JP2021076879A - Dental diagnosis program and system - Google Patents

Abstract

To precisely and automatically determine the state of a tooth with less human help.SOLUTION: A dental diagnosis program for determining the state of teeth in a dental diagnosis causes a computer to perform an information acquisition step of acquiring image information on an image of a determination target tooth; and a determination step of determining the state of the tooth on the basis of reference image information corresponding to image information acquired in the information acquisition step by using at least 3-stages relevance between reference image information of a tooth imaged in the past and the state of the teeth.SELECTED DRAWING: Figure 3

Description

The present invention relates to a dental diagnosis program and system for determining a tooth condition in a dental diagnosis.

While dental practice performed by a dentist is a highly specialized task, there are cases in which the captured images (CT images, X-ray images, spectral images) are used to overlook caries (cavities). In such a case, if there is a dental diagnosis AI that the dentist can refer to when performing medical treatment, the accuracy of the diagnosis can be further improved, but such a dental diagnosis AI has not been put into practical use. Was the current situation. Also, if a patient who does not have time to go to the dentist can make a dental diagnosis automatically to some extent just by taking an image by himself / herself at home, perform a self-diagnosis before having the dentist make a diagnosis. However, the urgency of going to a dental clinic can be known in advance, but the current situation is that such a dental diagnosis AI has not been put into practical use.
For this reason, with regard to dental diagnosis, there has been an increasing social demand for improving the accuracy of detection by obtaining assistance from artificial intelligence and allowing patients to perform self-diagnosis to some extent in advance.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide a dental diagnosis program and system capable of automatically discriminating a dental diagnosis with high accuracy. There is.

The dental diagnosis program according to the present invention is a dental diagnosis program for discriminating a tooth condition in a dental diagnosis, in which an information acquisition step of acquiring image information obtained by imaging a tooth to be discriminated and a reference image information of a tooth imaged in the past are provided. And, using the degree of association with the tooth condition in three or more stages, the computer executes a determination step of determining the tooth condition based on the reference image information corresponding to the image information acquired in the above information acquisition step. It is characterized by letting it.

Even if you do not have any special skills or experience, you can automatically determine the state of dental caries by automatically performing a dental diagnosis. As a result, the dentist can refer to the discrimination result when performing medical treatment, and the accuracy of the diagnosis can be improved. Patients who do not have time to go to the dentist can have AI automatically perform a dental diagnosis to some extent just by taking an image at home, so it is possible to perform a self-diagnosis before having a dentist diagnose it. You can know the urgency of going to the dentist's office in advance.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the dental diagnosis program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a dental diagnosis system 1 in which a dental diagnosis program to which the present invention is applied is implemented. The dental diagnosis system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent. The information acquisition unit 9 may be composed of a CT camera, a camera that captures an X-ray image, a spectrum image, or the like.

Database 3 stores various information necessary for performing dental diagnosis. Information necessary for performing a dental diagnosis includes reference image information of a tooth imaged in the past, reference treatment history information regarding a past treatment history of a tooth imaged in order to obtain the reference image information, and past treatment. Reference filling information regarding the above-mentioned tooth filling acquired from the history, reference subjective symptom information regarding the subjective symptom of the tooth by a past patient, etc. are accumulated as output data in relation to the condition of the tooth to be diagnosed. There is.

That is, in the database 3, any one or more of such reference image information, reference treatment history information, reference filling information, and reference subjective symptom information and the tooth condition are stored in association with each other. ..

The discrimination device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the discrimination device 2.

FIG. 2 shows a specific configuration example of the discrimination device 2. The discrimination device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire discrimination device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, a determination unit 27 for making various determinations, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the discrimination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the determination unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The determination unit 27 determines the search solution. The discriminating unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the discriminating operation. The discriminating unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation in the dental diagnosis system 1 having the above-described configuration will be described.

In the dental diagnosis system 1, for example, as shown in FIG. 3, it is premised that three or more levels of association between the reference image information and the tooth condition are set in advance. The reference image information is information obtained by imaging an image of a tooth, and also includes information obtained for the first time by analyzing this image information. This image may be a moving image as well as a still image. This image may be an image taken by a camera with the entire tooth applied to the target, or may be an image taken by the camera with the target applied to each tooth. This reference image information may be provided by a CT image, an X-ray image, or a spectral image.

