JP7195466B2 - DATA GENERATION DEVICE, SCANNER SYSTEM, DATA GENERATION METHOD, AND DATA GENERATION PROGRAM - Google Patents

Description

The present invention relates to a data generation device, a scanner system including the data generation device, a data generation method, and a data generation program.

2. Description of the Related Art Conventionally, in the field of dentistry, a three-dimensional scanner for acquiring a three-dimensional shape of a site including a missing tooth is known in order to digitally design a prosthesis or the like on a computer. For example, Patent Literature 1 discloses a technique of recording the shape of the oral cavity by imaging the intraoral cavity using a three-dimensional camera of a three-dimensional scanner.

JP-A-2000-74635

An operator such as a dentist can record shape data (hereinafter also referred to as "three-dimensional data") of a patient's oral cavity by using the technology disclosed in Patent Document 1. The recorded three-dimensional data is used to fabricate a prosthesis for filling a missing tooth or the like. For example, the operator acquires three-dimensional data including the patient's missing tooth with a three-dimensional scanner, and based on the acquired three-dimensional data, creates data (hereinafter referred to as " The prosthesis data") is digitally designed on a computer. Alternatively, the operator sends the acquired three-dimensional data to a dental technician, and the dental technician digitally designs the prosthesis data on a computer to create a prosthesis that fits the defect site based on the three-dimensional data. do.

However, since the skill levels of operators and dental technicians vary, it is not always easy to generate appropriate prosthetic data for the defect site based on the acquired three-dimensional data. A technology that can create objects is desired.

The present invention has been made to solve such problems, and provides a data generation device, a scanner system comprising the data generation device, a data generation method, and a data generation device that can more easily fabricate an appropriate prosthesis. The purpose is to provide a program for data generation.

According to one example of the present disclosure, a data generation apparatus is provided for generating prosthesis data for fabricating a dental prosthesis. The data generation device includes an input unit to which three-dimensional data including at least the missing tooth, which is a missing tooth, is input, and a prosthetic device that fits the missing part of the missing tooth based on the three-dimensional data and the generation model input from the input unit. A generator that generates prosthesis data for fabricating an object, and an identification unit that identifies whether the prosthesis data generated by the generator is appropriate. In the learning stage of the generative model, the generating unit generates prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the dentition including the tooth other than the arbitrary tooth and the generative model. The identification unit estimates the type of tooth corresponding to the prosthesis data generated by the generation unit, and determines whether the prosthesis data is appropriate based on the result of estimation of the type of tooth and the type of an arbitrary tooth. A generative model is machine-learned based on the identification result of the identification unit.

According to one example of the present disclosure, a scanner system for obtaining tooth shape information is provided. The scanner system includes a three-dimensional scanner that acquires three-dimensional data including at least the missing tooth, which is a missing tooth, using a three-dimensional camera, and a missing part of the missing tooth based on the three-dimensional data acquired by the three-dimensional scanner. and a data generator for generating prosthesis data for making a fitted prosthesis. A data generation device generates prosthetic data for manufacturing a prosthesis that fits a defect based on an input unit for inputting three-dimensional data, and three-dimensional data and a generation model input from the input unit. and an identification portion for identifying whether the prosthesis data generated by the generation portion is adequate. In the learning stage of the generative model, the generating unit generates prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the dentition including the tooth other than the arbitrary tooth and the generative model. The identification unit estimates the type of tooth corresponding to the prosthesis data generated by the generation unit, and determines whether the prosthesis data is appropriate based on the result of estimation of the type of tooth and the type of an arbitrary tooth. A generative model is machine-learned based on the identification result of the identification unit.

According to one example of the present disclosure, a data generation method for generating prosthesis data for making a computer-assisted dental prosthesis is provided. The data generation method includes a step of inputting three-dimensional data including at least a missing tooth, which is a missing tooth, as processing executed by a computer; Generating prosthesis data for making a site-fitting prosthesis; and identifying whether the prosthesis data generated by the generating step is adequate. In the learning stage of the generative model, the step of generating is based on the three-dimensional data of the dentition including the teeth other than the arbitrary tooth and the generative model, prosthesis data for producing the prosthesis corresponding to the arbitrary tooth. The generating and identifying step estimates a tooth type corresponding to the prosthesis data generated by the generating step, and generates the prosthesis data based on the tooth type estimation result and an arbitrary tooth type. is appropriate or not, and the generative model is machine-learned based on the identification result of the identifying step.

According to one example of the present disclosure, a data generation program is provided for generating prosthesis data for fabricating a dental prosthesis. The data generating program includes a step of inputting three-dimensional data including at least a missing tooth, which is a missing tooth, into a computer, and producing a prosthesis that fits the missing part of the missing tooth based on the three-dimensional data and the generation model. and identifying whether the prosthesis data generated by the generating step is appropriate. In the learning stage of the generative model, the step of generating is based on the three-dimensional data of the dentition including the teeth other than the arbitrary tooth and the generative model, prosthesis data for producing the prosthesis corresponding to the arbitrary tooth. The generating and identifying step estimates a tooth type corresponding to the prosthesis data generated by the generating step, and generates the prosthesis data based on the tooth type estimation result and an arbitrary tooth type. is appropriate or not, and the generative model is machine-learned based on the identification result of the identifying step.

According to the present invention, a suitable prosthesis can be produced more easily.

It is a schematic diagram which shows the example of application of the data generation apparatus which concerns on this Embodiment. FIG. 2 is a schematic diagram showing an example of a tooth to be scanned by a three-dimensional scanner according to the present embodiment; FIG. 4 is a schematic diagram for explaining types of teeth to be scanned by the three-dimensional scanner according to the present embodiment; FIG. 3 is a schematic diagram showing a functional configuration in a learning stage of the data generation device according to the present embodiment; FIG. 4 is a schematic diagram showing an example of prosthesis data generation processing of the data generation device according to the present embodiment; FIG. 4 is an enlarged view of a defective portion to be prosthetically restored by the data generation device according to the present embodiment; 1 is a schematic diagram showing a functional configuration of a data generation device according to an embodiment in a practical stage; FIG. 1 is a schematic diagram showing the overall configuration of a system according to an embodiment; FIG. It is a schematic diagram which shows the hardware constitutions of the data generation apparatus which concerns on this Embodiment. 4 is a flowchart for explaining learning processing executed by the data generation device according to the embodiment; 4 is a flowchart for explaining prosthesis data generation processing executed by the data generation device according to the present embodiment; FIG. 11 is a schematic diagram showing a functional configuration in a learning stage of a data generation device according to a modification;

Embodiments of the present invention will be described in detail with reference to the drawings. The same or corresponding parts in the drawings are given the same reference numerals, and the description thereof will not be repeated.

[Application example]
FIG. 1 is a schematic diagram showing an application example of a data generation device 100 according to this embodiment.

As shown in FIG. 1 , the user 1 can use the scanner system 10 to obtain three-dimensional shape data (three-dimensional data) of the oral cavity including the teeth of the subject 2 . It should be noted that the “user” is any person who uses the scanner system 10, such as an operator such as a dentist, a dental assistant, a teacher or student at a dental college, a dental technician, an engineer at a manufacturer, or an operator at a manufacturing plant. may be The “subject” may be any person to whom the scanner system 10 is applied, such as a patient at a dental clinic or a subject at a dental college. Also, the "subject" is not limited to a real person, and may be a human body model or a head model.

Scanner system 10 according to the present embodiment includes three-dimensional scanner 200 , data generation device 100 , and display 300 . The three-dimensional scanner 200 acquires three-dimensional data of a scanning target by a built-in three-dimensional camera. Specifically, the three-dimensional scanner 200 scans the oral cavity of the subject 2 to obtain positional information (for example, lateral direction, depth direction, height direction) and color information (for example, the color of the tooth surface) are acquired using an optical sensor or the like. The data generation device 100 generates a 3D image based on the 3D data acquired by the 3D scanner 200 and displays the generated 3D image on the display 300 .

