodontic appliances (eg, aligners or braces, retainers, sleep apnea devices, mouth guards, dental splints, bite plates), implants and restorations. (eg crowns or bridges, prosthetics such as fillers), and pharmaceuticals (eg antibiotics, mouthwashes, toothpaste). Exemplary treatment procedures include orthodontic surgery (e.g., orthognathic surgery, periodontal surgery), correction of dentition and/or other intraoral objects (e.g., orthodontics, tooth extraction, space maintenance, space supervision). ), space restoration, interproximal reduction (IPR), distillation, palate dilatation, occlusion adjustment), correction of oral hygiene habits (e.g. brushing teeth, flossing, mouthwash use), and lifestyle changes (e.g. , dietary habits, level of physical activity, smoking status, drug use status, and alcohol consumption status).

Some examples of treatment options that correspond to dental or orthodontic conditions include: airway obstruction or sleep apnea (e.g., sleep apnea devices such as mandibular forward moving devices, corrective surgery, palate expansion, etc.), crowding of teeth ( e.g., tooth extraction, IPR, distillation, palate extension, tooth extraction, orthodontics, etc.), at least one missing tooth (e.g., sutures, implants, orthodontic surgery, etc.), dental space problems (e.g., sutures, IPR, extraction, palate enlargement, corrective surgery, etc.), gum disease (eg, corrective surgery, better hygiene, mouthwash, etc. recommended), gum recession (eg, corrective surgery, better hygiene, mouthwash, etc. recommended) recommended), TMJ disorders (eg, jaw repositioning surgery, jaw repositioning devices, etc.), occlusion mismatch (eg, corrective surgery, orthodontic appliances, etc.), overbite (eg, orthodontic appliances, orthodontics, etc.) , cross bite (eg, orthodontic appliance, arch extension, orthodontic, etc.), open bite (eg, orthodontic appliance, corrective surgery, orthodontic, etc.), overjet (eg, orthodontic appliance, orthodontic, etc.) ); e.g., implants, etc.), or teeth grinding (e.g., orthodontic appliances, occlusal adjustments, etc.)

Treatment options 312 may be presented as a list of treatment products and/or procedures. Optionally, the list may include one or more of price information for treatment options, treatment time information, complication treatment information, or insurance reimbursement information. The list of treatment options may be ranked in a variety of ways by effectiveness as therapy, cost, time of treatment, suitability for the patient or patient, insurance reimbursement, to name a few. The listing may also include hyperlinks to preferred suppliers and/or healthcare professionals for therapeutic products and/or procedures. For example, an airway problem may be identified and one or more airway specialists may be recommended.

Optionally, predictive results 314 of one or more treatment options may be generated. Prediction result 314 may represent a predicted state (eg, position, shape, size, color, etc.) of one or more intraoral subjects at a future time after implementation of the selected treatment option. At least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, at least 7 months, at least 8 months, at least 9 months, at least 10 months, at least 11 months, at least 1 year , at least 2 years, at least 5 years, at least 10 years, at least 20 years, or any other desired length of time after implementation of the treatment option. Future time points may be determined based on patient activities or life events (eg, weddings, vacations, business trips, etc.). In some embodiments, generating predicted results 314 includes generating one or more models (eg, two-dimensional or three-dimensional models) representing the patient's oral cavity after the treatment is applied.

The predicted treatment outcome 314 can be generated using a variety of techniques. For example, results 314 may include previously obtained data about the patient's oral cavity and/or other relevant patient data, such as first digital data 302, second digital data 304, and/or additional data. (306). In some embodiments, the treatment outcome 314 is determined based on a comparison to historical data of a similar patient, eg, a patient with characteristics similar to the current patient. Optionally, past treatment data indicating results of similar treatments may be used to predict the outcome of applying the treatment to a current patient.

Optionally, in some embodiments, the current condition 316 of the patient's oral cavity is also determined. An existing condition is an unwanted tooth or orthodontic condition that has already occurred and is currently present in the patient's mouth (e.g., malocclusion, tooth decay, loss of one or more teeth, root resorption, periodontal disease, gum recession, temporomandibular joint disorder, bruxism). , airway obstruction, sleep apnea, etc.). In some embodiments, presence state 316 analyzes previous and/or current data (eg, first digital data 302 and second digital data 304) of the patient's oral cavity to determine any unwanted teeth. or predicted by identifying if a calibration state currently exists. The identification of an existing state from digital data can be performed similarly to the identification of a future state from a predicted digital representation discussed herein and above. Treatment options 312 and/or predicted outcomes 314 for an existing condition 316 may be generated, similar to the procedures discussed herein for a future condition 310 . It will be appreciated that embodiments herein presented in the context of detecting and treating predicted future conditions are equally applicable to detection and treatment of existing conditions.

As discussed above and herein, a predicted digital representation of the patient's mouth at a future time point can be generated by comparing digital data of the mouth obtained at different points in time. In some embodiments, the digital data may be compared to determine changes to one or more characteristics (eg, position, size, shape, color, etc.) of the intraoral subject over time. Various methods of comparing digital data sets to each other can be used to identify changes to intraoral objects. In some embodiments, two or more sets of digital data are registered with each other within a common coordinate system. The approach of the present disclosure is to combine a two-dimensional digital data set into another two-dimensional digital data set (e.g., two images), a three-dimensional digital data set into another three-dimensional digital data set (e.g., two images), as desired. three-dimensional model), and/or to register a two-dimensional digital data set into a three-dimensional digital data set or vice versa (eg, an image into a three-dimensional model). By registering data with respect to each other in a single coordinate system, a frame of reference for measurements is established.

For example, digital data of a tooth obtained at different time points (e.g., a stitched three-dimensional model of the crown and root as discussed herein) can determine a unique identifier or the tip or edge of a tooth, or the facial axis of a clinical crown ( It can be processed to identify landmarks such as Facial Axis of the Clinical Crown (FACC). These identifiers may be matched to corresponding identifiers of other digital representations to determine conversions that have occurred between different points in time. Alternatively or in combination, surface matching algorithms can be used to register digital data against each other. In some embodiments, the matching process locates the two teeth based on the approximate crown center and the tooth local coordinate system, respectively. Next, for each tooth, a matching operation is performed. The matching operation can be an iterative process that minimizes error values while attempting to find the proper tooth position. In some embodiments, the process finds points on the original crown and corresponding points on the current tooth, and calculates the distance between these points. The process then determines the transform that minimizes the sum of squares of these errors. The tooth is placed and the process is repeated. A new set of points is chosen, and the process finds the differences and determines the transformation that minimizes the error. The above steps may be repeated until the error is less than the termination criterion or the maximum number of iterations is reached.

Once the digital data are registered against each other, changes between different data sets can be determined. For example, initial and subsequent digital data can be superimposed into a common coordinate system to determine volume discrepancies between data and tooth changes that have occurred between data in three dimensions. By comparing the magnitude of the change with the time interval during which the change occurred, the rate of change can be determined. In some embodiments, the rate of change may include one or more of a tooth movement rate, a tooth shape change rate, a tooth size change rate, a gum shape change rate, and the like. For example, the rate of tooth shape change can be calculated where tooth wear is identified, and the rate of gum shape change can be calculated where gum recession or inflammation is identified. Change can be represented as one or more vectors representing the magnitude and/or direction of the change over time.

A predicted digital representation can be created at a later time by extrapolating the determined rate of change to a future point in time. Extrapolation can assume that objects in the oral cavity will continue to change at a rate consistent with the determined rate of change. For example, with respect to tooth movement, it may be assumed that the tooth continues to move along the speed and direction specified by the current tooth movement vector unless an obstacle occurs. Thus, extrapolation can be used to predict the tooth's trajectory and determine its future position. As discussed further herein, the extrapolation method used may be linear (eg, the rate of change is assumed to be constant) or non-linear (eg, the rate of change may vary over time). Linear extrapolation may be performed using data from at least two different time points, whereas non-linear extrapolation may be performed using data from at least three different time points. Non-linear extrapolation can be used, for example, to predict tooth movement along a curved path and/or acceleration or deceleration of tooth movement. In some embodiments, non-linear tooth movement may occur if there is collision of the tooth surfaces and/or sub-surfaces, and may occur due to changes in the patient's physiology, diet, age, and the like.

As an example, FIGS. 4A-4D show how the movement of a set of teeth 400 can be tracked and predicted. The set of teeth 400 may include a first tooth, a second tooth, and a third tooth. 4A shows a first tooth 401a, a second tooth 402a, and a third tooth 403a at an initial time point. Figure 4b shows the first tooth 401b, the second tooth 402b, and the third tooth 403b at a subsequent time point. As shown in FIG. 4B , the set of teeth 400 have moved from their position at an initial time point.

As shown in FIG. 4C , the positions of the tooth set 400 at initial and subsequent time points can be compared. For example, 3D models of the teeth 400 may be overlapped and compared with each other. The movement vectors of the teeth between the initial and subsequent time points can be determined. The movement may be a movement of the first tooth between an initial time point (tooth 401a) and a subsequent time point (tooth 401b), and a corresponding movement vector 411 may be determined. The movement may be the protrusion of the second tooth between an initial time point (tooth 402a) and a subsequent time point (tooth 402b), and a corresponding movement vector 412 may be determined. The movement may be the tipping of the third tooth between an initial time point (tooth 403a) and a subsequent time point (tooth 403b), and a corresponding movement vector 413 may be determined.

As shown in FIG. 4D , motion vectors 411 , 412 , and 413 can be used to determine the position of tooth 400 at a later point in time. In some embodiments, it can be assumed that tooth 400 will continue to move along the trajectory indicated by movement vectors 411 , 412 , and 413 . For example, the first tooth 401c can be moved further at the speed indicated by the first motion vector 411, and the second tooth 402c can protrude further at the speed indicated by the second motion vector 412. and the third tooth 403c may tip further at a speed indicated by the third motion vector 413. Specifically, the first tooth 401a, the first tooth 401b, and the first tooth 401c are the same first tooth of the set 400 but have different viewpoints, and the second tooth 402a and the second tooth 402a The tooth 402b and the second tooth 402c are the same second tooth of the set 400 but have different viewpoints, and the third tooth 403a, the third tooth 403b, and the third tooth 403c are It is the same third tooth of the set 400, but the viewpoint is different.