The condition of the teeth may indicate the potential risk of caries, even if it has not occurred. Alternatively, it may be displayed in two simple stages of "with signs of caries" and "no signs of caries". In addition, the condition of the tooth may indicate the determination result such as whether it is healthy, caries, alveolar pyorrhea, periodontal disease, or simply accumulated dirt, or caries. If so, the level (C1 to C4) may be specifically determined. Further, the tooth condition may be quantitatively expressed by a frequency called the degree of health of the tooth, and may be 100% if it is healthy and close to 0% if it becomes unhealthy.
Such a tooth condition may be acquired from past data accumulated in a dental clinic or the like. At this time, it is a prerequisite to acquire data associated with each other between the state of the tooth and the image actually captured for the tooth.

Furthermore, the condition of the tooth may be judged based on the previous experience of the dentist, or the material of the past medical case is actually read, and multiple dentists evaluate the level and they are evaluated. May be statistically analyzed to determine the condition of the teeth.

In the example of FIG. 3, it is assumed that the input data is, for example, reference image information P01 to P03. The reference image information P01 to P03 as such input data is connected to the state of the tooth as an output. In this output, specific examples of tooth states A to D as output solutions are shown. This tooth condition may be displayed as a percentage depending on the height of the signs of caries.

The reference image information is associated with each other through the degree of association of three or more levels with respect to the tooth states A to D as the output solution. The reference image information is arranged on the left side via this degree of association, and the state of each tooth is arranged on the right side via this degree of association. The degree of association indicates the degree of which tooth condition is highly relevant to the reference image information arranged on the left side. In other words, this degree of association is an index indicating what kind of tooth condition each reference image information is likely to be associated with, and is used to select the most probable tooth condition from the reference image information. It shows the accuracy in. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the state of the tooth as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates a past data set as to which of the reference image information and the tooth condition in that case is adopted and evaluated in discriminating the actual search solution, and stores these. By analyzing and analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the tooth condition A (health) is often evaluated as the tooth condition with respect to the reference image information captured on the tooth in the past. By collecting and analyzing such a data set, the degree of association with the reference image information is strengthened.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference image information P01, analysis is performed from various data as a result of evaluating the past tooth condition. In the case of reference image information P01, if there are many cases of tooth condition A (health), the degree of association that leads to the evaluation of this tooth condition is set higher, and tooth condition B (that is due to caries). When there are many cases where the level is C2), the degree of association that leads to the evaluation of the tooth condition is set higher. For example, in the example of the reference image information P01, the tooth condition A and the tooth condition C (alveolar pyorrhea) are linked, but from the previous case, the degree of association of w13 connected to the tooth condition A is reduced to 7 points. , The degree of association of w14 connected to the tooth condition C is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.
In such a case, as shown in FIG. 4, reference image information is input as input data, the state of teeth is output as output data, and at least one or more hidden layers are provided between the input node and the output node. , Machine learning may be made. The above-mentioned degree of association is set in either one or both of the input node and the hidden layer node, and this is the weighting of each node, and the output is selected based on this. Then, when the degree of association exceeds a certain threshold value, the output may be selected.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data through the data set of the image of the tooth to be evaluated before and the tooth condition actually discriminated and evaluated, the tooth condition is actually newly discriminated from now on. Above, the state of the tooth will be searched using the learned data described above. In such a case, the image information obtained by actually capturing the image of the tooth in the region to be discriminated is newly acquired. The newly acquired image information is input by the information acquisition unit 9 described above. The image information is acquired by imaging the appearance of the tooth, or, in the case of an X-ray image or CT image, by sound that images the inside of the tooth or the inside of the filling. The acquisition of the image information may be performed by the same method as the reference image information described above. Incidentally, as for the image information, the dentist may take an image of the tooth of his / her patient with a small camera, or the patient himself / herself may take an image of the tooth at the diseased part or the entire tooth with a camera.

Based on the image information newly acquired in this way, the state of the tooth is determined. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02, the tooth state B is associated with w15 and the tooth state C is associated with the association degree w16 through the degree of association. In such a case, the tooth state B having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the tooth state C in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the most suitable tooth condition from the newly acquired image information and display it to the user. By looking at the results of this search, it is possible to determine the signs and conditions of caries on the teeth, alveolar pyorrhea and dirt, the condition of dentures, etc. Can be done.