Specifically, the user 1 uses a three-dimensional scanner 200 to digitally design on a computer prosthesis data for fabricating a prosthesis that fits the missing part of the missing tooth of the subject 2. Imaging the inside of the oral cavity. Each time the user 1 takes an image of the oral cavity, three-dimensional data is sequentially obtained, and a three-dimensional image of the oral cavity including at least the missing tooth is displayed on the display 300 . The user 1 confirms the three-dimensional image displayed on the display 300 and scans the missing part of the three-dimensional data. The three-dimensional data recorded in this way are used for manufacturing the prosthesis.

A "missing tooth" includes, for example, an abutment tooth, a cavity, and an implant body, whose shape has been lost due to caries or cutting during treatment. "Prostheses" include, for example, crowns, bridges, implants, inlays, and other known various fillings and caps employed in prosthetic dentistry.

For example, when prostheticing a missing tooth with a crown, an operator first forms an abutment tooth by scraping off the carious portion of the tooth with a cutting instrument such as a turbine, and then mounts a crown thereon. At this time, if the outer edge of the abutment tooth, called the margin, and the outer edge of the crown are not brought into close contact with each other, secondary caries may occur. Similarly, in the case of using an inlay to prosthetic a defect, secondary caries may occur if the inlay is not brought into close contact with the cavity after the carious portion of the tooth has been scraped away.

In addition, in the production of a prosthesis, the teeth around the missing part (missing tooth) (hereinafter also referred to as "peripheral teeth"), that is, the teeth adjacent to the missing tooth (hereinafter also referred to as "adjacent teeth") and the prosthesis It is also important that the distance between the object and the distance between the missing part (missing tooth) and the opposing tooth (hereinafter also referred to as "opposing tooth") and the prosthesis is appropriate. Note that "adjacent" includes being positioned next to the missing tooth while being in contact with the missing tooth, or being positioned next to the missing tooth without being in contact with the missing tooth.

Furthermore, although details will be described later, teeth include central incisors, lateral incisors, canines, first premolars, second premolars, first molars, second molars, and third molars. , there are various types. In addition, the shape of teeth is characteristic according to the type of tooth, and these shapes affect the meshing of the teeth. Therefore, it is important to produce a prosthesis of the same type as the missing tooth, that is, a prosthesis having the same shape (characteristics) as the missing tooth as much as possible.

As described above, there are many important points in the production of a prosthesis, but since the skill level of the operator and the dental technician who are the user 1 varies, it is necessary to generate prosthesis data appropriate for the defect site. It's not always easy.

Therefore, the scanner system 10 according to the present embodiment utilizes AI (Artificial Intelligence) of the data generation device 100, based on the three-dimensional data acquired by the three-dimensional scanner 200, suitable for the defective portion. prosthesis data.

Specifically, when the user 1 scans the oral cavity of the subject 2 using the three-dimensional scanner 200 , three-dimensional data including at least the missing tooth is input to the data generation device 100 . The data generation device 100 automatically generates prosthesis data that fits the defect site based on the input three-dimensional data and generation model. The prosthesis data, as three-dimensional data for fabricating the prosthesis itself, includes positional information (coordinates of the longitudinal, lateral, and height axes) of each of a plurality of points forming the prosthesis. A PCD file, an STL file, a PLY file, or the like is applied as the output format of the prosthesis data.

The prosthesis data generated by the data generation device 100 in this manner is output to the dental laboratory. At the dental laboratory, a dental technician fabricates a prosthesis based on the prosthesis data acquired from the data generation device 100 .

Further, when an automatic manufacturing device 600 capable of automatically manufacturing a prosthesis is placed in the dental clinic, the prosthesis data generated by the data generating device 100 may be output to the automatic manufacturing device 600. In this way, the user 1 can manufacture the prosthesis based on the prosthesis data also by the automatic manufacturing device 600 . Examples of automated manufacturing equipment 600 include milling machines and 3D printers.

As described above, according to the scanner system 10 of the present embodiment, the AI of the data generation device 100 is used to automatically generate prosthesis data based on the three-dimensional data acquired by the three-dimensional scanner 200. be done. By using AI, the scanner system 10 can also find tooth features that cannot be extracted by the user 1, so that the user 1 can more easily obtain an appropriate prosthesis.

[An example of a tooth to be scanned]
FIG. 2 is a schematic diagram showing an example of a tooth to be scanned by the three-dimensional scanner 200 according to this embodiment. In FIG. 2, the tooth to be scanned by the three-dimensional scanner 200 is represented by a diagram.

As shown in FIG. 2, when the tooth to be scanned by the three-dimensional scanner 200 is the maxillary incisor, the three-dimensional images obtained are images of the upper lip side, the palate side, and the incisal side. The user 1 scans the oral cavity of the subject 2 so as to include at least Also, when the teeth to be scanned by the three-dimensional scanner 200 are the canines and molars of the upper jaw, the obtained three-dimensional image should include at least images of the buccal region, the palatal region, and the occlusal region. , the oral cavity of a subject 2 is scanned by a user 1 . When the tooth to be scanned by the three-dimensional scanner 200 is a mandibular incisor, the three-dimensional image obtained includes at least images of the lower lip side region, the lingual region, and the incisal side region. A user 1 scans the oral cavity of a subject 2 . When the teeth to be scanned by the three-dimensional scanner 200 are the canines and molars of the mandible, the resulting three-dimensional image includes at least images of the buccal region, the lingual region, and the occlusal region. 1 scans the oral cavity of a subject 2 .

In general, the teeth of the subject 2 differ in shape and size depending on their type. For example, for maxillary incisors, the upper labial surface is generally U-shaped, whereas for maxillary canines, the buccal surface is generally pentagonal. Each tooth is characteristic in shape and size according to its type. Therefore, digitally designing prosthesis data while ascertaining the characteristics (types) of each tooth greatly depends on the knowledge of the operator and the dental technician.

[Type of tooth to be scanned]
FIG. 3 is a schematic diagram for explaining types of teeth to be scanned by the three-dimensional scanner 200 according to this embodiment.

As shown in FIG. 3, each tooth has a central incisor, a lateral incisor, a canine, a first premolar, a second premolar, a first molar, a second molar, and third molars. Further, in the oral cavity, the above-described teeth are generally present on each of the right upper jaw, the left upper jaw, the right lower jaw, and the left lower jaw.

Furthermore, each tooth is assigned a predetermined number according to its type and position. For example, the central incisor is assigned number 1, the lateral incisor is assigned number 2, the canine is assigned number 3, the first premolar is assigned number 4, the second premolar is assigned The number 5 is assigned, the first molar is assigned the number 6, the second molar is assigned the number 7, and the third molar is assigned the number 8.

[Functional Configuration in Learning Stage of Data Generating Device]
FIG. 4 is a schematic diagram showing a functional configuration in the learning stage of data generation device 100 according to the present embodiment.

As shown in FIG. 4, the data generation device 100 has an input section 1102, a generation section 1104, and an identification section 1106. FIG. These functions are realized by the arithmetic device 130 of the data generation device 100, which will be described later, executing the OS 127, the identification program 120, the learning program, the data generation program 125, and the like.

Three-dimensional data acquired by the three-dimensional scanner 200 is input to the input unit 1102 . The three-dimensional data input to the input unit 1102 includes not only the missing tooth (missing part), but also adjacent teeth adjacent to the missing tooth, opposing teeth opposing the missing tooth, teeth adjacent to the opposing tooth, and maxillary dentition. 3D data of at least one of each tooth in a state in which the dentition of the lower jaw is engaged with the lower jaw and the tooth on the opposite side of the missing tooth.

For example, referring to FIG. 3, if the missing tooth is the maxillary left canine (number 3), each tooth can be exemplified as follows. For example, the adjacent teeth adjacent to the maxillary left canine (3rd) include the maxillary left lateral incisor (2nd), central incisor (1st), first premolar (4th), or second minor tooth. Molar tooth (number 5) and the like. The opposing teeth facing the upper left canine (3rd) include the lower left canine (3rd). Examples of the tooth adjacent to the opposing tooth facing the upper left canine tooth (3rd) include the lower left lateral incisor (2nd) or the first premolar (4th). Further, the tooth on the opposite side of the missing tooth is the tooth on the left and right opposite sides in the row of teeth to which the missing tooth belongs. For example, the tooth on the opposite side of the maxillary left canine (number 3) includes the maxillary right canine (number 3). Note that the input unit 1102 is not limited to the actual three-dimensional data of the tooth acquired by the three-dimensional scanner 200, and a three-dimensional data model prepared in advance for learning may be input. Furthermore, the input unit 1102 stores the type (name or number) may be entered.