Although movement, protrusion, and tipping appear independently in FIGS. 4A-4D , the teeth may move in other ways, such as rotation or any combination. For example, a tooth may tip and move, a tooth may protrude and rotate, a tooth may tip, move, and may protrude. Movement of teeth that can be tracked includes one or more of tooth protrusion, intrusion, rotation, torque, tipping, or translation. Alternatively or in combination, other types of changes to teeth such as root resorption, enamel erosion, and/or caries formation may be tracked using the methods herein.

In some embodiments, movement of a set of teeth may be tracked and predicted using sub-surface data in addition to surface scan data, as discussed herein and above. 5A to 5D show how the movement of the set of teeth 500 can be tracked and predicted. The set of teeth 500 may include a first tooth and a second tooth. 5A shows a first tooth 501a and a second tooth 502a at an initial point in time. Each tooth includes a portion above the gum line 503 (crown 504a and crown 505a, respectively) and a portion below the gum line 503 (root 506a and root 507a, respectively). Figure 5b shows the first tooth 501b and the second tooth 502b at a subsequent time point. As shown in FIG. 5B , the set of teeth 500 move from their position at an initial time point, as do the crowns 504 b and 505 b and the roots 506 b and 507 b at subsequent time points different from the initial time point. In some embodiments, the position of the crown of tooth 500 at different viewpoints is determined using a three-dimensional scan, while the position of the root of tooth 500 at different viewpoints is determined using sub-surface data (e.g., X-ray, CBCT scan, CT scan, MRI, ultrasound, etc.) Thus, each tooth as a whole, including both visible and invisible parts, can be represented digitally, for example as a three-dimensional model.

As shown in FIG. 5C , the position of the tooth set 500 at an initial time point and at a subsequent time point can be compared. For example, a 3D model of a tooth 500 including both a crown and a tooth root may be overlapped and compared with each other. The comparison may include comparing the root position of tooth 500 at initial and subsequent time points, as well as the crown position of tooth 500 at initial and subsequent time points. Movement vectors 510 and 511 of the tooth between the initial and subsequent time points may be determined. Movement vectors 510 and 511 may be based on changes in the position of the crown and/or root of tooth 500 . As shown in FIG. 5C , tooth 500 moves with time at a trajectory and speed indicated by vectors 510 and 511 . The motion vectors 510 and 511 can be used to determine the position of the tooth 500 at a later point in time. For example, as shown in FIG. 5D, assuming that the teeth continue to move along the movement vectors 510 and 511, the first tooth 501c and the second tooth 502c start to decelerate and start decelerating each other at a subsequent time point. Predictions that they will collide can be made. Optionally, a vector representing a change to the shape of the root (eg shrinkage of the root due to resorption) can be determined and used to predict the future position of the tooth.

In some embodiments, future tooth positions are predicted by analyzing and extrapolating movement and/or shape changes of the roots. Since the position and structure of the root can have a significant impact on tooth movement, this approach is advantageous compared to methods that rely only on surface scan data and are thus limited to analysis of the crown. Thus, combining surface scan data and sub-surface data to determine global changes in tooth structure as described herein can improve the accuracy of extrapolating future tooth positions.

As discussed herein and above, shape and/or size changes to teeth may also be determined. Changes to the shape and/or size of teeth (eg, length, width, height, surface area, volume, etc.) can be associated with conditions such as bruxism, malocclusion, and improper bite alignment. 6A shows tooth 600a at an earlier time point, and FIG. 6B shows the same tooth 600b at a later time point. As discussed herein and above, teeth 600a and 600b may be scanned at different points in time to create a three-dimensional model of changes in shape and/or size over time. As shown in FIG. 6C (which shows models of teeth 600a, 600b overlapping each other to show shape and/or size change over time), models of teeth 600a, 600b can be registered with each other. there is. A vector 610 for the trajectory and magnitude of change can be determined by comparing the surfaces of teeth 600a and 600b. As shown in FIGS. 6B and 6C , tooth 600b wears over time at a trajectory and rate indicated by vector 610 . Based on vector 610, the shape and/or size of future tooth 600b, shown as tooth 600c in FIG. 6D, can be determined. For example, it may be assumed that teeth will continue to wear at a rate similar to that indicated by vector 610 . Alternatively or in combination, the volumes of the tooth models 600a and 600b may be compared to each other to determine a rate of change in volume of the tooth (eg, percent change relative to the initial volume). The size and/or shape of future teeth 600c may be determined by extrapolating volume changes to future time points.

Although vertical wear to the teeth is shown independently in FIGS. 6A-6D , other shape and/or size changes such as lateral wear of the teeth and combinations thereof may be tracked to determine future movement. Changes in shape and/or size may be tracked along with combinations of tooth movements to determine the shape and position of teeth at future time points. The predictive approach described herein can detect changes in tooth shape and/or size earlier and more accurately compared to methods that rely on visual inspection, thereby enabling rapid correction of conditions such as malocclusion, root resorption, enamel erosion, caries formation, etc. and diagnosis is possible.

As discussed herein and above, changes in the gums and other tissues in the vicinity of the teeth may also be determined. Changes to the location and/or shape of the gums can be associated with gum-related conditions such as gum recession or gingivitis. 7A shows the gum line 700a of tooth 702 at an initial time point, and FIG. 7B shows the same tooth 702 and gum line 700b at a later time point. As discussed herein and above, teeth 702 and gum lines 700a, 700b may be scanned at different points in time to create a three-dimensional model of position and/or shape change over time. As shown in FIG. 7C (representing a model of teeth 702 and gum lines 700a, 700b overlapping each other to show position and/or shape change over time), teeth 702 and gum lines 700a , 700b) can be registered with each other. A vector 710 for the magnitude and trajectory of the change to the location and/or shape of the gum line may be determined. As shown in FIGS. 7B and 7C , the gum line retracts over time at a trajectory and rate indicated by vector 710 . Based on vector 710, the future location and/or shape of gum line 700b, shown as gum line 700c in FIG. 7D, may be determined. For example, the position and/or shape of future gum line 700c may be calculated based on the assumption that the gum will continue to retract along vector 710 .

Alternatively or in combination, the locus and magnitude of the change relative to the gum line may be determined by tracking the size (eg, length, width, height, surface area, volume, etc.) of the corresponding tooth 702 . For example, an increase in the surface area and/or height of a tooth (eg, distance from crown to gum line) may indicate gum recession. As shown in FIGS. 7A and 7B , a tooth 702 has an initial height 704a at a first time point and an increased height 704b at a second time point. The difference between heights 704a and 704b can be used to calculate the rate of change in height for tooth 702, as shown in FIG. 7C. The height change rate can be used to predict the future height 704c of the tooth 702 at a future point in time, as shown in FIG. 7D. A tooth height that exceeds a critical value may indicate, for example, gum recession or gingivitis. The predictive approaches described herein allow for earlier and more accurate detection of changes in the position and/or shape of the gums compared to other methods (eg, visual inspection, measurement of gum spacing).

As discussed herein and above, changes to the shape and/or position of an object (eg, tooth) within the oral cavity may be tracked to determine a current trajectory for the object's shape change and/or movement. Some changes to the shape and/or position of an object in the oral cavity may be linear and may be tracked by comparing data obtained at at least two time points (eg, scan data, sub-scan data, etc.). Some changes to the shape and/or position of an object in the oral cavity may be non-linear (eg, exponential) and may be tracked by comparing data obtained at at least three time points. Linear or non-linear extrapolation techniques can then be used to determine an expected trajectory for the object in order to predict its shape and/or location at future time points.

8A shows a tooth at a first, initial time point 800 (time t=0) and the same tooth at a second, subsequent time point 801 (time t=t ₁ ). As discussed herein and above, scan data (eg, three-dimensional scans) may be made of teeth and compared to determine how the teeth may change at a future point in time. Scan data may be combined with additional data from one or more time points, such as root sub-surface data. The difference between the teeth at the first time point 800 and the second time point 801 can be defined as a linear tooth movement and/or shape change, and the linear speed of the movement and/or shape change is extrapolated at a future time point. The position and/or shape of the teeth can be determined. 8A further shows the tooth at a future, subsequent time point 899 (time t=t _n ).

8B shows a tooth at a first, initial time point 800′ (time t=0), the same tooth at a second, subsequent time point 801′ (time t=t ₁ ), and a third, subsequent time point ( 802′) represents the same tooth at (time t=t ₂ ). As discussed herein and above, scan data may consist of teeth and may be compared to determine how the teeth may change at future points in time. Scan data may be combined with additional data from one or more time points, such as root sub-surface data. The difference between the teeth at the first time point 800', the second time point 801', and the third time point 802' may be non-linearly defined in tooth movement and/or shape change, and may also include movement and/or shape change. Alternatively, the non-linear rate of shape change can be extrapolated to determine the position and/or shape of the tooth at a future time point. 8B additionally shows the tooth at a future, subsequent time point 899' (time t=t _n ).

Optionally, the predicted digital representation is able to respond to obstacles the structure may encounter (eg, other teeth, colliding roots, occlusion, root resorption or erosion, gum recession), such as through use of the collision avoidance algorithms described herein. It can be generated using an extrapolation procedure that adjusts the expected trajectory of an object in the oral cavity based on the For example, in some embodiments, digital models of different arrangements of the patient's teeth obtained at different time points are used to create predicted digital representations of the arrangement of the patient's teeth at future time points. The rate of tooth movement for one or more teeth between different configurations may be calculated as described herein. Next, the teeth of the digital model can be relocated according to the calculated speed to create future tooth arrangements. During repositioning, the collision detection process can be used to detect whether the expected movement will cause collision with adjacent teeth. For example, the collision detection process may determine, at each time step, which of the geometric shapes describing the surfaces of the teeth intersect. If no collision occurs, it can be assumed that it will continue to move according to the calculated movement speed.

Various techniques can be implemented to detect expected collisions between teeth. For example, in some embodiments, collision detection algorithms are constructed in a binary tree-like manner, centered around recursive subdivision of the space occupied by objects. A triangle is used to represent the teeth in the digital data set. Each node in the tree is called an oriented bounding box (OBB) and contains a subset of the triangles that appear in the parent of the node. Children of a parent node contain all triangle data stored in the parent node between them.

A node's bounding box is oriented to fit tightly around all triangles of that node. A leaf node in the tree ideally contains a single triangle, but may contain more than one triangle. Detecting a collision between two entities involves determining if the OBB trees of the entities intersect. If the OBB of the root node of the tree overlaps, the root's children are checked for overlap. The algorithm proceeds in an iterative manner until a leaf node is reached. At this point, a robust triangle intersection routine is used to determine which leaf's triangle is involved in the collision.