In the example of FIG. 5, the degree of association between the reference image information and the reference treatment history information is formed. The reference treatment history information is related to the past treatment history of the tooth imaged in order to obtain the reference image information. The past treatment history includes whether or not the tooth has been treated by caries before, and if the tooth has been treated, the level of the caries and the specific content of the treatment for it. This reference treatment history information includes whether or not the caries is filled with padding, whether or not the nerve is pulled out and covered with a covering such as gold teeth, and whether or not the extracted tooth or denture is used. Also includes information on. For example, caries may erode from the inside of the stuffing or covering, and in such a case, it is difficult to make a visual judgment from the outside of the tooth or an image capture. In such a case, it is possible to improve the discrimination accuracy by combining the X-ray image and the CT image and combining the information with the padding and the covering. Therefore, in addition to the reference image information, the reference treatment history information is combined to form the above-mentioned degree of association.

In the example of FIG. 5, it is assumed that the input data is, for example, reference image information P01 to P03 and reference treatment history information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference image information and reference treatment history information as such input data. Each intermediate node is further linked to the output. In this output, the state of the tooth as an output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference treatment history information is associated with each other through three or more levels of association with the tooth condition as this output solution. The reference image information and the reference treatment history information are arranged on the left side through this degree of association, and the tooth condition is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the tooth condition with respect to the reference image information and the reference treatment history information arranged on the left side. In other words, this degree of association is an index indicating what kind of tooth condition each reference image information and reference treatment history information is likely to be associated with, and is a reference image information and reference treatment history. It shows the accuracy in selecting the most probable tooth condition from the information. Therefore, the optimum tooth condition is searched for by combining the reference image information and the reference treatment history information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference image information, the reference treatment history information, and the tooth condition in that case is suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

For example, it is assumed that the reference image information in the actual case in the past is the image data α. Further, it is assumed that the reference treatment history information is (in the past, suffered from C3 caries, and the nerve was pulled out and covered with a covering). In such a case, the tooth condition indicating how much the tooth condition was actually is learned as a data set and defined in the form of the above-mentioned degree of association. In addition, such reference image information and reference treatment history information may be extracted from a database such as a patient's medical record managed by a dental clinic.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference image information P01 is the reference treatment history information P16, the state of the tooth is analyzed from the past data. When there are many cases of tooth condition A, the degree of association leading to this tooth condition A is set higher, and when there are many cases of tooth condition B and there are few cases of tooth condition A, the tooth The degree of association leading to the state B of the tooth is set high, and the degree of association leading to the state A of the tooth is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the tooth state A and the quality B, but from the previous case, the degree of association of w13 connected to the tooth state A is set to 7 points, and w14 connected to the tooth state B. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference treatment history information P14 is combined with the reference image information P01, the degree of association of the tooth state C is w15, and the tooth state E. The degree of association is w16. The node 61c is a node that is a combination of the reference treatment history information P15 and P17 with respect to the reference image information P02, and the degree of association of the tooth state B is w17 and the degree of association of the tooth state D is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually determining the tooth condition from now on, the above-mentioned learned data will be used. In such a case, the image information of the tooth for which the state of the tooth is to be actually determined and the treatment history information of the tooth are input or selected. Dental treatment history information may be manually entered by the dentist from the patient's chart or extracted from the database. Patients may be allowed to enter their own dental treatment history.

Based on the newly acquired image information and treatment history information in this way, the optimum tooth condition is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the treatment history information is P17, the node 61d is associated via the degree of association. The node 61d is associated with the tooth state C by w19 and the tooth state D by the degree of association w20. In such a case, the tooth state C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the tooth state D in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, Table 2 below shows an example of the degree of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

In addition to the above-mentioned reference image information, the reference filling information regarding the filling to the tooth may be used instead of the above-mentioned reference treatment history information. This reference padding information includes information on whether the padding is a padding or a covering, and if it is a padding, information on its shape. Information on the material of the padding (eg, ceramics, metal, platinum, etc.) is also included in this reference padding information. It also includes information about when the filling was stuffed into the teeth, how long it has been used, and if the filling itself is missing or worn. By forming a degree of association between such reference padding information and image information, it is possible to diagnose the tooth condition with high accuracy as well. This reference padding information may be extracted from the reference treatment history information or may be manually input.