The generation unit 1104 generates prosthesis data for manufacturing a prosthesis that fits the defect based on the three-dimensional data and the generation model 114 input from the input unit 1102 . At this time, the generation unit 1104 generates prosthesis data according to generation conditions specified by generation condition data 119, which will be described later. Such a process of generating prosthesis data by the generation unit 1104 is also referred to as "prosthesis data generation process". When the type (name or number) of each tooth included in the three-dimensional data is input to the input unit 1102, the generation unit 1104 generates the type (name or number) of each tooth included in the three-dimensional data input by the user. or number) to generate prosthesis data.

The identification unit 1106 identifies whether or not the prosthesis data is appropriate based on the prosthesis data generated by the generation unit 1104 and the correct data input by the user 1 . To identify whether the prosthesis data is appropriate or not, it is necessary to identify whether the shape of the prosthesis manufactured based on the prosthesis data matches the shape of the sample tooth input as the correct data; To discriminate whether or not the degree of similarity between the shape of a prosthesis manufactured based on prosthesis data and the shape of a sample tooth input as correct data is equal to or greater than a reference value; identifying whether or not the type (name or number) of the prosthesis tooth matches the type (name or number) of the missing tooth input as the correct data. Such a process of identifying whether or not the prosthesis data is appropriate by the identification unit 1106 is also called "identification process".

In this embodiment, the identification unit 1106 is configured to estimate the type (name or number) of the tooth corresponding to the prosthesis data generated by the generation unit 1104 based on the machine-learned identification model 116. ing. Specifically, the identifying unit 1106 estimates the tooth name or tooth number shown in FIG. Identify whether it matches the type (name or number) of the missing tooth.

For example, if the sixth first molar on the right side of the mandible is missing, the user 1 provides information for identifying the type of missing tooth, such as "sixth" or "first molar" as correct data. It is input to the data generator 100 in advance. In the data generation device 100, when the three-dimensional data of the oral cavity in which the sixth first molar is missing is input, the generation unit 1104, based on the input three-dimensional data and the generation model 114, An attempt is made to generate prosthesis data for making a prosthesis with the characteristics of the number 6 first molars as much as possible. Based on the prosthesis data generated by the generation unit 1104 and the identification model 116, the identification unit 1106 estimates the type (name or number) of the prosthesis tooth to be manufactured based on the prosthesis data. It is determined whether or not the estimated result of the type matches the type (name or number) of the missing tooth input as the correct data.

Here, the discriminative model 116 is machine-learned in advance in a preparatory stage before learning the generative model 114 . Specifically, the discriminative model 116 includes a neural network (not shown) and parameters used by the neural network.

In the neural network of the discriminative model 116, processing by deep learning is performed because the intermediate layer has a multi-layered structure. In this embodiment, for example, VoxNet, 3D ShapeNets, Multi-View CNN, RotationNet, OctNet, FusionNet, PointNet, PointNet++, SSCNet, MarrNet, etc. are used as programs that perform processing specialized for 3D images. but other programs may be used. Also, an existing neural network mechanism of the discriminative model 116 may be applied.

The identification model 116 uses tooth information corresponding to the type (name or number) of the tooth associated with the missing tooth, that is, the tooth for which the prosthesis needs to be generated, and the three-dimensional data. It is optimized (adjusted) by machine learning based on the identification result of the tooth type (name or number).

For example, when the identification model 116 receives 3D intraoral data for learning, it uses a neural network to extract tooth features based on the 3D data, and based on the extracted tooth features, the tooth type (name or number). Then, the identification model 116 does not update the parameter based on the tooth type estimated by itself and the tooth type (tooth information) associated with the input three-dimensional data for learning, if both match. If the two do not match, the parameters are updated so that they match, thereby optimizing the parameters. As a result, the discriminative model 116 uses teacher data including three-dimensional data as input data and tooth types (names or numbers) as correct data to optimize parameters, thereby performing machine learning. be done. By repeating such machine learning in the preparatory stage before learning the generative model 114, a learned discriminant model 116 is obtained. Note that the discriminant model 116 is not limited to updating the parameters, and may also update the neural network (for example, the algorithm of the neural network).

The identification result obtained by identification section 1106 is fed back to generation section 1104 . The generative model 114 is machine-learned based on the identification result fed back from the identification unit 1106 . Such a process of performing machine learning on the generative model 114 is also referred to as a “learning process”.

For example, generative model 114 includes a neural network (not shown) and parameters used by the neural network. In the neural network of the generative model 114, processing by deep learning is performed because the intermediate layer has a multi-layered structure. In the present embodiment, programs that perform processing specialized for three-dimensional images include, for example, AAE, "Learning Representations and Generative Models", ShapeVAE, "Shape Generation using Spatially Partitioned Point Clouds", FoldingNet, P2P-Net, PCN (Point Completion Network), PPF-FoldingNet, PC-GAN, and DeepSDF are used, but other programs may be used. Also, an existing neural network mechanism of the generative model 114 may be applied.

In the learning process, based on the identification result, the generative model 114 compares the tooth type (name or number) of the prosthesis corresponding to the prosthetic data generated by itself with the tooth type (name or number) that is the correct data. If it is determined that they match, the parameters are not updated. If it is determined that they do not match, the parameters are updated so that they match, thereby optimizing the parameters. Thus, in the learning process, the generative model 114 is machine-learned by optimizing the parameters. Note that in the generative model 114, the parameters are not limited to being updated, and a neural network (for example, a neural network algorithm) may be updated.

In the data generation device 100 configured as described above, three-dimensional data acquired by the three-dimensional scanner 200 or three-dimensional data prepared in advance for learning is input. Before learning, the generation unit 1104 cannot generate appropriate prosthesis data that matches the defect site, but first generates prosthesis data based on the three-dimensional data and the generation model 114 according to its own prediction. Try. The identification unit 1106 estimates the type of tooth corresponding to the prosthesis data generated by the generation unit 1104, and identifies whether the estimated type of tooth matches the type of tooth that is correct data, The identification result is fed back to the generation unit 1104 . The generation unit 1104 machine-learns the generative model 114 based on the feedback identification results, thereby generating appropriate prosthesis data closer to the correct data than before learning. By repeating such machine learning, the generation unit 1104 eventually becomes able to generate prosthesis data that can produce an appropriate prosthesis. Furthermore, when the type (name or number) of each tooth included in the three-dimensional data, such as a missing tooth, an adjacent tooth, an opposing tooth, a tooth adjacent to the opposing tooth, and a tooth on the opposite side, is input to the input unit 1102, The generation unit 1104 generates prosthesis data based on the type (name or number) of each tooth included in the three-dimensional data. In this case, the generation unit 1104 performs machine learning in consideration of the type (name or number) of each tooth input by the user, thereby generating more accurate prosthesis data.

[Example of prosthesis data generation process]
FIG. 5 is a schematic diagram showing an example of prosthesis data generation processing of the data generation device 100 according to the present embodiment. FIG. 6 is an enlarged view of a defective portion to be prosthetically restored by the data generating apparatus 100 according to this embodiment. As shown in FIG. 5, the data generation device 100 generates prosthetic data based on the three-dimensional data acquired by the three-dimensional scanner 200 and the generation model 114 according to predetermined generation conditions specified by generation condition data 119, which will be described later. to generate

Specifically, first, three-dimensional data of the oral cavity is acquired by the three-dimensional scanner 200 (STEP 1). For example, with the subject 2's mouth open, the teeth located in the upper jaw are scanned and the teeth located in the lower jaw are scanned. Furthermore, when the subject 2 has his/her mouth closed, that is, when the upper and lower jaws are in occlusion, the teeth located in the upper and lower jaws are scanned.