In some embodiments, the OBB tree may be built in a slow fashion to save memory and time. This approach is due to the observation that parts of the model will never participate in collisions, and consequently OBB trees for these parts of the model do not need to be computed. The OBB tree is extended by splitting internal nodes of the tree as needed during an iterative collision determination algorithm. In addition, triangles in the model that are not required for collision data may be specifically excluded from consideration when constructing the OBB tree. For example, motion can be viewed at two levels. Entities can be conceptualized as "moving things" in a generic sense, or as "moving things" in relation to other objects. Since the additional information does not change the collision state between these entities, it improves the time taken for collision detection by avoiding recalculation of collision information between entities that exist relative to each other.

In an alternative embodiment, the collision detection algorithm calculates a “collision buffer” oriented along the z-axis along which the two teeth lie. A collision buffer is calculated for each position or step along the trajectory for which collision detection is desired. To create a buffer, x, y planes are defined between the teeth. The plane may be “neutral” to the two teeth. Ideally, the neutral plane is positioned so that it does not intersect any teeth. If intersection with one or all teeth cannot be avoided, the neutral plane is oriented so that the teeth lie on opposite sides of the plane, if possible. In other words, the neutral plane minimizes the amount of surface area of each tooth that is on the same side of the plane as the other teeth. The plane has a grid of discrete points, the resolution of which depends on the resolution required for the collision detection routine. A typical high-resolution collision buffer contains a 400x400 grid, and a typical low-resolution buffer contains a 20x20 grid. The z-axis is defined by a line perpendicular to the plane.

The relative positions of the teeth are determined by calculating, for each point on the grid, the linear distance parallel to the z-axis between the plane and the nearest face of each tooth. For example, at any given lattice point (M, N), the closest face and plane of the back surface of the tooth are separated by a distance indicated by the value Z ₁ (M, N), while the closest face and plane of the front face of the tooth are separated. are separated by a distance indicated by the Z ₂ (M, N) value. If the plane is at z = 0 and the collision buffer is defined so that positive values of z are toward the back of the tooth, Z ₁ (M, N) ≤ Z ₂ (M, N) at any lattice point (M, N) on the plane ), the teeth collide.

If a collision between teeth is detected, the movement speed and/or trajectory of one or all teeth may be modified. For example, in some embodiments, when a collision occurs, a “push” vector is created to change the expected travel path away from the collision. Based on the push vector, a "bounce" of the current tooth from collision and a new tooth movement is created. Alternatively, it can be assumed that the collision stops some or all further movement of the colliding teeth, eg reduces the movement speed or is set to zero. Optionally, in some embodiments, if a collision is detected, the extrapolation process is aborted, for example to alert the user.

Extrapolation of surface scan data and/or other types of digital data obtained at multiple different points in time can provide faster and more reliable predictions not possible by visual inspection or review of static images or models. One aspect of malocclusion changes can be a cascade of tooth movements that occurs little by little. A single tooth can move only barely detectable due to the heavy occlusion between the teeth. A heavy bite can cause these teeth to move first, which can lead to a heavy bite at a different location causing another movement, followed by a heavy bite at another location. Predicting such a "domino" effect can be very difficult because the effects of jaw movement and malocclusion are not taken into account by the medical professional's visual examination. By obtaining digital data, such as a three-dimensional scan at an initial time point, and allowing sufficient time (e.g., after 6 months or 1 year) to pass before obtaining second and/or subsequent digital data, the occlusion, jaw, and soft tissue The real consequences of this may be evident in the alignment of the teeth themselves. The role of any dental restoration operation can also be determined.

9 shows a method 900 of generating a predicted digital representation of a patient's oral cavity to determine the patient's future state. Method 900, like other methods provided herein, may be used in combination with any embodiment of the system and apparatus of the present disclosure. For example, some or all of the steps of method 400 may be performed by a processor of one or more computer systems, as discussed further herein.

In step 905, first oral digital data is received. The first digital data may represent a real state of the oral cavity or one or more entities thereof (eg, teeth, gums, jaw, palate, tongue, airway, TMJ, etc.) at the first point in time. In some embodiments, the first time point is an initial time point at which dental and orthodontic monitoring begins for the patient. In some embodiments, the initial time point occurs after a primary tooth is lost, but before a permanent tooth emerges, for example between 4 and 6 years of age. Obtaining scan data of teeth at a relatively early age can facilitate detection of dental or orthodontic conditions before such conditions can be detected using visual inspection.

In step 910, second digital data of the oral cavity is received. The second digital data may represent the actual state of the oral cavity or one or more structures thereof at a second time point different from the first time point. The first and second time points are, for example, at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, at least 7 months, at least 8 months, at least 9 months, at least 10 months. , at least 11 months, at least 1 year, at least 2 years, at least 5 years, or any other time interval suitable for generating accurate measurements and/or predictions as described herein. The second point in time may follow the first point in time. The second time point may occur after the permanent teeth begin to erupt, but before all primary teeth have fallen out, for example between the ages of 6 and 12 years.

In some embodiments, for example, the first and second digital data are three-dimensional scans of the oral cavity, and/or an image of the patient's dentition and/or surrounding tissue (eg, gums, palate, tongue, airway). Contains data representing three-dimensional surface topography. Each scan may include an upper and/or lower arch of a tooth. A three-dimensional model of the tooth may be created based on the scan as described herein and above. Scan data obtained at two or more different time points may be used to estimate the future position and/or shape of objects in the oral cavity, such as teeth, gums, occlusion, and the like, as further described herein. Alternatively or in combination, other types of digital data other than scan data, such as root sub-surface data, may be used to determine the location and/or shape of non-visible intraoral objects (eg, root, jaw, airway). It can also be used to determine

Optionally, at an additional time point subsequent to the second time point, digital data representing the oral cavity may be received, for example, third digital data obtained at a third time point, fourth digital data obtained at a fourth time point, and the like. Any number of digital data sets at any number of points in time can be received and later analyzed, as described herein and below. The digital data herein may be obtained at any combination of time points during the development of the patient's teeth, such as before, during, and/or after the development of the permanent dentition. For example, subsequent digital data typically occurs after all primary teeth have been lost (eg, after 12 years of age) and/or once all permanent dentition has been completed (eg, between 17 and 25 years of age). However, it can be obtained after the third molars emerge).

At step 915, additional data is received. In some embodiments, the additional data provides other information useful for identifying a patient's current condition and/or predicting a future condition. For example, additional data may include demographic information, lifestyle information, medical information, medical history, family history, and/or genetic factors. The additional data may be received at one or more different points in time, which may or may not be the same point in time for the collection of the first and second digital data. For example, the additional data may be received at multiple time points during tooth development and/or concurrently with the scan data. Alternatively, additional data may be received at a single point in time.

At step 920, a predicted digital representation of the oral cavity is created. The predicted digital representation may be a two-dimensional or three-dimensional model of the predicted future state of the oral cavity or one or more structures thereof at a future time point subsequent to the first and second time points. At least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, at least 6 months, at least 7 months, at least 8 months, at least 9 months, at least 10 months, at least 11 months, at least 1 year , at least 2 years, at least 5 years, at least 10 years, at least 20 years, or any desired length of time following the first and/or second time points. Future points in time may correspond to life events such as the patient turning 18, getting married, moving, retiring, traveling, competing, and the like.

As discussed herein and above, a predicted digital representation may be generated based on the first and second digital data using a variety of methods. For example, in some embodiments, digital data from two or more points in time may be compared to create their comparison. Any number of digital data sets from any number of points in time can be compared. In some embodiments, generating the comparison includes registering the first and second digital data with respect to each other in a common coordinate system. Generating the comparison may include measuring a property of an intraoral subject at a first time point (eg, using first digital data), or oral cavity at a second time point (eg, using second digital data). measuring a property of my subject, and determining a change in the property between a first and a second time point. Optionally, the characteristic can be measured at subsequent time points using the corresponding digital data to determine changes in the characteristic with subsequent time points.

Future changes to the characteristics of the intraoral subject may be predicted, in response to the determined changes and/or selected time intervals, and used to generate predicted digital data. For example, as discussed herein and above, a rate of change for a characteristic can be determined, and the rate of change can be extrapolated to a selected future time point to predict a characteristic of an intraoral subject at a future time point. By repeating this process for multiple intraoral objects (eg, each tooth in the patient's mouth), a digital representation of the oral cavity at a future point in time can be predicted and created.

In an exemplary embodiment, the methods herein may be applied to generate predictions for a patient's teeth at a future point in time. In some embodiments, the position and/or shape of a tooth at a first time point may be measured, for example, from scan data at a first time point, and the position and/or shape of a tooth at a second time point may be, for example, For example, it may be measured from scan data at the second time point. Optionally, the position and/or shape of the tooth at an additional time point (eg, a third time point) may be determined from scan data obtained, for example, at an additional time point. In some embodiments, the location and/or shape of tissue around a tooth may be measured by the location and/or shape of, for example, the gums, tongue, palate, airways, and the like.

Many changes to teeth between two or more time points can occur. For example, one or more teeth may have been moved, one or more teeth may have been worn or lost, one or more teeth may have been restored or replaced, and some or all of the gums around the teeth may have receded or swollen. Thus, the rate of movement and/or shape change relative to one or more teeth and/or surrounding tissue may be determined. Comparison between teeth and/or surrounding tissue at different time points determines movement and/or change in shape of one or more teeth and/or surrounding tissue over the time interval between the first and second time points, and the magnitude of this movement or change This can be done by determining the speed. For example, the upper dental arch at a first time point may be registered and compared with the upper dental arch at a second time point, and the lower dental arch at a first time point registered and compared with the lower dental arch at a second time point. It can be.