In such a case, for the tooth to be diagnosed, the filling information regarding the filling to the tooth may be acquired from the past treatment history, or the filling information may be newly input. Determining the tooth condition based on the reference filling information according to the filling information acquired in this way is determined by forming a degree of association using the reference treatment history information as shown in FIG. 5 described above. It is based on the same process as the case.

In FIG. 6, in addition to the above-mentioned reference image information, a combination with the reference subjective symptom information instead of the above-mentioned reference treatment history information and a degree of association with the tooth condition with respect to the combination are set at three or more levels. An example is shown.

This reference subjective symptom information, which is added as an explanatory variable instead of the reference treatment history information, is all information about the patient's subjective symptom regarding the tooth to be imaged in the reference image information. The reference subjective symptom information includes all subjective symptoms related to teeth such as "cold symptom stains", "biting something hurts", and "bleeding when brushing". Such reference subjective symptom information may be used by the dentist listening to the patient and input to the medical record or the like, or may be input by the patient himself / herself. In actually implementing this program, it is premised that these reference treatment history information is converted into electronic data.

Since such subjective symptoms also affect the tooth condition, the discrimination accuracy can be improved by discriminating the tooth condition through the degree of association in combination with the reference image information.

In the example of FIG. 6, it is assumed that the input data is, for example, reference image information P01 to P03 and reference subjective symptom information P18 to 21. The intermediate node shown in FIG. 6 is a combination of reference image information and reference subjective symptom information as such input data. Each intermediate node is further linked to the output. In this output, the state of the tooth as an output solution is displayed.

Each combination (intermediate node) of the reference image information and the reference subjective symptom information is associated with each other through three or more levels of association with the tooth condition as this output solution. The reference image information and the reference subjective symptom information are arranged on the left side through this degree of association, and the tooth condition is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to the tooth condition with respect to the reference image information and the reference subjective symptom information arranged on the left side. In other words, this degree of association is an index indicating what kind of tooth condition each reference image information and reference subjective symptom information is likely to be associated with, and the reference image information and reference subjective symptom information. It shows the accuracy in selecting the most probable tooth condition from the information.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, in determining the actual search solution, the discrimination device 2 determines which of the reference image information, the reference subjective symptom information obtained when acquiring the reference image information, and the tooth condition in that case. Whether it was suitable or not, the past data is accumulated, and these are analyzed and analyzed to create the degree of association shown in FIG.

For example, at the time of evaluating the actual tooth condition in the past, it is assumed that the reference subjective symptom information is "a cold thing stains" with respect to a certain reference image information. In such a case, if there are many cases where the tooth condition is determined to be A, these are learned as a data set and defined in the form of the above-mentioned degree of association.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference image information P01 is the reference subjective symptom information P20, the state of the tooth is analyzed from the past data. When there are many cases of tooth condition A, the degree of association that this tooth condition leads to A is set higher, and when there are many cases of tooth condition B and there are few cases of tooth condition A, , The degree of association in which the tooth condition is connected to B is set high, and the degree of association in which the tooth condition is connected to A is set low. For example, in the example of the intermediate node 61a, the output of the tooth state A and the tooth state B is linked, but from the previous case, the degree of association of w13 connected to the tooth state A is set to 7 points, and the tooth state B is set. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence. Other than that, the configuration related to artificial intelligence is the same as the description in FIG.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference image information P01 is combined with the reference subjective symptom information P18, the degree of association of the tooth state C is w15, and the degree of association of the tooth state E is w15. The degree of association is w16. The node 61c is a node that is a combination of the reference subjective symptom information P19 and P21 with respect to the reference image information P02, and the degree of association of the tooth state B is w17 and the degree of association of the tooth state D is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the tooth condition from now on, the above-mentioned learned data will be used. In such a case, the image information of the tooth condition to be discriminated and the subjective symptom information are actually acquired. Here, the subjective symptom information is newly acquired for the tooth for which the image information is to be acquired when the state of the tooth is newly determined, and the acquisition method is the same as the above-mentioned reference subjective symptom information.