In this way, when the intraoral cavity is scanned by the three-dimensional scanner 200, not only the missing tooth (missing part) but also adjacent teeth adjacent to the missing tooth, opposing teeth opposing the missing tooth, and teeth adjacent to the opposing tooth , each tooth in a state in which the tooth row of the upper jaw and the tooth row of the lower jaw are in mesh, and at least one of the teeth on the opposite side of the missing tooth are input to the data generation device 100. be. At this time, if the missing tooth is an abutment tooth or a tooth with a cavity, the user may specify the margin line of the outer edge of the missing portion. Designation of such margin lines may be realized by a function of detecting edge lines.

Next, the generation unit 1104 of the data generation device 100 defines a spatial grid (three-dimensional grid) in the image embodied based on the three-dimensional data (STEP 2). The spatial grid is a space that defines the three-dimensional positions of the missing tooth and the teeth adjacent to the missing tooth in the three-dimensional space specified by the three-dimensional data of the tooth row. More specifically, the spatial grid is, for example, a three-dimensional space that is a predetermined size larger than the missing tooth (for example, 1.5 times the size of the missing tooth), and in which the X axis (for example, the width of the tooth A direction axis), a Y axis (e.g., the tooth depth axis), and a Z axis (e.g., the tooth height axis) are defined, and on each axis an object existing in the space is defined. A space for recording the presence/absence information of a prosthesis or a space for generating prosthesis data.

For example, FIG. 6 shows a spatial grid when the missing tooth is an abutment tooth. As shown in FIG. 6, a grid-like spatial grid is defined so as to surround the abutment tooth, which is the missing tooth.

In this way, by making the space grid larger than the missing tooth, not only the missing tooth but also the adjacent tooth adjacent to the missing tooth, the opposing tooth facing the missing tooth, and the missing tooth in the tooth adjacent to the opposing tooth Presence/absence information can also be recorded for part of the side. Therefore, the spatial grid is set to include the adjacent tooth, the opposing tooth, and a portion of the tooth adjacent to the opposing tooth, with the missing tooth in the center. In addition, by limiting the size of the spatial grid to a predetermined size, compared to the case where the size of the spatial grid is not limited, it is possible to reduce the computational processing load when performing machine learning on the generative model 114 that generates the prosthesis data. be able to.

Next, the generating unit 1104 identifies adjacent teeth adjacent to the missing tooth corresponding to the missing portion included in the defined spatial grid, opposing teeth opposing the missing tooth, and teeth adjacent to the opposing tooth (STEP 3). . Specifically, the generation unit 1104 generates specific information (for example, "1") at positions corresponding to the contours of the teeth in the XZ cross section of the missing tooth, the adjacent tooth, the opposing tooth, and the tooth adjacent to the opposing tooth. (such as numbers and specific colors).

In FIG. 5, the outline of the right part of the adjacent tooth a is shown on the left side of the missing tooth, and the left part of the adjacent tooth b is shown on the right side of the missing tooth. In the upper left of the missing tooth, the lower right partial outline of the opposing tooth c is shown, and in the upper right of the missing tooth, the lower left partial outline of the opposing tooth and adjacent tooth d is shown. . Specific information (for example, a number such as "1" or a specific color) is associated along these contours. Thereby, the generation unit 1104 can specify the shape and position of the missing tooth, the adjacent tooth, the opposed tooth, and the tooth adjacent to the opposed tooth.

Next, the generator 1104 determines the shape of the prosthesis based on the identified missing tooth, adjacent tooth, opposing tooth, and the shape and position of the tooth adjacent to the opposing tooth (STEP 4). Specifically, the generating unit 1104 generates specific information ( In this example, "1") is linked. This allows the generation unit 1104 to determine the shape of the prosthesis based on the shape of the tooth adjacent to or facing the defect site.

For example, the generation unit 1104 associates the specific information (“1”) so as to contact the contours of the left and right adjacent teeth, and associates the specific information (“1”) so as to contact the contours of the upper left and upper right opposing teeth. . The space surrounded by the specific information linked in this way is shaped to cover the missing tooth.

Although illustration is omitted, the generation unit 1104 repeats STEP 3 and STEP 4 described above a plurality of times while shifting the XZ cross section in the Y-axis direction at a predetermined interval, thereby determining the three-dimensional shape of the prosthesis.

In this way, the generation unit 1104 can determine the shape of the prosthesis (especially the contour and height) so as to compensate for the missing part, but does not recognize the type of tooth. However, it is not possible to determine a shape suitable for the type of missing tooth. However, as described with reference to FIG. 4, the generation unit 1104 repeats machine learning based on the identification results fed back from the identification unit 1106, thereby determining a shape suitable for the type of missing tooth.

For example, when the number 6 first molar is missing, the generation unit 1104 does not recognize that the number of the missing tooth is number 6. It is not possible to define the shape of a prosthesis with molar characteristics. However, the generation unit 1104 repeats machine learning based on the identification results fed back from the identification unit 1106, and eventually determines the shape of the prosthesis having the characteristics of the first molar No. 6 that matches the type of missing tooth. be able to

The size of the prosthesis, the positional relationship between the prosthesis site (missing tooth) and adjacent teeth or opposing teeth, and the contact points between the prosthesis and the surrounding teeth in the occlusal state differ for each patient. There is a need to generate the optimal prosthesis for the patient. In view of this point, the generation unit 1104 according to the present embodiment uses spatial grids as shown in FIGS. Relationships can be identified and the optimal prosthesis shape can be determined. In addition, parts that do not have contact with the prosthesis (e.g., labial, buccal, lingual, palatal, etc.) or parts that should not be in contact (e.g., occlusal grooves, pits, etc.) ) can be optimized by feedback machine learning based on the identification result (the tooth type determination result) by the identification unit 1106 .

As described above, the generation unit 1104 according to the present embodiment determines the shape of the prosthesis using the spatial grid, while the identifying unit 1106 determines the shape of the prosthesis that is difficult to determine only by using the spatial grid. It is configured to determine the optimal shape by machine learning of feedback based on the identification result. Thereby, the generation unit 1104 can generate optimum prosthesis data according to the condition of the teeth in the patient's oral cavity.

Although illustration is omitted, the generation unit 1104 generates the tooth row adjacent to the missing tooth, the opposing tooth opposing the missing tooth, and the tooth adjacent to the opposing tooth, as well as the tooth row of the upper jaw and the row of teeth of the lower jaw. The shape of the prosthesis may be determined based on the shape and position of each tooth in the occlusal state, or the tooth opposite the missing tooth.

[Functional Configuration of Data Generating Device in Practical Stage]
FIG. 7 is a schematic diagram showing the functional configuration of the data generation device 100 according to this embodiment in the practical stage.

Each time the generative model 114 in the generator 1104 undergoes machine learning through the learning process described with reference to FIG. 4, the generative model 114 can generate prosthesis data for producing a more appropriate prosthesis. When the generation unit 1104 can generate prosthesis data for manufacturing an appropriate prosthesis that meets the criteria, the user will use the data generation device 100 in the practical stage. In other words, when the scanner system 10 equipped with the learned data generation device 100 is put on the market, the prosthesis data for fabricating the prosthesis of the actual patient is obtained by the operator who is the user 1 .

As shown in FIG. 7, since machine learning of the generative model 114 is not required in the practical stage, the data generation device 100 does not need to include the identification unit 1106 involved in the learning process. When the operator uses the three-dimensional scanner 200 to acquire three-dimensional data of the actual patient's oral cavity, the acquired three-dimensional data is input from the input unit 1102 . The generation unit 1104 generates prosthesis data based on the three-dimensional data input from the input unit 1102 and the learned generation model 114 . The prosthesis data generated by the generator 1104 is sent to the dental laboratory or the automatic manufacturing device 600 . Then, based on the prosthesis data, a prosthesis suitable for the defect in the oral cavity of the patient is manufactured. After the prosthesis data is generated, when the user fine-tunes the generated prosthesis data to finally complete the prosthesis, the data generation model 114 is machine-learned using the completed prosthesis data as correct data. You may let

Note that the data generation device 100 may include the identifying unit 1106 even in the practical stage, and may execute learning processing in the practical stage as well. In this way, even after the scanner system 10 equipped with the data generation device 100 is put on the market as a product, the generation model 114 is machine-learned based on the three-dimensional intraoral data of the actual patient. be able to. As a result, the accuracy of the prosthesis data generated by the generation unit 1104 can be improved in the practical stage.