Each person's mouth is unique because of the person's size, shape, and occlusion. Any natural deterioration of the position of the teeth varies with different rates of tooth movement and different areas to which the teeth are worn. For example, some patients' occlusion may be exacerbated by wear of the occlusal surfaces of the posterior teeth, resulting in stronger contact and greater crowding of the incisors as a result of internal pressure placed on the incisors as they impinge toward the tongue. In another example, tongue pressure can cause a larger gap and anterior bite opening relative to the other, initially at barely perceptible proportions, but can escalate to significant changes over time. The rate of this change for each tooth can be determined from a comparison of the three-dimensional scans. Individual teeth can be identified and labeled. A difference in the X, Y, and/or Z position of an individual tooth and/or one or more identified landmarks of a tooth between the two scans may be determined. The individual trajectories of teeth can be calculated. The speed, magnitude, and direction of tooth movement can be calculated as three-dimensional vectors. The rate and location of tooth shape change can also be determined. For example, worn areas are likely to continue to wear out. In some embodiments, the determined rate, magnitude, direction, and/or location of the change of one or more teeth may be adjusted based on additional information, such as from a comparison of root positions or gum lines. For example, a comparison of root positions at two separate time points can be extrapolated to determine the location of a root at any future time point, and this determined location of the root is at least in part relative to the tooth at a future time point. It can be used to determine changes and conflicts. In another example, a comparison of gum lines at two separate time points can be extrapolated to determine a gum line at any future time point, which determined gum line at least in part accounts for a change to a tooth at a future time point. can be used to determine The determination and use of the velocity and trajectory for tooth change based on the scan is further described herein and above.

Optionally, changes to the teeth may be compared to data relating to similar changes in similar patients available from the patient's database. For example, the position and/or shape of a tooth at a future time point may be determined by a determined rate of movement or shape change for the tooth between the first and second time points and/or patient data from a patient database (e.g., similar patient A similar movement or change for ) can be predicted based on. Prediction can be performed, for example, by linearly extrapolating position and/or shape data from first and second instants to determine the velocity and/or vector of movement relative to the position and/or shape at future instants. . A third or additional three-dimensional scan at a third time point can be used to detect any non-linear changes and perform non-linear extrapolation, as described further herein. For example, tooth movement along a curved path and/or acceleration or deceleration of tooth movement may be predicted.

In some embodiments, the expected trajectory of each tooth may be determined, but obstructions such as adjacent teeth may prevent the expected tooth movement from fully occurring. The teeth may be superimposed in a three-dimensional geometry, for example when the changes are rendered by a computer system. Embodiments of the present disclosure may provide tooth collision avoidance algorithms when predicting future positions and/or shapes of teeth, as discussed herein and above.

In some embodiments, the predicted digital representation is presented to the user as, for example, a three-dimensional model of the oral cavity at a future point in time. The 3D model can be used to name a few examples: expected tooth movement, expected tooth direction change, expected tooth shape change, expected gum line change, expected root position change, expected occlusal position change, expected airway change, or one or more of the expected tongue position changes. The three-dimensional model data can be displayed so that a medical professional can visualize and verify the predicted future position, size, shape, etc. of teeth and/or other objects in the oral cavity. These models may be stored in a patient database for later use.

At step 925, the future state of the oral cavity may be determined. As discussed herein and above, the future condition may be an undesirable dental or orthodontic condition predicted to occur at a future point in time if the oral cavity is left untreated. In some embodiments, the future state is determined prior to the occurrence of the future state. Alternatively, the future state may be determined after the future state has occurred, but before it is visually detectable. In some embodiments, a state may be predicted based on the predicted digital representation, for example by measuring one or more parameters of the representation to determine if an anomaly exists. Additional information such as the patient or patient's age, age of tooth development, molar relationship, incisor crowding and/or spacing, arch shape, facial type, airway, and horizontal/vertical overbite may be provided, to name a few. It can be taken into account in predicting the calibration status.

The computer system determines, for example, if the current patient or patient data falls within the appropriate range for a tooth or orthodontic treatment; if the current patient or patient data closely matches that of a previously identified tooth or orthodontic condition in the database; Automatically predicts a tooth or orthodontic condition if the current patient or patient data closely matches that of the current patient or patient data in the case of a relapsing condition, or automatically creates a list of applicable teeth or orthodontic conditions can provide A medical professional using a computer system may select one or more conditions from the list. Alternatively or in combination, the computer system may provide and display key parameters (eg, measurements) that a medical professional may evaluate to diagnose a dental or orthodontic condition. Optionally, the medical professional can select certain critical parameters for monitoring, and the computer system can generate an alert if a problem or anomaly with that parameter is detected during the prediction process. In addition to treatment for a condition, a predicted dental or orthodontic condition may include any condition and corresponding treatment described herein and above. In some embodiments, the methods described herein may also be applied to identify pre-existing teeth or orthodontic conditions. Additional methods for identifying or predicting dental or orthodontic conditions and treating conditions are described in US provisional application Ser. No. 62/079,451, incorporated herein by reference.

In some embodiments, step 925 includes automatic setting and/or detection of features and/or reference data on the predicted digital representation. These reference data and/or characteristics can be used to obtain automatically calculated various dental measurements to facilitate diagnosis and treatment. Such selected reference data and features may be properly recognized through databases, libraries, and other memory applications containing dental features and reference data that may enable automatic recognition of such reference data and dental features via a computer-implemented method. can For example, in some embodiments, automatic establishment and/or detection of reference data and/or features may include automatic establishment of reference objects, automatic establishment of reference frames, automatic detection of anatomical dental features, and/or automatic establishment of orthodontic criteria. may include construction. Any one or more of these features, frames, and criteria can be used to automatically calculate appropriate measures for predicting future conditions.

In some embodiments, the automatic calculation of dental measurements includes calculation of arch dimensions and the like, e.g., the relative position of teeth in the arch, as well as calculation of a number of tooth dimensions, e.g., size, shape, and other dental characteristics. can do. For example, automatic calculation of tooth measurements may include calculation of angular relative positions, such as crown angle (tip), crown tilt (torque), and/or crown rotation (about a tooth axis). Additionally, the automatic calculation of dental measurements may include calculation of the translational relative position of each tooth relative to other teeth, such as crown level and/or crown projection. Automatic calculation of tooth measurements may also include calculation of relative overlap, for example local overcrowding or how teeth interfere with each other. Other computational tooth measurements may include relative coherence, which is a derivative of angular relative position and translational relative position measurements. For example, the relative coherence of two adjacent teeth is the difference in relative position for angular and translational components.

In some embodiments, computer-assisted dental measurements include crown shape, mesial distal width, bucca lingual width, crown height (crown length in a direction perpendicular to the occlusal plane), and other tooth characteristics such as a spline curve around the crown base (a spline curve on the boundary of the crown and gum surface). Automatic calculation of tooth measurements may also include point features (described by a center point and the area around the center point, eg cusps) and/or (center curve and area around the center curve, eg groove). and calculation of various other dental features, such as incisor, canine, pre-molar, and molar features including elongated features (described by ridges and ridges). Automatic calculation of dental measurements may include calculation of tooth dimensions and/or arch dimensions, etc., based on one or more of a set, detected, and/or constructed frame, static or dynamic dental features, and/or reference objects.

In some embodiments, automatic calculation of dental measurements may include an incisor ridge alignment angle, a mandibular posterior alignment characteristic, a maxillary posterior alignment characteristic, a posterior border ridge relative height, a buccolingual inclination distance of the posterior teeth, and/or It may further include determining dental alignment properties, such as contact between adjacent teeth. Automatic calculation of dental measurements may also include automatic calculation of occlusal characteristics, including, for example, occlusal contacts and occlusal relationships along an arch.

The dental measurements described herein may be used to detect the presence of unwanted teeth or orthodontic conditions. In some embodiments, the extent and amount of malocclusion, such as crowding, spacing, overjet, open bite, cross bite, angular class, occlusal contact, and/or others, as discussed herein and above, facilitates dental treatment and planning. It can be automatically determined from a variety of dental measurements that are calculated and displayed appropriately to the user. For example, the overjet may be determined by the distance from the intersection of the curve through the midpoint of the lower incisor ridge with the midpoint on the lower arch to the buccal surface of the upper incisors (in the closed position). In another example, overbite may be defined as the percentage of the buccal surface of the maxillary incisors that lies on a curve through the midpoint of the ridges of the mandibular incisors. In this way, automatic determination of malocclusion can be achieved accurately, reliably, and/or efficiently.

In some embodiments, measurements may be used to automatically calculate orthodontic or dental indices, such as assessment and assessment of predicted future conditions. For example, automatic calculation of orthodontic or dental indices such as Peer Assessment Ratings (PAR) indices, ABO discrepancy indices, ABO objective evaluation systems, etc. can be realized based on automatically calculated measurement results. The automated methods provided herein can reduce or eliminate errors, discrepancies, and time delays associated with visual assessment or manual calculation of these indices. In some embodiments, a PAR index finds contact points of anterior teeth, determines a posterior occlusion class, detects or measures a posterior open bite or cross bite, calculates anterior overjet, and/or midline discrepancy. As with measuring , it is automatically calculated by determining and assessing the measured value. These indices can be calculated to allow a user to efficiently assess the complexity of a case and/or measure the quality of a treatment outcome during any stage of the treatment process. In addition, these metrics can be used to score or evaluate treatment cases before, during, and/or after treatment.

At step 930, treatment options for future conditions are created and/or displayed. For example, a list of recommended treatment products and/or procedures may be created and displayed, for example to a healthcare professional. The resulting therapeutic product and/or procedure may be based on desirable parameters, for example for severity of condition, patient-specific characteristics (e.g., demographic information, lifestyle information, etc.), cost and duration of treatment, etc. It can be tailored to a specific patient. A medical professional may select one or more of the indicated treatment products and/or procedures to be used to treat the predicted dental or orthodontic condition.

Recommended treatment products and/or procedures may be generated based on the severity of the condition or predicted condition. In some embodiments, the severity of the condition or predicted condition is assessed to determine whether remediation is required. For example, correction may be recommended if severity exceeds a threshold (eg, amount of distance, rotation, arch, overbite, underbite, crowding, space, gum gap, etc.). The threshold may vary according to the preference and judgment of the healthcare professional. Optionally, the severity of the patient's condition or predicted condition may be indicated to the healthcare professional with or without a corresponding threshold to allow the healthcare professional to determine whether or not correction is required. If the medical professional determines that correction is necessary, a list of recommended products and/or procedures may be generated and displayed as described herein. Steps 935 and 945 may be omitted if the medical professional determines that no correction is necessary. In other embodiments, recommended treatment products and/or procedures may be generated regardless of severity once a condition or predicted condition is identified. For example, prescribing treatment when the condition is first detected; modifying oral hygiene habits (e.g., brushing habits, type of toothbrush, mouthwash use) if gingivitis is suspected; treating TMJ problems It may be advantageous to provide an occlusal plate at the first sign of tooth wear due to grinding to limit teeth grinding.