Based on the image information newly acquired in this way and the subjective symptom information, the optimum tooth condition is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired image information is the same as or similar to P02 and the subjective symptom information is the same as or similar to P21, the node 61d is associated via the degree of association. The node 61d is associated with the tooth state C by w19 and the tooth state D by the degree of association w20. In such a case, the tooth state C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the tooth state D in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

The tooth condition determined through the degree of association may be modified based on the estimation auxiliary information described below. This estimation auxiliary information is composed of any one or more of treatment history information regarding the past treatment history of the tooth to be discriminated, filling information regarding the filling to the tooth, and subjective symptom information regarding the subjective symptom of the tooth by the patient. ..

For example, as shown in FIG. 7, by making rules regarding the correction of the tooth condition as the output solution with the reference estimation auxiliary information P22 to P24, the reference estimation auxiliary information that is the same as or similar to the actually input estimation auxiliary information is used. Based on the estimation auxiliary information, the output solution (tooth condition) is adjusted according to the rule. At this time, in addition to the case of actually adjusting the output solution (tooth state), the degree of association itself may be adjusted.
For example, when the reference correction auxiliary information is applied as the reference correction auxiliary information, and the reference correction auxiliary information P22 is assumed to be "a cold thing stains the teeth", the level of caries is raised by one level. It is assumed that this is a rule (for example, when the caries is C1, this is raised to C2). Under such a rule, when the correction auxiliary information (subjective symptom information) is "a cold thing stains the teeth", the reference image is based on the rule created in advance in the reference correction auxiliary information P22 corresponding to this. The level of caries in the output (tooth condition) obtained from the information shall be increased by one level for judgment.

This estimation auxiliary information does not form a degree of association with the output (tooth condition), but is used to correct the output (tooth condition) searched through the degree of association with the image information. Tooth. At this time, how to correct the output solution is ruled in advance between the reference estimation auxiliary information and the adjustment to the output (tooth condition), and corresponds to the actually acquired estimation auxiliary information. Make adjustments according to the adjustment rules for the output (tooth condition) defined with the (same or similar) reference estimation auxiliary information. In other words, any form may be used as long as the output (tooth condition) is adjusted based on the estimation auxiliary information when it is input. The rule between the estimation auxiliary information for reference and the adjustment to the output (tooth condition) is that if the amount of tooth scraping is large, the amount of tooth scraping increases as the treatment history information, and the health of the tooth condition. Adjustments may be made to reduce the degree, and it is not essential to use the degree of association or artificial intelligence.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily determine and search for the tooth condition without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the input data and the output data described above do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

In the above-mentioned degree of association, the case where the information is composed of any of the reference treatment history information, the reference filling information, and the reference subjective symptom information in addition to the reference image information has been described as an example. It is not limited to this. That is, the degree of association may be composed of a combination of any two or more of the reference treatment history information, the reference filling information, and the reference subjective symptom information in addition to the reference image information. In addition to the reference image information, the degree of association is formed by adding any one or more of the reference treatment history information, the reference filling information, and the reference subjective symptom information, and other factors to this combination. May be.

In either case, data is input according to the reference information of the degree of association, and the state of the tooth is obtained using the degree of association.

Further, as shown in FIG. 8, the present invention determines the state of teeth based on the degree of association between two or more types of information, reference information U and reference information V. It is assumed that the reference information Y is the reference image information, and the reference information V is any of the reference treatment history information, the reference filling information, and the reference subjective symptom information.

At this time, as shown in FIG. 8, the output obtained for the reference information U is used as input data as it is, and is associated with the output (tooth state) via the intermediate node 61 in combination with the reference information V. May be good. For example, for reference information U (reference image information), after an output solution is output as shown in FIG. 3, this is used as an input as it is, and the degree of association with other reference information V is used. The output (tooth condition) may be searched.

Further, according to the present invention, instead of obtaining the tooth state as an output solution as an output, the warning information such as an alarm and an alarm based on the tooth state may be transmitted. The higher the condition of the teeth, the higher the degree of alerting of the alerting information. As a result, it is possible to efficiently call attention to the outside that the tooth is in a dangerous state.

Further, according to the present invention, it is characterized in that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this and can be described by a numerical value of 3 or more steps at any stage. It may be configured.