[Overall system configuration]
FIG. 8 is a schematic diagram showing the overall configuration of the system according to this embodiment.

As shown in FIG. 8, the scanner system 10 is arranged in each of a plurality of locals A-C. For example, local A and local B are dental clinics, and in the dental clinic, an operator who is user 1 acquires three-dimensional data including teeth of a patient who is subject 2 using scanner system 10. At the same time, prosthesis data is generated. Local C is a dental college, and at the dental college, a teacher or student who is user 1 acquires intraoral three-dimensional data of a subject who is subject 2 and generates prosthesis data. The intraoral three-dimensional data and the prosthesis data acquired by each of the locals A to C are transmitted to the dental laboratory, which is the local D, via the network 5 .

The intraoral three-dimensional data and the prosthesis data acquired by each of the locals A to C may be output via the network 5 to the server device 500 arranged in the management center. In the management center, the server device 500 accumulates and stores the three-dimensional data and the prosthesis data acquired from each of the locals A to C, and holds them as big data.

Note that the server device 500 is not limited to being arranged in a management center such as a dental clinic, which is different from the local one, and may be arranged locally. For example, the server apparatus 500 may be arranged within any one of the locals A to C. FIG. Also, a plurality of data generation devices 100 may be arranged in one local, and a server device 500 capable of communicating with the plurality of data generation devices 100 may be arranged in the one local. Moreover, the server device 500 may be implemented in the form of a cloud service.

In the dental laboratory, intraoral three-dimensional data and prosthesis data are aggregated from various locations such as local A to C. For this reason, intraoral three-dimensional data and prosthesis data held at the dental laboratory may be transmitted to the management center via the network 5, or may be transmitted to CD (Compact Disc) and USB (Universal Serial) data. Bus) memory or other removable disk 550 to the management center.

The intraoral three-dimensional data and the prosthesis data may also be sent from each of the locals A to C to the management center via the removable disk 550 without going through the network 5 . Also, intraoral three-dimensional data and prosthesis data may be sent to each other via the network 5 or the removable disk 550 between each of the locals A to C.

The generative model 114 held by each local A to C data generation device 100 may be shared among the local A to C data generation devices 100 .

Also, the server device 500 may have the function of prosthetic data generation processing. For example, each of the locals A to C transmits the acquired intraoral three-dimensional data to the server device 500, and the server device 500 receives each of the three-dimensional data received from each of the locals A to C and the generated Prosthesis data in each may be generated based on the model. The server device 500 may then transmit the prosthesis data to each local AC or dental lab. In this way, each of the local A to C data generation devices 100 may share the generation model held by the server device 500 in the form of a cloud service. In this way, each of the locals A to C can cause the server device 500 to generate the prosthesis data simply by transmitting the three-dimensional data to the server device 500 .

Furthermore, each of the local AC data generators 100 may machine-learn the identification model 116 based on the three-dimensional data sent to each other.

The identification model 116 held by each of the local A to C data generation devices 100 may be shared among the local A to C data generation devices 100 .

Also, the server device 500 may have the function of identification processing in the data generation device 100 . For example, each of the locals A to C transmits the generated prosthesis data to the server device 500, and the server device 500 compares the prosthesis data received from each of the locals A to C with the identification model held by itself. Based on this, the identification result of each tooth type may be calculated. Then, server device 500 may transmit the respective identification results to each of locals A to C, and each of locals A to C may feed back the identification results received from server device 500 to generating section 1104 . In this way, each local A to C data generation device 100 may share the identification model held by the server device 500 in the form of a cloud service. In this way, each of the locals A to C can cause the server device 500 to identify whether or not the prosthesis data is appropriate simply by transmitting the prosthesis data to the server device 500 .

[Hardware Configuration of Data Generating Device]
FIG. 9 is a schematic diagram showing the hardware configuration of the data generation device 100 according to this embodiment. Data generation device 100 may be realized by a general-purpose computer, or may be realized by a computer dedicated to scanner system 10, for example.

As shown in FIG. 9, the data generation device 100 includes, as main hardware elements, a scanner interface 102, a display interface 103, a peripheral device interface 105, a network controller 106, a media reader 107, and a PC display 108. , a memory 109 , a storage 110 , and an arithmetic device 130 .

The scanner interface 102 is an interface for connecting the three-dimensional scanner 200 and realizes input/output of data between the data generation device 100 and the three-dimensional scanner 200 .

The display interface 103 is an interface for connecting the display 300 and realizes input/output of data between the data generation device 100 and the display 300 . Display 300 is configured by, for example, an LCD (Liquid Crystal Display) or an organic ELD (Electroluminescence Display).

The peripheral device interface 105 is an interface for connecting peripheral devices such as the keyboard 601 and the mouse 602, and realizes input/output of data between the data generation device 100 and the peripheral devices.

The network controller 106 controls, via the network 5, the equipment located at the dental laboratory, the server equipment 500 located at the management center, the automated manufacturing equipment 600, and other locally located data generation equipment 100. Send and receive data to and from each. The network controller 106 supports arbitrary communication methods such as Ethernet (registered trademark), wireless LAN (Local Area Network), and Bluetooth (registered trademark).

The media reader 107 writes and reads various data such as three-dimensional data and prosthesis data to and from the removable disk 550 .

The PC display 108 is a dedicated display for the data generation device 100 . PC display 108 is configured by, for example, an LCD or an organic EL display. Although PC display 108 is separate from display 300 in the present embodiment, it may be shared with display 300 .

The memory 109 provides a storage area for temporarily storing program codes, work memory, etc. when the arithmetic unit 130 executes an arbitrary program. The memory 109 is composed of, for example, a volatile memory device such as a DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory).

The storage 110 provides storage areas for storing various data necessary for identification processing, learning processing, prosthesis data generation processing, and the like. The storage 110 is composed of a non-volatile memory device such as a hard disk or SSD (Solid State Drive), for example.

Storage 110 contains scan information 112, generation model 114, identification model 116, generation condition data 119, identification program 120, learning program 121, data generation program 125, and OS (Operating System) 127. and store

The scan information 112 includes intraoral three-dimensional data 122 acquired by the three-dimensional scanner 200, identification results 124 by identification processing executed based on the three-dimensional data 122, and the prosthesis generated by the prosthesis data generation processing. object data 123; The identification result 124 and the prosthesis data 123 are stored in the storage 110 in association with the three-dimensional data 122 .

The identification program 120 is a program for executing identification processing. The learning program 121 is a program for executing learning processing of the generative model 114 . The data generation program 125 is a program for executing prosthesis data generation processing.

The generation condition data 119 is data referred to when generating prosthesis data in the prosthesis data generation process, and is adjacent to the defect location specified based on the intraoral three-dimensional data acquired by the three-dimensional scanner 200 . It includes data for specifying conditions for determining the shape of the prosthesis based on the shape of the adjacent tooth that fits, the opposing tooth that faces the defect site, or the opposing tooth and the neighboring tooth. For example, as described with reference to FIGS. 5 and 6, the generation condition data 119 includes the conditions for defining the spatial grid (three-dimensional grid), the adjacent teeth, the opposed teeth, and the teeth adjacent to the opposed teeth. Including conditions for specifying the shape, conditions for determining the shape of the prosthesis, and the like. The data to be referred to may include data for specifying the margin of the missing tooth.

The arithmetic device 130 is an arithmetic entity that executes various types of processing such as identification processing, learning processing, and prosthesis data generation processing by executing various programs, and is an example of a computer. Arithmetic device 130 includes, for example, a CPU (Central Processing Unit) 132, an FPGA (Field-Programmable Gate Array) 134, a GPU (Graphics Processing Unit) 136, and the like.

[Flowchart of learning process]
FIG. 10 is a flowchart for explaining the learning process executed by data generation device 100 according to the present embodiment. Each step shown in FIG. 10 is realized by executing the OS 127, the identification program 120, the learning program 121, the data generation program 125, and the like by the arithmetic device 130 of the data generation device 100. FIG.