At step 935, prediction results for treatment options are generated and/or displayed. A variety of methods can be used to predict the outcome of implementing treatment options for a patient. In some embodiments, the extrapolation techniques presented herein may be used to predict treatment outcome. For example, tooth position changes produced by a tooth repositioning device may be extrapolated to future time points to predict the future arrangement of a patient's teeth after wearing the device. Alternatively or in combination, data mining of past patient information can be used to predict treatment outcome. For example, data mining can be used to compare treatments from treatment databases (e.g., compare for type of condition, severity of condition, demographic information, lifestyle information, medical information, medical history, family history, genetic factors, etc.) , as well as comparable patient's historical data from the patient database. Past patient and/or treatment data can be used as a basis for predicting how a patient will respond to a proposed course of treatment, as well as the effectiveness, cost, and duration of such treatment.

Predictive outcomes for different treatment products and/or procedures (e.g., with respect to treatment effectiveness, duration, cost, patient preference, aesthetics, etc.) can be compared with each other to facilitate selection of the optimal course of treatment. can be compared For example, a two-dimensional or three-dimensional model of the oral cavity may be created by application of the considered therapeutic products and/or procedures. In some embodiments, a plurality of different models representing the outcomes of different treatment options may be displayed and compared. Optionally, the healthcare professional and/or patient can show a model of the oral cavity at a future point in time that was left untreated, as well as a similar model for treatment for comparison.

At step 940, treatment options for the future condition are selected and ordered. Treatment options may be ordered from a treatment provider or product manufacturer. In embodiments where the selected option is a therapeutic product (eg, appliance, prosthetic, etc.), the ordering process may include using patient-specific data to design and/or create the therapeutic product. In some embodiments, patient-specific data includes digital data and/or additional data obtained in steps 905-915. For example, three-dimensional digital data representing a patient's current tooth arrangement can be used to design a shell aligner that repositions the patient's teeth. In another example, a digital representation of a patient's teeth and jaw can be used as a basis for manufacturing a mandibular forward movement device that treats sleep apnea in a patient. Patient-specific data can be transmitted, for example, to a manufacturing machine and/or manufacturing facility to create a product.

It will be appreciated that method 900 has been described above as an example. One or more steps of method 900 may include one or more sub-steps. Method 900 can include additional steps or sub-steps, one or more steps or sub-steps can be omitted, and/or one or more steps or sub-steps can be repeated. For example, three or more sets of digital data may be obtained and analyzed instead of two, and non-linear trajectories and magnitudes of tooth and/or gum movement or other changes may be determined and analyzed. In some embodiments, one or more of steps 915, 930, 935, or 940 are optional. The steps of method 900 can be performed in any order, for example the order of steps 920-905 can be varied as desired. One or more steps of method 900 may be performed with a computer system as described herein and below.

10A and 10B show an algorithm 1000 for predicting and treating a patient's future condition. Algorithm 1000 may be used in combination with any of the embodiments of the systems, methods, and apparatus described herein. Further, although algorithm 1000 is discussed herein in the context of three-dimensional scanning, it should be understood that algorithm 1000 may also be applied to other types of digital data of the patient's oral cavity, such as two-dimensional image data.

The algorithm starts at step 1002. In step 1004, a first 3D scan is received at a first point in time t0 and inputted into a coordinate system. The three-dimensional scan can be segmented to isolate intraoral objects of interest (eg, individual teeth) to facilitate measurement and analysis of the objects. The scan can be used as a basis for comparison with other scans, as discussed herein and above, intraoral objects and/or landmarks in the mouth (e.g., gum line, jaw width, occlusal surfaces of teeth, opposite jaw, etc.)

In some embodiments, the three-dimensional scan provides surface data of the oral cavity, such as data of crowns and gums. Optionally, in step 1006, the algorithm checks if any sub-surface data has been received, such as root data. If sub-surface data has been received, the algorithm determines at step 1008 whether the data is 2-dimensional or 3-dimensional. The 3D sub-surface data can be stitched directly into the 3D scan, as shown in step 1010. The 2D sub-surface data may undergo additional processing before being combined with the 3D scan data, as shown in step 1012. For example, algorithms such as surface matching algorithms can be used to identify landmarks in two-dimensional data that can be used to create three-dimensional images. Alternatively, the 2D data may be applied in the same coordinate system as the first 3D scan. Next, the sub-surface data can be combined with the 3D scan data in step 1010 .

In step 1014, a second 3D scan at a second time point t ₁ is received. A second scan may be placed in the same coordinate system as the first scan, segmented, and processed to identify characteristics and/or landmarks, similar to step 1004 described above. Optionally, sub-surface data may be obtained and stitched to second three-dimensional scan data, similar to the process described herein for steps 1006-1010.

In step 1016, the algorithm detects whether there are any changes to the intraoral objects depicted in the scan. The change can be detected by comparing the first 3D scan and the second 3D scan with each other. Detection of changes between scan data can be performed according to various methods described herein. For example, changes between landmarks and/or corresponding properties of one or more intraoral objects between two scans may be calculated. As indicated in step 1018, if a change is detected, the algorithm calculates and displays the change (eg, on a graphical user interface).

As indicated in step 1020, if no change is detected, the algorithm creates a prediction of the future state of the oral cavity. As discussed herein and above, step 1020 may include generating a predicted digital representation of the oral cavity at a future time point following time t ₀ , t ₁ . A prediction may be generated based on changes to one or more intraoral objects determined in step 1016, for example using linear and/or non-linear extrapolation techniques.

In step 1022, the algorithm evaluates whether any tooth or orthodontic condition has been detected in the predicted future condition of the subject in the oral cavity. If no condition is detected, the algorithm proceeds to step 1024 where the scan data at the next time point t _x is received, placed in a coordinate system, segmented and/or processed to determine properties and/or landmarks. Optionally, the surface data may be obtained or stitched with additional three-dimensional scan data, similar to the process described herein for steps 1006-1010. The algorithm may proceed to perform steps 1016 to 1022 to determine if there are any changes between the new scan and the previous scan, predict future oral conditions, and detect what conditions exist in the future conditions. Steps 1016-1024 can be repeated as desired to update the predictions as new scans are obtained. For example, three or more scans can be compared to perform a non-linear extrapolation of the detected change, as discussed herein and above.

As shown in step 1026, if a predicted tooth or orthodontic condition is detected in step 1022, the predicted condition is displayed (eg as a three-dimensional model and/or list of conditions in a user interface). At step 1028, potential treatment options for the predicted condition may be created and displayed. At step 1030, the algorithm detects whether user input selecting one or more displayed treatment options has been received. If no selection is made, or if the user declines to select an option, the algorithm proceeds to step 1032, where the user is prompted to schedule the next scan of the oral cavity. Additional scans may be received and processed in step 1024 described above. Alternatively, if no additional scans are scheduled, the algorithm ends at step 1034.

If a treatment option is selected, the user is given the option to view information about the selected treatment option in step 1036 . In step 1038, information is displayed, for example on a graphical user interface. The user interface may provide, for example, a link to the treatment provider's website along with information.

At step 1040, an option is given to view the predicted outcome of treatment options. The predicted result may include predicted treatment cost, duration, result, etc., and may be displayed in step 1042, for example on a graphical user interface. In some embodiments, the predicted outcome may be displayed as a two-dimensional or three-dimensional model representing the predicted future state of the oral cavity once treatment options are applied. Optionally, the user may be given the option of comparing the predicted results of the different treatment options to each other and/or to the untreated condition of the oral cavity.

At step 1044, the user is given the option to order treatment options. If the user does not order the option, the algorithm ends at step 1046. Alternatively, the algorithm may proceed back to step 1024 to receive additional scan data and continue the prediction process. If the user decides to order a treatment option, the option is ordered in step 1048. Optionally, digital data related to a treatment option (eg, a scan of the patient's oral cavity) may be uploaded and/or transmitted to a treatment provider to facilitate creation of a treatment product. The algorithm then ends at step 1050. In an alternative embodiment, after administering treatment to the patient, additional scans may be received at step 1024 to continue monitoring the patient's oral cavity.

13 illustrates a method 1300 of calculating changes in a patient's oral cavity to an intraoral object to determine a future state of the intraoral object, in accordance with various embodiments. Method 1300 can be used in combination with any of the embodiments of the systems, methods, and apparatus described herein. In some embodiments, method 1300 is a computer-implemented method, and some or all steps of method 1300 are performed with the aid of a computer system or device, eg, one or more processors. For example, method 1300 can be performed by a computer system that includes one or more processors and a memory having instructions executable by the processor to cause the system to perform the steps described herein.

In step 1310, first digital data representing the oral cavity at a first point in time is received. In step 1320, second digital data representing the oral cavity at a second time point is received. Steps 1310 and 1320 may be performed analogously to steps 905 and 910 of method 900 previously described herein. Likewise, digital data from additional points in time may be received and used in subsequent steps of method 1300 . The received digital data may include any data of the oral cavity, such as surface data and/or sub-surface data. The received digital data may represent the actual state of the oral cavity of one or more intraoral subjects at a specific point in time, such as the actual position, orientation, size, shape, color, etc., thereby providing an expected, desired, or ideal state of the oral cavity. It can be distinguished from the data it represents.

At step 1330, the data is processed to determine changes to intraoral objects in the oral cavity over the first and second time points. The processed data may include, for example, the first and second digital data obtained in steps 1310 and 1320, as well as data at other points in time and/or any other additional data that may be related to orthodontic health. An intraoral object may be any entity described herein, such as a crown, root, gum, airway, palate, bone, or jaw. A change to an object within the oral cavity may be a change to any characteristic of the object, such as position, orientation, shape, size, and/or color. For example, step 1330 may include determining a change in position of the object within the oral cavity between the first and second time points.