By discriminating the most probable tooth condition based on the degree of association represented by the numerical values of three or more stages, the degree of association is high under the situation where there are multiple possible candidates for the search solution. It is also possible to search and display in order. If the users can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. is there. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable that it appears once every tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition, when each reference information such as reference image information is acquired and knowledge, information, and data regarding the tooth condition and improvement measures for these are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Dental diagnosis system 2 Discrimination device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Discrimination unit 28 Storage unit 61 Node

The dental diagnosis program according to the present invention is a dental diagnosis program for discriminating the state of a tooth in a dental diagnosis, and includes an information acquisition step of acquiring image information obtained by imaging the tooth to be discriminated and padding information related to the filling to the tooth. , A combination having reference image information of a tooth imaged in the past and reference padding information regarding a filling in a tooth imaged in obtaining the reference image information, and a degree of association of three or more levels with the state of the tooth. To make a computer execute a determination step for determining the tooth condition based on the reference image information according to the image information acquired in the above information acquisition step and the reference filling information according to the filling information. It is characterized by.

Claims (9)

In a dental diagnosis program that determines the condition of teeth in dental diagnosis
An information acquisition step to acquire image information obtained by capturing an image of the tooth to be discriminated, and
The tooth condition is based on the reference image information according to the image information acquired in the above information acquisition step by using the reference image information of the tooth captured in the past and the degree of association between the tooth condition and the tooth condition in three or more stages. A dental diagnostic program characterized by having a computer perform a discriminant step to discriminate between. In the above information acquisition step, the above image information based on the CT image, the X-ray image, and the spectral image is acquired.
The dental diagnosis program according to claim 1, wherein in the determination step, reference image information corresponding to the image information is acquired from the CT image and the X-ray image. In the above information acquisition step, treatment history information regarding the past treatment history of the tooth to be discriminated is further acquired.
In the determination step, there are three or more stages of the combination having the reference image information, the reference treatment history information regarding the past treatment history of the tooth imaged in obtaining the reference image information, and the state of the tooth. The dental diagnosis program according to claim 1 or 2, wherein the condition of the tooth is determined based on the reference treatment history information according to the treatment history information acquired in the above information acquisition step. .. In the above information acquisition step, the filling information regarding the filling to the tooth is acquired, and the filling information is acquired.
In the determination step, the combination of the reference image information and the reference filling information regarding the filling in the tooth imaged in obtaining the reference image information and the degree of association with the state of the tooth in three or more stages are determined. The dental diagnosis program according to claim 1 or 2, wherein the dental diagnosis program is used, and further, the condition of the tooth is determined based on the reference filling information according to the filling information acquired in the above information acquisition step. In the above information acquisition step, the subjective symptom information regarding the subjective symptom of the tooth by the patient is acquired.
In the determination step, the combination of the reference image information and the reference subjective symptom information regarding the subjective symptom of the tooth by the past patient and the degree of association with the tooth condition in three or more stages are used. The dental diagnosis program according to claim 1 or 2, further comprising determining the tooth condition based on the reference subjective symptom information according to the subjective symptom information acquired in the above information acquisition step. In the above information acquisition step, the level of pain to the teeth is acquired as the above subjective symptom information, and
The dental diagnosis program according to claim 5, wherein in the determination step, the level of pain to the teeth is acquired as the reference subjective symptom information. In the above information acquisition step, as estimation auxiliary information, treatment history information regarding the past treatment history of the tooth to be discriminated, filling information regarding the filling from the past treatment history to the tooth, and subjective symptom regarding the subjective symptom of the tooth by the patient. Get any one or more of the information and
The dental diagnosis program according to claim 1, wherein in the determination step, corrections are made to the determined tooth condition based on the estimation auxiliary information acquired in the information acquisition step. The dental diagnosis program according to any one of claims 1 to 7, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. In a dental diagnosis system that determines the condition of teeth in dental diagnosis
Information acquisition means for acquiring image information of the tooth to be discriminated,
Based on the reference image information of the tooth captured in the past and the reference image information according to the image information acquired by the above information acquisition means by utilizing the degree of association between the tooth state and the state of the tooth in three or more stages, the tooth A dental diagnostic system characterized by being provided with a discriminating means for discriminating a state.

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