As shown in FIG. 10, the data generation device 100 determines whether or not the conditions for starting the learning process are satisfied (S1). The start condition may be established, for example, when some action for executing the learning process is performed in data generation device 100 . For example, the starting condition may be that the learning start icon of the learning application has been operated by the user.

The data generation device 100 terminates this process when the start condition is not satisfied (NO in S1). On the other hand, when the start condition is satisfied (YES in S1), the data generation device 100 determines whether three-dimensional data has been input (S2). For example, the data generation device 100 determines whether or not a sufficient amount of three-dimensional data has been input to generate prosthesis data. If a sufficient amount of three-dimensional data has not been input (NO in S2), the data generation device 100 repeats the process of S2.

On the other hand, when a sufficient amount of three-dimensional data is input (YES in S2), the data generation device 100 reads the generation condition data 119 (S3). After that, the data generation device 100 generates prosthesis data according to the generation conditions specified by the generation condition data 119 based on the input three-dimensional data and the generation model 114 (S4).

Next, the data generation device 100 estimates the type of tooth of the prosthesis to be produced based on the generated prosthesis data based on the machine-learned identification model 116 (S5). Next, the data generation device 100 compares the estimation result of the prosthesis tooth type obtained in S5 with the missing tooth type input as the correct data (S6).

The data generation device 100 determines whether or not the estimation result of the tooth type of the prosthesis obtained in S5 matches the type of missing tooth input as the correct data (S7). If the estimation result of the prosthesis tooth type obtained in S5 matches the missing tooth type input as the correct data (YES in S7), the data generation device 100 ends this process.

On the other hand, the data generation device 100 adjusts the generative model 114 when the estimation result of the tooth type of the prosthesis obtained in S5 does not match the type of missing tooth input as correct data (NO in S7). (S8). For example, data generator 100 optimizes generative model 114 by adjusting neural networks or parameters included in generative model 114 . After that, the data generation device 100 returns to S4.

[Flowchart of prosthesis data generation processing]
FIG. 11 is a flowchart for explaining prosthesis data generation processing executed by the data generation device 100 according to the present embodiment. Each step shown in FIG. 11 is realized by the arithmetic device 130 of the data generation device 100 executing the OS 127, the identification program 120, and the data generation program 125. FIG.

As shown in FIG. 11, the data generation device 100 determines whether or not a condition for starting the prosthesis data generation process is satisfied (S11). The start condition may be established, for example, when some action for executing prosthesis data generation processing is performed in the data generation device 100 . Specifically, the start condition may be satisfied when the power of the three-dimensional scanner 200 is turned on, or the mode corresponding to the prosthesis data generation process may be set after the power of the three-dimensional scanner 200 is turned on. may be established when Alternatively, the start condition may be satisfied when the start switch is operated after the icon corresponding to the prosthesis data generation process (for example, the AI assist icon) is operated and the icon flashes.

If the start condition is not met (NO in S11), the data generation device 100 ends this process. On the other hand, when the start condition is satisfied (YES in S11), the data generation device 100 determines whether three-dimensional data has been input (S12). For example, the data generation device 100 determines whether or not a sufficient amount of three-dimensional data has been input to generate prosthesis data. If a sufficient amount of three-dimensional data has not been input (NO in S12), the data generation device 100 repeats the process of S12.

On the other hand, when a sufficient amount of three-dimensional data is input (YES in S12), the data generation device 100 reads the generation condition data 119 (S13). After that, the data generation device 100 generates prosthesis data according to the generation conditions specified by the generation condition data 119 based on the input three-dimensional data and the generation model 114 (S14).

Next, the data generation device 100 outputs the generated prosthesis data to the dental laboratory, the automatic manufacturing device 600, or the server device 500 (S15). After that, the data generation device 100 terminates this process.

[Main configuration]
As described above, the present embodiment includes the following disclosures.

The data generating apparatus 100 includes an input unit 1102 to which three-dimensional data including at least the missing tooth, which is a missing tooth, is input, and based on the three-dimensional data and the generation model 114 input from the input unit 1102, the missing part of the missing tooth. and a generator 1104 for generating prosthesis data for fabricating a prosthesis that conforms to. The generative model 114 is machine-learned based on the identification result of whether or not the prosthesis data generated by the generator 1104 is appropriate.

As a result, the data generation device 100 generates more appropriate prosthesis data by machine-learning the generation model 114 using the identification result as to whether or not the prosthesis data generated based on the three-dimensional data is appropriate. be able to Therefore, by using the data generation device 100, the user 1 does not need to digitally design prosthesis data by himself/herself, and can more easily obtain an appropriate prosthesis.

The data generation device 100 includes an identification unit 1106 that identifies whether the prosthesis data generated by the generation unit 1104 is appropriate. The identification unit 1106 estimates the type of tooth corresponding to the prosthesis data generated by the generation unit 1104, and generates the prosthesis data based on the estimation result of the type of the tooth and the type of the tooth corresponding to the missing portion. is appropriate or not.

As a result, the data generation device 100 determines whether or not the prosthesis data is appropriate based on the result of estimating the type of tooth corresponding to the prosthesis data generated based on the three-dimensional data and the type of tooth corresponding to the missing portion. By identifying , the generative model 114 can be machine-learned, so that it is possible to generate appropriate prosthesis data suitable for the type of missing tooth. Therefore, by using the data generation device 100, the user 1 does not need to specify the type of tooth corresponding to the missing portion by himself/herself, and can more easily obtain an appropriate prosthesis.

The identification unit 1106 estimates the type of tooth corresponding to the prosthesis data generated by the generation unit 1104 and the identification model 116, and the identification model 116 uses three-dimensional data including at least the tooth. Machine learning is performed on the basis of the estimation result of the type of tooth that was used and the type of tooth corresponding to the three-dimensional data.

As a result, the data generation device 100 can machine-learn the identification model 116 based on the tooth type estimation result using the three-dimensional data and the tooth type corresponding to the three-dimensional data. The accuracy of machine learning of the generative model 114 using the model 116 can be further improved.

The three-dimensional data input from the input unit 1102 includes the tooth adjacent to the missing portion, the tooth facing the missing portion, the tooth adjacent to the tooth facing the missing portion, and the tooth row of the upper jaw and the tooth row of the lower jaw. It further includes three-dimensional data of at least one of each tooth in the state and the tooth on the opposite side of the missing tooth corresponding to the missing part.

As a result, the data generation device 100 can generate at least one of the adjacent teeth, the opposing teeth, each tooth in a state where the upper and lower teeth are in mesh, and the tooth on the opposite side of the missing tooth corresponding to the missing part. Based on one three-dimensional data, the shape and position of these teeth can be considered to determine the shape of the prosthesis. Therefore, the user 1 can obtain a more suitable prosthesis based on the shape and position of the teeth located around the missing site.

The input unit 1102 receives margin position data for the missing tooth. As a result, the data generation device 100 can determine the shape of the prosthesis in consideration of the margin of the missing tooth, so that the user 1 can obtain a more appropriate prosthesis.

The generation unit 1104 generates the prosthesis data based on the three-dimensional data input from the input unit 1102 and the generation model 114 according to predetermined generation conditions. Furthermore, the predetermined generation condition includes a condition for determining the shape of the prosthesis based on the shape of the tooth adjacent to or facing the missing portion specified based on the three-dimensional data input from the input unit.

As a result, the data generation device 100 generates prosthesis data according to the conditions for determining the shape of the prosthesis based on the shape of the tooth adjacent to or facing the missing portion specified based on the input three-dimensional data. Therefore, it is possible to generate more efficient and appropriate prosthesis data than determining the shape of the prosthesis without following predetermined conditions.

The three-dimensional data input from the input unit 1102 is three-dimensional data of a row of teeth including at least a missing tooth, a tooth adjacent to the missing tooth, and a tooth facing the missing tooth. Prosthesis data is generated based on a spatial grid that defines the three-dimensional positions of the missing tooth, the tooth adjacent to the missing tooth, and the tooth facing the missing tooth in the three-dimensional space specified by the three-dimensional data.