Alternatively or in combination, the data may be processed to determine a rate of change of the subject in the oral cavity over the first and second time points. As discussed herein and above, the rate of change may be for one or more of the position, orientation, shape, size and/or color of an object within the oral cavity. For example, step 1330 may include determining the speed (eg, speed of movement) of the object within the oral cavity. As another example, step 1330 may include determining the rate of tooth shape change of teeth and/or the rate of gum shape change of gums. In some embodiments, determining the rate of change is a vector representing the trajectory and magnitude of the change across time points, similar to that shown in FIGS. includes deciding

In step 1340, it is evaluated whether the change determined in step 1330 exceeds a predetermined threshold. For example, step 1330 may include determining a change in position of the object in the oral cavity between first and second time points based on the first and second digital data, and step 1340 may include determining that the change in position is a predetermined threshold value. It may include evaluating whether it exceeds A predetermined threshold may be indicative of an undesirable tooth or orthodontic condition, and if the change exceeds the threshold, it may be determined that the patient has the condition or is at risk of developing the condition at a future point in time. The value of the predetermined threshold may be determined in a variety of ways, for example based on user preferences, patient characteristics, and/or values from dental or orthodontic literature. In some embodiments, the predetermined threshold is entered by a user such as a physician or therapist.

In some embodiments, if the determined change exceeds a predetermined threshold, an alert is output to the user, for example via a user interface presented on a display. A warning indicates to the user that the patient has developed, or is at risk of developing, an unwanted tooth or orthodontic condition. Optionally, in response to an assessment that the change exceeds a predetermined threshold, a plurality of options for generating the desired tooth or orthodontic result may be generated and presented to the user on a user interface presented on the display. The plurality of options may be a plurality of treatment options to treat an unwanted dental or orthodontic condition that exists or is predicted to occur. Displayed treatment options may include associated price information, treatment time information, complication treatment information, and/or insurance reimbursement information, as described herein and above.

In alternative embodiments, other criteria may be used to evaluate the determined change, including but not limited to: whether the determined change is less than a predetermined threshold, whether the determined change is approximately equal to the predetermined threshold, Whether the determined change is within a predetermined range, whether the determined change is outside a predetermined range, or a combination thereof.

At step 1350, the future state of the object in the oral cavity is determined based on the predetermined change. The future state may be a future position, direction, shape, size, color, and the like of the object in the oral cavity. For example, in some embodiments, step 1350 includes determining a future position of the object within the oral cavity, and the future position is determined by determining a movement trajectory based on the movement speed calculated in step 1340 . The movement trajectory may be linear (eg, see FIG. 8A) or non-linear (eg, see FIG. 8B). Thus, the future position of an object within the oral cavity can be determined by extrapolating the velocity of the object within the oral cavity to a future time point, using linear or non-linear extrapolation, where appropriate. In some embodiments, the non-linear movement trajectory is digital data from two or more time points, such as first, second, and third digital data representing the actual state of the oral cavity at each of the first, second, and third time points. is determined based on For example, the nonlinear movement trajectory may include a change in a moving direction and/or a change in the moving speed of the object. These non-linear changes can be determined and extrapolated using data from two or more time points. Data from two or more time points may also be used to determine other parameters, such as the force vector associated with the intraoral object across the first, second, and third time points.

Optionally, a graphical representation of the future state of the intraoral object may be displayed to the user to facilitate visualization and diagnosis of current or future dental or orthodontic conditions. For example, if a future position is predicted, the graphical representation may show an object in the oral cavity at a future position in the oral cavity. The graphical representation may be provided as part of an interactive graphical user interface presented on a display operably coupled to a computer system, as discussed further herein.

The future condition of a subject in the oral cavity can be used to determine future undesirable dental or orthodontic conditions that are predicted to occur at a future time point if the oral cavity is left untreated. As discussed herein and above, a future condition can be predicted prior to its occurrence to allow for prophylactic treatment. Similar to other embodiments herein, a predicted digital representation of the oral cavity at a future time point is generated based on the predicted future state of the object in the oral cavity, and used to predict the future state.

It will be appreciated that method 1300 has been described above as an example. One or more steps of method 1300 may include one or more sub-steps. Method 1300 can include more steps or sub-steps, omit one or more steps or sub-steps, and/or repeat one or more steps or sub-steps. For example, three or more pieces of digital data may be obtained and analyzed instead of two. Some steps may be optional, such as steps 1340 and/or 1350. For example, in some embodiments, step 1350 is omitted, and method 1300 does not include predicting the object in the oral cavity and/or the future state of the oral cavity. In such an embodiment, an assessment of whether the change exceeds a predetermined threshold may be sufficient to indicate whether an undesirable tooth or orthodontic condition currently exists or is predicted to occur in the future.

In some embodiments, systems, methods, and apparatus for predicting future dental or orthodontic status implement one or more user interfaces to output data to a user and/or receive user input. For example, a computer system configured to predict a patient's future state may include instructions for generating a graphical user interface on a display (eg, monitor, screen, etc.). A computer system may be operatively coupled with one or more input devices (eg, keyboard, mouse, touch screen, joystick, etc.) to receive input from a user to interact with the user interface. The user interface may allow the user to visualize changes to the patient's oral cavity, any predicted future conditions, potential treatment options, and/or predicted outcomes of treatment options.

11A-11G show a user interface 1100 for predicting a patient's future state. The user interface 1100 may be generated by one or more processors of a computer system and displayed to a user on a display. The user interface 1100 includes one or more pull-down menus 1102 that allow the user to access various functions, a display window 1104 that displays a digital model of the oral cavity or other patient data, and chronological information about the displayed patient data. A timeline 1106, a list of identified and/or predicted dental or orthodontic conditions 1108, a list of potential treatment options or solutions for the condition 1110, and a button 1112 displaying cost information associated with one or more treatment options. ) or other interactive elements, and/or a navigation dial 1114 or other interactive elements for manipulating the view of the display window 1104. A user may interact with various elements of the user interface 1100 to perform various operations related to predicting a future state (eg, through clicks, keyboard input, etc.).

11A shows a user interface 1100 displaying digital data of a patient's oral cavity. Digital data may be generated from any suitable combination of the digital data types described herein (eg, scan data, sub-surface data, etc.) and displayed via user interface 1100 in any suitable format. there is. In the described embodiment, the digital data is displayed in display window 1104 as a three-dimensional model of the patient's upper arch 1116a and lower arch 1116b. In an alternative embodiment, the digital data may be displayed in other formats, such as a two-dimensional image of the oral cavity. If desired, models of other parts of the oral cavity may be displayed, such as models of the jaw, palate, airway, tongue, and/or TMJ. The user interface 1100 may include various types of tools and settings that allow a user to control what data is displayed in the display window 1104 and how it is displayed. For example, a user may have the option of toggling between different display formats, such as two-dimensional and three-dimensional views. In some embodiments, the user may control which parts of the oral cavity are displayed, for example by selecting the appropriate field in the “view” option 1118 of the pull down menu 1102 . For example, the user may choose to view the lower arch only, the upper arch only, or the lower and upper arches together (eg, occluded or separately). Optionally, the user may choose whether to view certain tissues, such as teeth, root, gums, tongue, jaw, palate, airway, etc., if such data are available. Further manipulation of the displayed data can be performed using the navigation dial 1114. For example, navigation dial 1114 can be used to control the position, orientation, and/or zoom level of data displayed in display window 1104 in three-dimensional space.

As discussed herein, digital data of a patient's oral cavity may be received at multiple points in time. Thus, the user interface 1100 can be used to display and compare data from different points in time. In some embodiments, timeline 1106 displays a chronological sequence of available digital data for a particular patient, for example as a scan over time. A user may select one or more time points using the timeline 1106 to display data from the selected time points. For example, in the illustration of FIG. 11A , a single time point has been selected, as indicated by marker 1120, and the digital data presented in display window 1104 corresponds to oral data obtained at the selected time point.

In some embodiments, if multiple viewpoints are selected, display window 1104 may display corresponding sets of digital data overlaid on each other, facilitating comparison of data from different viewpoints. Changes to the oral cavity between different time points can be visually indicated using highlighting, coloring, shading, marking, etc., according to the user's preference. In some embodiments, user interface 1100 may display measurement data that quantifies changes between different time points, such as dimensional, force, and/or vector data. Optionally, user interface 1100 may display an animation of the progress of the patient's oral cavity over one or more selected time points. In such an embodiment, the user interface 1100 may include one or more animation controls (not shown) that allow the user to play, stop, rewind, and/or fast-forward the animation.

The user interface 1100 may display a list 1108 of existing teeth or orthodontic conditions existing in the oral cavity of the patient at the selected time point. As discussed herein and above, the approaches presented herein can be used to ascertain pre-existing conditions based on oral digital data. For example, in the description of Fig. 11A, the small gap and small rotation states were confirmed at selected time points. In some embodiments, the portion of the oral cavity associated with the listed condition is identified on the data presented in display window 1104 using visual indicators such as labels, coloring, shading, and the like. For example, in the illustrated embodiment, symptomatic areas of the patient's upper and lower arches 1116a and 1116b are indicated with circles and labels.

In some embodiments, user interface 1100 may display a list 1110 of potential treatment options (also referred to herein as treatment solutions) for the identified tooth or orthodontic condition. The list 1110 may indicate which condition each solution is intended to treat. Optionally, the listed treatment solutions can be displayed as hyperlinks, and the user can click or select a hyperlink to provide images, descriptions, estimated duration of treatment, estimated cost, insurance, and reimbursement information of the treatment product or procedure. You can view additional information about each solution, such as , a list of treatment providers, a treatment provider web page, ordering information, and more. Alternatively or in combination, the predicted cost for a particular solution is displayed in response to the user selecting a “cost” button 1114 . In some embodiments, once a treatment solution is selected, user interface 1100 displays a prediction of treatment outcome, as discussed further herein. Optionally, user interface 1100 may generate a display of suggested timing for one or more medical appointments, for example, to monitor the progress of a detected condition and/or implement a selected treatment solution.

11B shows a user interface 1100 displaying digital data obtained at additional points in time. In the described embodiment, timeline 1106 has been updated to include additional time points. The list of identified conditions 1108 and potential treatment solutions 1110 has been updated to reflect oral progress. For example, compared to the patient data shown in FIG. 11A , patient data for later time points shown in FIG. 11B show an increase in the number and severity of conditions 1108 identified. This is also reflected by the increase in the number and intensity of treatment solutions 1110 displayed.

11C shows a user interface 1100 displaying a predicted digital representation of the oral cavity at a future point in time. A predicted digital representation may be created based on digital data of the oral cavity from previous time points, as discussed herein and above. In some embodiments, a predicted digital representation may be generated in response to a user selecting a “prediction” option 1122 from pull down menu 1102 . The user may indicate a point in the future at which the forecast should be generated, for example by selecting from one or more preset options or by entering a user-defined date.