As a result, the data generating apparatus 100 can generate prosthesis data based on the spatial grid that defines the three-dimensional positions of the missing tooth and the tooth adjacent to the missing tooth in the three-dimensional space. It is possible to determine the optimal prosthesis shape by specifying the positional relationship based on the points of contact between the tooth) and its surrounding teeth.

The scanner system 10 includes a three-dimensional scanner 200 that obtains three-dimensional data including at least a missing tooth that is a missing tooth using a three-dimensional camera, and a missing tooth based on the three-dimensional data acquired by the three-dimensional scanner 200. and a data generation device 100 for generating prosthesis data for producing a prosthesis that fits the defect site. The data generating apparatus 100 includes an input unit 1102 to which three-dimensional data is input, and prosthetic data for manufacturing a prosthesis suitable for the defect based on the three-dimensional data and the generation model 114 input from the input unit 1102. and a generation unit 1104 that generates the . The generative model 114 is machine-learned based on the identification result of whether or not the prosthesis data generated by the generator 1104 is appropriate.

As a result, the data generation device 100 of the scanner system 10 machine-learns the generation model 114 using the identification result as to whether or not the prosthesis data generated based on the three-dimensional data is appropriate, thereby providing a more appropriate prosthesis. data can be generated. Therefore, by using the data generation device 100 of the scanner system 10, the user 1 does not need to generate prosthesis data by himself and can more easily obtain an appropriate prosthesis.

The data generation method includes a step of inputting three-dimensional data including at least the missing tooth (S12), and a prosthesis that fits the missing part of the missing tooth based on the three-dimensional data and the generation model 114. generating (S14) prosthesis data for performing. The generative model 114 is machine-learned based on the identification result of whether the prosthesis data generated by the generating step is appropriate.

As a result, according to the data generation method, the generation model 114 is machine-learned using the identification result as to whether or not the prosthesis data generated based on the three-dimensional data is appropriate, thereby generating more appropriate prosthesis data. be able to Therefore, by using the data generation method, the user 1 does not need to generate prosthesis data by himself and can more easily obtain an appropriate prosthesis.

The data generation program 125 performs a step (S12) in which three-dimensional data including at least a missing tooth that is a missing tooth is input to the computer (arithmetic device) 130, and based on the three-dimensional data and the generation model 114, the missing tooth (S14) of generating prosthesis data for fabricating a prosthesis that fits the missing part of the body. The generative model 114 is machine-learned based on the identification result of whether the prosthesis data generated by the generating step is appropriate.

As a result, according to the data generation program 125, the generation model 114 is machine-learned using the identification result as to whether or not the prosthesis data generated based on the three-dimensional data is appropriate, thereby generating more appropriate prosthesis data. will be able to generate Therefore, by using the data generation method, the user 1 does not need to generate prosthesis data by himself and can more easily obtain an appropriate prosthesis.

[Modification]
The present invention is not limited to the above embodiments, and various modifications and applications are possible. Modifications applicable to the present invention will be described below.

(learning process)
As shown in FIG. 4, the data generating apparatus 100 according to the present embodiment receives three-dimensional data including at least a missing tooth (missing portion), and generates a prosthesis that compensates for the missing tooth based on the input three-dimensional data. It was configured to generate prosthesis data for fabrication. At this time, the data generating apparatus 100 determines the shape of the prosthesis so as to match the shape and position of surrounding teeth such as adjacent teeth and opposing teeth, as shown in FIGS.

In other words, the data generation device 100 according to the present embodiment performs machine learning on the shape of the prosthesis without correct data. Here, FIG. 12 is a schematic diagram showing the functional configuration in the learning stage of the data generation device according to the modification. As shown in FIG. 12, the data generation device 100a according to the modification regards an arbitrary tooth whose shape has been determined in advance as a missing tooth, and based on the three-dimensional data of the row of teeth including teeth other than the arbitrary tooth. , prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth may be generated. Furthermore, the generative model 114a may be machine-learned using three-dimensional data of a row of teeth that does not exclude any teeth as correct data.

Specifically, as shown in FIG. 12, in the data generation device 100a, the input unit 1102a receives, as three-dimensional data, three-dimensional data of a row of teeth excluding an arbitrary tooth and including teeth other than the arbitrary tooth. is entered. The generation unit 1104a generates prosthesis data for manufacturing a prosthesis that fits any removed tooth based on the three-dimensional data and the generation model 114a input from the input unit 1102a. The three-dimensional data input to the input unit 1102a is not limited to data excluding any tooth, and may be data designating any tooth. In this case, the generation unit 1104a generates prosthesis data based on the tooth adjacent to the designated tooth, the opposite tooth opposite to the arbitrary tooth, or the tooth adjacent to the opposite tooth. In other words, when an arbitrary tooth is specified, the generation unit 1104a does not consider the designated arbitrary tooth, but considers the surrounding teeth to generate prosthesis data for the arbitrary tooth.

Based on the prosthesis data generated by the generation unit 1104a and the correct data input by the user 1, the identification unit 1106a identifies whether the prosthesis data is appropriate. Specifically, the identification unit 1106a identifies whether or not the shape of the prosthesis manufactured based on the prosthesis data generated by the generation unit 1104a matches the shape of any tooth input as the correct data. do.

The identification result obtained by the identification unit 1106a is fed back to the generation unit 1104a. The generative model 114a is machine-learned based on the identification results fed back from the identification unit 1106a.

For example, generative model 114a includes a neural network (not shown) and parameters used by the neural network. In machine learning, the generative model 114a does not update the parameters if it determines that the shape of the prosthesis corresponding to the prosthesis data generated by itself matches the shape of an arbitrary tooth that is the correct data based on the identification result. If it is determined that they do not match, the parameters are optimized by updating the parameters so that they match. In this way, the generative model 114a is machine-learned by optimizing the parameters. Note that in the generative model 114a, the parameters are not limited to being updated, and a neural network (for example, a neural network algorithm) may be updated.

In the data generation device 100a, by sequentially changing arbitrary teeth as three-dimensional data input to the input unit 1102a, the generation unit 1104a generates prosthesis data for each tooth included in the tooth row. The identification of the prosthesis data by 1106a and the machine learning of the generative model 114a based on the identification results fed back by the identification unit 1106a are repeated. As a result, with respect to the shape of each tooth included in the tooth row, the generative model 114a performs machine learning on the shape of each tooth while considering the shape of adjacent teeth adjacent to each tooth included in the tooth row. can do.

Thus, the data generation device 100a includes an identification unit 1106a that identifies whether or not the prosthesis data generated by the generation unit 1104a is appropriate. In the learning stage of the generative model 114a, the generating unit 1104a generates prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the tooth row excluding the arbitrary tooth and the generative model 114a. do. The identification unit 1106a identifies whether or not the prosthesis data is appropriate based on the prosthesis data generated by the generation unit 1104a and the three-dimensional data of an arbitrary tooth.

As a result, the data generation device 100a generates a prosthesis based on the shape of the prosthesis corresponding to the prosthesis data generated based on the three-dimensional data of the row of teeth excluding an arbitrary tooth and the shape of the arbitrary tooth, which is the correct data. By identifying whether the object data is appropriate or not, the generative model 114a can be machine-learned. can be improved. Therefore, the user 1 can more efficiently machine-learn the generative model 114a based on the shape of the tooth whose shape has been determined in advance.

In addition, the generation unit 1104a generates prosthesis data for each tooth by sequentially changing an arbitrary tooth in the tooth row. The identification unit 1106a identifies whether or not the prosthesis data generated by the generation unit 1104a is appropriate for each tooth.

As a result, the data generation device 100a can machine-learn the generative model 114a for the shape of each tooth included in the dentition, so that it is possible to generate appropriate prosthesis data adapted to the shape of each tooth. As such, its production capacity can be improved.

Furthermore, by preparing a large number of such dentition samples and performing machine learning as shown in FIG. 12 on each sample, the data generation device 100a can generate more appropriate prosthesis data.