Once a time interval is selected, according to the methods presented herein, a predicted digital representation is created using digital data from previous time points. Optionally, the user may select which time point is used to generate the prediction, for example by selecting a desired time point from timeline 1106 . The resulting predicted digital representation may be displayed as a two-dimensional or three-dimensional model of the oral cavity in the display window 1104, such as models of the patient's lower arch 1124a and upper arch 1124b. A user may adjust how the predicted representation is displayed in display window 1104 (eg, by adjusting position, orientation, zoom, view, etc.), as discussed herein with respect to FIG. 11A . Timeline 1106 may be updated to show a chronological relationship between a future point in time represented by the prediction (e.g., as indicated by marker 1126) and a point in time corresponding to the digital data used in the prediction. there is. In addition, the user interface 1100 may include tools that allow the user to compare previous and/or current conditions of the oral cavity to predicted conditions of the oral cavity. For example, a predicted digital representation can be overlaid on digital data from a previous point in time, and changes between previous and future states can be visually displayed using highlighting, coloring, shading, marking, etc., according to the user's preference. can If desired, a quantitative measure of change can be calculated and displayed. As another example, user interface 1100 can be configured to display an animated representation of how the user's oral cavity progresses from a previous point in time to a future point in time.

As discussed herein and above, the predicted digital representation can be used to predict one or more orthodontic or dental conditions that may occur in the oral cavity at a selected future time point. The predicted future state may be displayed to the user in list 1108 and/or displayed on the model in display window 1104 . In addition, potential treatment solutions for the identified condition may be displayed in listing 1110 along with links to related resources where appropriate. For example, the predictions illustrated in FIG. 11C represent 5 years from the last time point, and the patient will develop a large spacing and small compaction state in addition to the small spacing and small rotation already present at the last time point (see FIG. 11A ). purple). The user interface 1100 also indicates that orthodontic treatment is a potential treatment solution to treat and/or prevent the identified condition.

11D shows a user interface 1100 displaying the predicted outcome of a selected treatment solution. The prediction result can be displayed as a two-dimensional or three-dimensional model representing the condition of the oral cavity at a specific point in time after implementation of the selected solution. For example, in the described embodiment, models of the patient's upper arch 1128a and lower arch 1128b are displayed in the display window 1104, and the predicted result of the selected treatment solution 1130 at a future time point 1132. respond to As discussed herein, the user may adjust how the model representing the prediction results is displayed in the display window 1104 (eg, by adjusting the position, orientation, zoom, view, etc.). Optionally, interface 1100 may display other types of data related to the predicted outcome, such as predicted duration of treatment, estimated cost, insurance and reimbursement information, list of treatment providers, treatment provider web page, ordering information, and the like. .

In some embodiments, user interface 1100 allows the user to view the predicted outcome, as well as the predicted future state of the oral cavity if left untreated (e.g., by selecting a corresponding time point in timeline 1106). ) to compare with the previous and/or current state. For example, a model representing a predictive outcome can be overlaid onto digital data from a previous time point and/or a predicted digital representation of an untreated state. Changes between the various digital models can be visually indicated by highlighting, coloring, shading, marking, and the like. If desired, a quantitative measure of change can be calculated and displayed.

Optionally, if multiple treatment solutions are available, interface 1100 includes tools for comparing characteristics (eg, cost, duration, etc.) and/or results of different treatments to facilitate decision making. can do. For example, interface 1100 may display multiple models of the oral cavity representing the results of different treatment solutions (eg, overlaid on one another), thereby facilitating visual comparison of the efficacy of different treatments. . As another example, the predicted cost, duration, and/or any other relevant parameters for each treatment solution can be displayed and compared, for example in a list or table format.

11E shows a user interface 1100 displaying a comparison of digital data of a patient's oral cavity. Although the described embodiments represent a comparison of intraoral data obtained at two different time points, the embodiments herein are equally applicable to comparisons between different types of data, such as a comparison between previously obtained oral data and a predicted future state of the oral cavity. It will be appreciated that this may apply. Also, the approaches herein can be used to compare three or more data sets (eg, 3, 4, 5, or more data sets), if desired.

In some embodiments, the comparison is presented to the user in display window 1104 as an overlay of second model 1136 and first model 1134 . The user can select which data sets are compared, for example by making appropriate selections in the timeline 1106 . In the description of FIG. 11E , a first model 1134 corresponds to digital data of the oral cavity obtained at a first time point 1138 and a second model 1136 corresponds to data obtained at a subsequent time point 1140 . Similar to other embodiments herein, the user can select which part of the oral cavity should be displayed in the comparison (e.g., via “view” option 1118 and/or display settings 1142); , and/or by adjusting the displayed view (via the navigation dial 1114), the data presented in the display window 1104 can be manipulated.

The different models shown in the display window 1104 can be visually distinguished from each other through epiphyses, shading, outlines, accents, and the like. For example, the first model 1134 is shown as a dotted line, while the second model 1136 is shown as a volumetric representation. Additionally, any differences or changes between the displayed models may be indicated through visual indicators such as highlighting, coloring, shading, marking, labeling, and the like. Optionally, if a difference or change indicates an existing or predicted future state, it may be displayed to the user in display window 1104 and/or status list 1108 . For example, in the described embodiment, a particular tooth of the patient has been worn between a first time point 1138 and a second time point 1140 , and “tooth grinding” is displayed in the status list 1108 .

11F shows a user interface 1100 displaying a model 1144 of a patient's arch in an occlusion condition. As discussed herein and above, data representing the spatial relationship between the patient's upper and lower arches can be obtained and used to model the patient's occlusion. Thus, alternatively or in combination with displaying the arch separately (eg, as shown in FIGS. 11A-11E ), the user interface 1100 may be used to display the patient's arch in occlusion. An indication of a patient's arch in an occlusion condition can be beneficial in predicting and/or indicating progression of an occlusion-related condition, such as an underbite, overbite, crossbite, or similar Class II or III malocclusion. Optionally, the occlusion data may be supplemented with data for other parts of the jaw to provide a more complete representation of the patient's intraoral anatomy, thereby diagnosing complex conditions involving multiple areas of the oral cavity and help with treatment For example, TMJ and root data may be displayed along with occlusal data to facilitate visualization of root movement and bruxism problems that may contribute to, for example, TMJ pain.

11G shows a user interface 1100 displaying a digital model 1146 of a treatment solution for sleep apnea. In some embodiments, a treatment solution for sleep apnea includes applying an oral appliance (eg, a mandibular forward movement splint) to the patient's jaw while sleeping such that the patient's lower jaw advances relative to the upper jaw. Mandibular forward movement can reduce the occurrence of sleep apnea events, for example by moving the tongue away from the upper airway. Accordingly, the user interface 1100 may be used to display a model 1146 representing the relative position of the patient's chin generated by wearing the device. The model 1146 may be created using digital data of the patient's mouth at a current or previous point in time, such as scan data and/or bite data of teeth representing the occlusion relationship between the jaws. In some embodiments, additional portions of the oral cavity may also be displayed, such as models of the tongue, palate, and/or upper respiratory tract. Optionally, if desired, user interface 1100 may allow the user to manipulate model 1146 (eg, change the position of the upper and/or lower jaw) to adjust a target amount of forward movement of the jaw. . In some embodiments, when a user orders an oral device treatment to treat a patient's sleep apnea, the model 1146 is sent to the treatment provider and/or device manufacturer as part of the ordering process to facilitate design and manufacture of the oral device. can be sent to

This disclosure also provides a computer system that can be programmed or otherwise configured to implement the methods provided herein.

12 schematically illustrates a system 1200 comprising a computer server (“server”) 1201 programmed to implement the methods described herein. Server 1201 may be referred to as a "computer system". Server 1201 includes a central processing unit (CPU, also referred to herein as “processor” and “computer processor”), which may be a single-core or multi-core processor, or multiple processors for parallel processing. Server 1201 also includes memory 1210 (eg, random access memory, read-only memory, flash memory), an electronic storage device (eg, hard disk), and a communication interface for communicating with one or more other systems. 1220 (eg, a network adapter), and peripherals 1225 such as caches, other memory, data storage devices, and/or electronic display adapters. The memory 1210, storage device 1215, interface 1220, and peripheral device 1225 communicate with the CPU 1205 through a communication bus (solid line), such as that of a motherboard. The storage device 1215 may be a data storage unit (or data storage) that stores data. Server 1201 is operatively connected with a computer network (“network”) 123 with the aid of a communication interface 1220 . Network 1230 may be the Internet, the Internet and/or extranet, or an intranet and/or extranet in communication with the Internet. In some cases, network 1230 is a telecommunications and/or data network. Network 1230 may include one or more computer servers that enable distributed computing, such as cloud computing. In some cases, network 1230 may implement a peer-to-peer network that, with the help of server 1201 , may allow devices to couple with server 1201 to act as clients or servers. can Server 1201 communicates with imaging device 1245, such as an intraoral scanning device or system. Server 1201 can communicate with imaging device 1245 over network 1230 or, alternatively, through direct communication with imaging device 1245 .

The storage device 1215 may store files such as computer readable files (eg, 3D intraoral scan files). In some cases server 1201 may include one or more additional data storage devices that are external to server 1201, such as located on a remote server that communicates with server 1201 over an intranet or the Internet.

In some circumstances, system 1200 includes a single server 1201 . In other situations, system 1200 includes multiple servers that communicate with each other via an intranet and/or the Internet.

The methods described herein are implemented by machine (or computer processor) executable code (or software) stored in an electronic storage location on server 1201, such as, for example, memory 1210 or electronic storage 1215. It can be. In use, code may be executed by the processor 1205. In some cases, the code may be retrieved from storage 1215 and stored on memory 1210 in preparation for access by processor 1205 . In some circumstances, electronic storage device 1215 may be omitted, and machine-executable instructions are stored on memory 1210 . Alternatively, the code may be executed on a remote computer system.

The code may be precompiled and configured for use with a machine having a processor adapted to execute the code, or it may be compiled during runtime. The code may be provided in a programming language of choice to enable execution of the code in a pre-compiled or compiled manner.