(input data)
In the data generation device 100, the attribute data of the subject 2 who is the owner of the dentition may be input together with the three-dimensional data of the dentition. The attribute data includes attribute information (profile) regarding the subject 2, such as age, sex, race, height, weight, and place of residence. Furthermore, the data generation device 100 may generate prosthesis data in consideration of attribute information (profile) regarding the subject 2 .

In general, tooth shape differs depending on genetics or living environment such as age, sex, race, height, weight, place of residence, and the like. For example, the permanent teeth of adults are generally larger than the milk teeth of children, and the two have different shapes. In general, male teeth are larger than female teeth, and the two have different shapes. In general, the teeth of Westerners tend to have sharp tips so that they can easily bite off hard meat and bread, whereas the teeth of Japanese people tend to have sharp tips so that they can easily grind soft rice and vegetables. It tends to be smooth. For this reason, if the generative model 114 is machine-learned based on the attribute information (profile), a prosthesis for producing a prosthesis suitable for the attribute information (profile) regarding the subject 2 in consideration of heredity, living environment, etc. data can be generated.

Further, in the data generation device 100, along with the three-dimensional data of the row of teeth, motion data relating to the occlusal motion of at least one of adjacent teeth and opposing teeth may be input. The bite movement includes, for example, vertical movement of the mandible for crushing food, back and forth movement of the mandible for crushing food, and the like. Furthermore, the data generation device 100 may generate prosthesis data in consideration of occlusal motion in at least one of adjacent teeth and opposing teeth.

In this way, the input unit 1102 may receive motion data of the mandible when the mandible moves. In this way, the shape of the prosthesis can be determined in consideration of the movement data of the mandible, so the user 1 can obtain a more suitable prosthesis.

It should be considered that the embodiments disclosed this time are illustrative in all respects and not restrictive. The scope of the present invention is indicated by the scope of the claims rather than the above description, and is intended to include all modifications within the scope and meaning equivalent to the scope of the claims. Note that the configurations exemplified in this embodiment and the configurations exemplified in the modifications can be combined as appropriate.

1 user, 2 subject, 5 network, 10 scanner system, 100, 100a data generator, 102 scanner interface, 103 display interface, 105 peripheral device interface, 106 network controller, 107 media reader, 108, 300 display, 109 memory , 110 storage, 112 scan information, 114, 114a generation model, 116 identification model, 119 generation condition data, 120 identification program, 121 learning program, 122 three-dimensional data, 123 prosthesis data, 124 identification result, 125 data generation program, 130 computing device, 200 three-dimensional scanner, 500 server device, 550 removable disk, 600 automatic manufacturing device, 601 keyboard, 602 mouse, 1102, 1102a input section, 1104, 1104a generation section, 1106, 1106a identification section.

Claims (11)

A data generating device for generating prosthetic data for manufacturing a dental prosthesis,
an input unit into which three-dimensional data including at least a missing tooth that is a missing tooth is input;
a generation unit for generating prosthesis data for manufacturing a prosthesis that fits the missing part of the missing tooth based on the three-dimensional data and the generation model input from the input unit;
an identification unit that identifies whether the prosthesis data generated by the generation unit is appropriate;
In the learning stage of the generative model,
The generation unit generates the prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the tooth row including the tooth other than the arbitrary tooth and the generation model,
The identification unit estimates a tooth type corresponding to the prosthesis data generated by the generation unit, and based on the estimation result of the tooth type and the arbitrary tooth type, the prosthesis data is identify whether it is appropriate or not;
The data generating device, wherein the generative model is machine-learned based on the identification result of the identification unit. The identification unit estimates the type of tooth corresponding to the prosthesis data based on the prosthesis data and the identification model generated by the generation unit,
2. The data generation device according to claim 1, wherein the identification model is machine-learned based on a tooth type estimation result using three-dimensional data including at least teeth and a tooth type corresponding to the three-dimensional data. . The generation unit generates the prosthesis data for each tooth by sequentially changing the arbitrary tooth in the tooth row,
3. The data generating apparatus according to claim 1, wherein the identification unit identifies whether the prosthesis data generated by the generation unit is appropriate for each tooth. The three-dimensional data input from the input unit includes the tooth adjacent to the missing portion, the tooth facing the missing portion, the tooth adjacent to the tooth facing the missing portion, the upper jaw row and the lower jaw row of teeth. further comprising three-dimensional data of at least one of each tooth in an engaged state and the tooth on the opposite side of the missing tooth corresponding to the missing part, according to any one of claims 1 to 3 data generator. Claims 1 to 3, wherein at least one of position data of the margin of the abutment tooth that is the missing tooth and motion data of the mandible when the mandible moves is input to the input unit. 5. The data generating device according to any one of 4. The generating unit according to any one of claims 1 to 5, wherein the generating unit generates the prosthesis data based on the three-dimensional data input from the input unit and the generating model according to predetermined generating conditions. data generator. wherein the predetermined generation conditions include conditions for determining the shape of the prosthesis based on the shape of teeth adjacent to or facing the defect site identified based on the three-dimensional data input from the input unit. Item 7. The data generation device according to item 6. The three-dimensional data input from the input unit is three-dimensional data of a row of teeth including at least the missing tooth, a tooth adjacent to the missing tooth, and a tooth facing the missing tooth,
The generation unit generates a spatial grid that defines three-dimensional positions of the missing tooth, a tooth adjacent to the missing tooth, and a tooth facing the missing tooth in a three-dimensional space specified by the three-dimensional data of the tooth row. The data generation device according to any one of claims 1 to 7, which generates the prosthesis data based on the data. A scanner system for acquiring tooth shape information,
a three-dimensional scanner that acquires three-dimensional data including at least missing teeth that are missing teeth using a three-dimensional camera;
a data generating device for generating prosthesis data for manufacturing a prosthesis that fits the missing part of the missing tooth based on the three-dimensional data acquired by the three-dimensional scanner;
The data generation device is
an input unit to which the three-dimensional data is input;
a generation unit that generates prosthesis data for manufacturing a prosthesis that fits the defect site based on the three-dimensional data and the generation model input from the input unit;
an identification unit that identifies whether the prosthesis data generated by the generation unit is appropriate;
In the learning stage of the generative model,
The generation unit generates the prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the tooth row including the tooth other than the arbitrary tooth and the generation model,
The identification unit estimates a tooth type corresponding to the prosthesis data generated by the generation unit, and based on the estimation result of the tooth type and the arbitrary tooth type, the prosthesis data is identify whether it is appropriate or not;
The scanner system, wherein the generative model is machine-learned based on the identification result of the identification unit. A data generation method for generating prosthetic data for manufacturing a dental prosthesis by computer, comprising:
The data generation method includes, as processing executed by the computer,
a step of inputting three-dimensional data including at least a missing tooth that is a missing tooth;
generating prosthesis data for manufacturing a prosthesis that fits the missing part of the missing tooth based on the three-dimensional data and the generated model;
identifying whether the prosthetic data generated by the generating step is adequate;
In the learning stage of the generative model,
The step of generating generates the prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the tooth row including the tooth other than the arbitrary tooth and the generation model,
The identifying step includes estimating a tooth type corresponding to the prosthesis data generated by the generating step, and based on the estimation result of the tooth type and the arbitrary tooth type, the prosthesis identify whether the data is appropriate or not;
The data generation method, wherein the generative model is machine-learned based on the identification result of the identifying step. A data generation program for generating prosthesis data for manufacturing a dental prosthesis,
The data generation program is a computer,
a step of inputting three-dimensional data including at least a missing tooth that is a missing tooth;
generating prosthesis data for manufacturing a prosthesis that fits the missing part of the missing tooth based on the three-dimensional data and the generated model;
identifying whether the prosthesis data generated by the generating step is adequate;
In the learning stage of the generative model,
The step of generating generates the prosthesis data for manufacturing a prosthesis corresponding to the arbitrary tooth based on the three-dimensional data of the tooth row including the tooth other than the arbitrary tooth and the generation model,
The identifying step includes estimating a tooth type corresponding to the prosthesis data generated by the generating step, and based on the estimation result of the tooth type and the arbitrary tooth type, the prosthesis identify whether the data is appropriate or not;
A program for data generation, wherein the generative model is machine-learned based on the identification result of the identifying step.

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