Like server 1201, aspects of the systems and methods presented herein may be embodied in programming. Various aspects of technology can generally be thought of as “products” or “articles of manufacture” in the form of machine (or processor) executable code and/or related data embodied or carried on on a tangible machine-readable medium. Machine executable code may be stored on an electronic storage device, such as a memory (eg, read only memory, random access memory, flash memory) or a hard disk. “Storage” tangible media may include tangible memory, or modules related thereto, of any or all computers, processors, etc., such as various semiconductor memories, tape drives, disk drives, etc., which are at any time non-software programming - Can provide temporary storage. All or part of the Software may be communicated over the Internet or various other telecommunications networks from time to time. Such communication may, for example, load software from one computer or processor to another computer or processor, for example from a management server or host server to the computer platform of an application server. Accordingly, other tangible media that may carry software components include optical, electrical, and electromagnetic waves, as used across physical interfaces between local devices, over wired and optical landline networks and various wireless links. The physical element that carries these waves, such as a wired or wireless link, an optical link, etc., may also be considered as a medium carrying software. As used herein, unless limited to a non-transitory, tangible “storage” medium, the term computer or machine “readable medium” refers to any medium that participates in providing instructions to a processor for execution. refers to

Thus, machine-readable media, such as computer-executable code, can take many forms, including but not limited to tangible storage media, carrier wave media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device, such as any computer, which can be used to run a database or the like shown in the drawings. Volatile storage media includes dynamic memory, such as the main memory of a computer platform. A tangible transmission medium is coaxial cable; Includes copper wire and optical fiber, including wires that contain buses in computer systems. Carrier-wave transmission media may take the form of electrical or electromagnetic signals, such as those generated during radio frequency (RF) and infrared (IR) data communications, or acoustic or light waves. Thus, common forms of computer readable media include, for example: floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other Optical media, punch cards, paper tape, any other physical storage medium with a pattern of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or cartridge, that transmits data or instructions. A carrier wave, a cable or link that carries such a carrier wave, and any other medium from which a computer can read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

Server 1201 may perform data mining, extract, transform, and load (ETL), or spidering (where the system retrieves data from a remote system over a network, accesses an application programmer interface, or parses the resulting markup). It can be configured with operations (including web spidering) to cause the system to load information from raw data sources (or mined data) into the data warehouse. Data warehouses can be configured to use business intelligence systems (eg Microstrategy®, Business Objects®).

Results of the methods of the present disclosure may be displayed to a user on a user interface (UI), such as a graphical user interface (GUI) of a user's electronic device, such as, for example, a health care provider. A UI, such as a GUI, may be presented on a display of a user's electronic device. The display may be a capacitive or resistive touch display. Such displays may be used with other systems and methods of the present disclosure.

One or more processors may be programmed to perform various steps and methods as described with reference to various embodiments and implementations of the present disclosure. An embodiment of the system of the present application, as described above, may be composed of various modules, for example. Each module can contain various subroutines, procedures, and macros. Each module can be compiled individually and linked into a single executable program.

It will be apparent that the number of steps using this method is not limited to that described above. Also, the method does not require all steps described to be present. Although the foregoing method has been described as individual steps, one or more steps may be added, combined, or even deleted without departing from the intended function of the embodiment. It will be clear that the method described above can also be performed in a partially or substantially automatic manner.

As will be appreciated by those skilled in the art, the methods of the present disclosure may be embodied, at least in part, in software and performed on a computer system or other data processing system. Thus, in some demonstrative embodiments, hardware may be used in combination with software instructions implementing the present disclosure. Any process description, element or block in the flowcharts described herein and/or depicted in the accompanying drawings is a module, segment, or portion of code containing one or more executable instructions that implements a particular logical function or element in a process. should be understood as potentially representing Also, functions described in one or more examples may be implemented in hardware, software, firmware, or any combination of the above. If implemented in software, the functions may be transmitted or stored as one or more instructions or code on a computer readable medium, including a general purpose microprocessor, application specific integrated circuit, field programmable logic array, or other logic circuit. may be executed by a hardware-based processing unit, such as one or more processors.

As used herein, the term “and/or” is used as a functional word to indicate that two words or expressions are included together or separately. For example, A and/or B includes A alone, B alone, and A and B together.

Although preferred embodiments have been shown and described herein, it will be apparent to those skilled in the art that these embodiments are provided by way of example only. Now, various modifications, changes, and replacements will be made to those skilled in the art without departing from the present invention. It should be understood that various alternatives to the embodiments described herein may be used in practicing the present invention. By way of non-limiting example, it will be understood by those skilled in the art that a particular feature or characteristic described with reference to one figure or embodiment may be suitably combined with a feature or characteristic described in another figure or embodiment. . It is intended that the following claims define the scope of the invention and that methods and structures within the scope of the claims and equivalents thereof be covered thereby.

Claims (30)

A computer-implemented method for calculating the future position of an intraoral object in a patient's oral cavity, comprising:
receiving first digital data representing an actual state of the oral cavity at a first point in time;
receiving second digital data indicating an actual state of the oral cavity at a second time point different from the first time point;
processing data comprising the first and second digital data to determine a velocity of an intraoral object of the oral cavity over the first and second time points; and
determining a future position of the object in the oral cavity at a future time point based on the velocity;
The method of claim 1 , wherein the future position is determined before the intraoral object is in the future position. The method of claim 1,
wherein the first and second digital data each include one or more of surface data or sub-surface data of the oral cavity. The method of claim 1,
The method further comprising displaying a graphical representation of the intraoral object at the future location of the intraoral object on a user interface presented on a display. The method of claim 1,
determining a position change of an object in the oral cavity between the first and second viewpoints based on the first and second digital data; and
estimating whether the change in position exceeds a predetermined threshold. The method of claim 4,
and outputting a warning to a user in response to an assessment that the location change exceeds the predetermined threshold. The method of claim 4,
The predetermined threshold is input by a user. The method of claim 4,
wherein the predetermined threshold is determined based on one or more of user preferences, patient characteristics, or values from dental or orthodontic literature. The method of claim 4,
wherein the predetermined threshold indicates an undesirable tooth or orthodontic condition. The method of claim 4,
generating a plurality of options for generating a desired tooth or orthodontic result in response to an assessment that the change in position exceeds the predetermined threshold; and
and displaying the plurality of options on a user interface presented on a display. The method of claim 9,
The plurality of options include a plurality of treatment options for an undesirable tooth or orthodontic condition, and
The displaying of the plurality of options includes displaying at least one of price information, treatment time information, complication treatment information, and insurance reimbursement information related to each of the plurality of treatment options. The method of claim 1,
The method of claim 1, wherein the intraoral object includes one or more of a tooth crown, tooth root, gum, airway, palate, tongue, or jaw. The method of claim 1,
further comprising processing data comprising the first and second digital data to determine a rate of change for one or more of the shape, size, or color of the intraoral object. The method of claim 12,
The method of claim 1 , wherein the object in the oral cavity includes a tooth, and the rate of change includes a rate of tooth shape change. The method of claim 12,
The method of claim 1 , wherein the object in the oral cavity includes gums, and the rate of change includes a rate of change in shape of gums. The method of claim 1,
The method of claim 1 , wherein determining a future position of the object in the oral cavity comprises determining a movement trajectory of the object in the oral cavity based on the velocity. The method of claim 15
The movement trajectory is linear. The method of claim 1,
receiving third digital data representing the oral cavity at a third viewpoint different from the first and second viewpoints; and
processing data comprising the first, second, and third digital data to determine the velocity of the intraoral subject over the first, second, and third time points. The method of claim 17
determining a movement trajectory of the intraoral object based on the velocity over the first, second, and third time points, wherein the movement trajectory is non-linear; and
and determining the future position of the object in the oral cavity based on the non-linear movement trajectory. The method of claim 18
The method of claim 1 , wherein the non-linear travel trajectory comprises at least one of a change in a direction of travel or a change in speed of travel. The method of claim 17
processing data comprising the first, second, and third digital data to determine a force vector associated with the intraoral object over the first, second, and third time points; How to include. The method of claim 1,
generating a predicted digital representation of the oral cavity at a future time point subsequent to the first and second time points, based on the future position of the subject within the oral cavity. The method of claim 1,
The method of claim 1 , wherein determining the future position of the object within the oral cavity comprises extrapolating the velocity to the future time point using linear or non-linear extrapolation. The method of claim 1,
determining a future state of the oral cavity based on the future position of the subject in the oral cavity;
The future condition includes an unwanted tooth or orthodontic condition predicted to occur at the future time point if the oral cavity is left untreated,
wherein the future state is determined prior to the occurrence of the future state. The method of claim 1,
wherein at least one of receiving the first digital data, receiving the second digital data, processing the data, or determining the future location is performed with the aid of one or more processors. A computer system for calculating the future position of an intraoral object in a patient's oral cavity, comprising:
one or more processors; and
When executed by the one or more processors, the system:
Receiving first digital data representing an actual state of the oral cavity at a first point in time;
Receiving second digital data representing an actual state of the oral cavity at a second time point different from the first time point;
processing data comprising the first and second digital data to determine a velocity of an intraoral object of the oral cavity over the first and second time points; and
A memory including instructions for determining a future position of the object in the oral cavity at a future time point based on the velocity;
The system of claim 1 , wherein the future position is determined before the intraoral object is in the future position. A computer-implemented method for calculating a positional change over time of an intraoral object in a patient's oral cavity, comprising:
receiving first digital data representing an actual state of the oral cavity at a first point in time;
receiving second digital data representing an actual state of the oral cavity at a second time point different from the first time point;
processing data comprising the first and second digital data to determine a change in position of the subject in the oral cavity between the first and second time points; and
and evaluating if the position change exceeds a predetermined threshold. The method of claim 26 ,
and outputting a warning to a user in response to an assessment that the location change exceeds the predetermined threshold. The method of claim 26 ,
generating a plurality of options for generating a desired tooth or orthodontic result in response to an assessment that the change in position exceeds the predetermined threshold; and
The method further comprising presenting the plurality of options on a user interface presented on a display. The method of claim 26 ,
wherein the predetermined threshold is determined based on one or more of user preferences, patient characteristics, or values from dental or orthodontic literature. A computer system for calculating a positional change over time of an intraoral object in a patient's oral cavity, comprising:
one or more processors; and
When executed by the one or more processors, the system:
Receiving first digital data representing an actual state of the oral cavity at a first point in time;
Receiving second digital data representing an actual state of the oral cavity at a second time point different from the first time point;
processing data comprising the first and second digital data to determine a change in position of the object in the oral cavity between the first and second time points; and
and a memory comprising instructions to evaluate whether the change in position exceeds a predetermined threshold.

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