JP2022068485A - Oral cavity state evaluation system - Google Patents

Abstract

To provide an oral cavity state evaluation system capable of evaluating the state of an oral cavity by simple means.SOLUTION: An oral cavity state evaluation system 10 comprises: an information acquisition unit which acquires an oral cavity image including at least a first specific portion in the oral cavity from an interface part 20 as input information; and an information analysis part 33 which analyzes the input information by means of a learned model which is obtained by previously learning the oral cavity image to evaluate the state in the oral cavity. The information analysis part 33 estimates estimation information concerning the state of a second specific portion in the oral cavity different from the first specific portion.SELECTED DRAWING: Figure 1

Description

The present invention relates to an oral condition evaluation system for evaluating a user's oral condition.

There is known a system that analyzes the oral condition from an image including the entire oral cavity and assists a judgment by a specialist such as a dentist. Patent Document 1 discloses a dental analysis system that extracts a lesion site from an X-ray image of the entire oral cavity of a user.

Japanese Unexamined Patent Publication No. 2019-208831

Obtaining information about the condition of the entire oral cavity is difficult without the help of specialists and testing instruments or devices such as probes. Therefore, it is required that the user's own oral condition can be grasped by a simple means.

The oral condition evaluation system according to the present invention includes an information acquisition unit that acquires an oral image including at least the first specific portion in the oral cavity as input information from the interface unit, and a preliminarily used unit for evaluating the input information in order to evaluate the condition in the oral cavity. The information analysis unit includes an information analysis unit that analyzes the oral image by a learning model that has learned the oral image, and the information analysis unit estimates estimated information about a state in the second specific portion in the oral cavity that is different from the first specific portion.

According to the oral condition evaluation system according to the present invention, it is possible to provide an oral condition evaluation system capable of evaluating an oral condition by a simple means.

The block diagram which shows the structure of the oral condition evaluation system of embodiment. The figure which shows the oral cavity image which is an example of input information. FIG. 2 is a partially enlarged schematic view of the oral image of FIG. The figure which shows an example of the oral condition when there is crowding. The figure which shows an example of the oral condition in the absence of crowding. The figure which shows an example of the oral condition when there is an interdental space. The figure which shows an example of the oral condition when there is no interdental space. The figure which shows an example of the oral condition when there is gingival recession. The figure which shows an example of the oral condition when there is no gingival recession.

(Example of possible forms of oral condition evaluation system)
(1) The oral condition evaluation system according to the present invention evaluates an information acquisition unit that acquires an oral image including at least the first specific portion in the oral cavity as input information from the interface unit, and the input information to evaluate the condition in the oral cavity. Therefore, the information analysis unit includes an information analysis unit that analyzes the oral cavity image by a learning model that has been learned in advance, and the information analysis unit obtains estimated information about a state in the second specific part of the oral cavity that is different from the first specific part. presume.
According to the oral condition evaluation system, the information analysis unit estimates estimated information about the state of the second specific part in the oral cavity different from the first specific part from the oral image including the first specific part. The oral condition of the entire oral cavity can be evaluated regardless of whether or not the second specific site is included in the oral image. Therefore, the oral condition can be evaluated by a simple means.

(2) According to an example of the oral condition evaluation system, the oral image includes a tooth image and a gum image in the oral cavity.
According to the oral condition evaluation system, since the oral image includes an image of an appropriate part in the oral cavity, the oral condition can be appropriately evaluated.

(3) According to an example of the oral condition evaluation system, the tooth image includes a central incisor image, a lateral incisor image, and a canine image in the oral cavity.
According to the oral condition evaluation system, since the oral image includes an image of an appropriate tooth in the oral cavity, the oral condition can be appropriately evaluated.

(4) According to an example of the oral condition evaluation system, the second specific site includes at least one of the premolars and molars in the oral cavity, and the estimated information is the premolars and molars in the oral cavity. Contains at least one piece of information about the molars.
According to the oral condition evaluation system, at least one of the information regarding the premolars and molars in the oral cavity is estimated, so that the oral condition can be appropriately evaluated.

(5) According to an example of the oral condition evaluation system, the estimated information includes interdental space, gingival retraction, crowding, gingival inflammation, unpolished, gingival, caries, hypersensitivity, and mouth odor at the second specific site. And information on the presence or absence or degree of coloring, information on the presence of periodontal disease, information on whether the chewing function is normal, information on whether the occlusion is normal, information on whether the swallowing function is normal. , And at least one of the information regarding the condition of the teeth, gums, and oral mucosa corresponding to the second specific site.
According to the oral condition evaluation system, at least one of the tooth and gum conditions at the second specific site can be estimated. Therefore, the oral condition can be appropriately evaluated.

(6) According to an example of the oral condition evaluation system, an information storage unit for storing the learning model is further provided.
According to the oral condition evaluation system, a learning model for analyzing the oral condition can be preferably referred to.

(7) According to an example of the oral condition evaluation system, the input information includes information on the lifestyle of the user, information on the oral secretions of the user, information on the oral flora of the user, and attributes of the user himself / herself. And at least one of the information obtained by the sensor that detects the condition in the oral cavity.
According to the oral condition evaluation system, the oral condition can be evaluated more appropriately.

(8) According to an example of the oral condition evaluation system, an information output unit that outputs information corresponding to the estimated information as output information is further provided, and the information output unit outputs the output information to at least the interface unit. do.
According to the oral condition evaluation system, the user can easily recognize the output information.

(9) According to an example of the oral condition evaluation system, the output information includes information on the current oral condition of the user, information on the prediction of the future oral condition of the user, information on the oral care method for the user, and information on the oral care method for the user. Includes at least one piece of information about the user's health condition affected by the user's oral condition.
According to the oral condition evaluation system, the user can suitably recognize the output information regarding the oral condition.

(Embodiment)
The oral condition evaluation system 10 of the present embodiment will be described with reference to FIGS. 1 to 9. The oral condition evaluation system 10 is a system that acquires and analyzes the input information I and estimates the estimated information E. The oral condition evaluation system 10 may calculate the output information O corresponding to the estimation information E and output it to a predetermined configuration. The main element constituting the oral condition evaluation system 10 is the server 30. Preferably, the oral condition evaluation system 10 includes an interface unit 20 for executing information exchange with the server 30. The interface unit 20 is, for example, a smart device configured to be portable by the user. Smart devices include tablet terminals or smartphones. In another example, the interface unit 20 is a personal computer. The personal computer is installed in the user's residence, store or dental office. Stores include stores that sell oral care items or other products. The interface unit 20 and the server 30 are configured to be able to communicate with each other using, for example, the Internet line N. In another example, the interface unit 20 and the server 30 are integrally configured.

The interface unit 20 includes a control unit 21, a storage unit 22, an acquisition unit 23, a communication unit 24, and a display unit 25. The control unit 21 includes an arithmetic processing device that executes a predetermined control program. The arithmetic processing unit includes, for example, a CPU, GPU or MPU.

The storage unit 22 stores various control programs executed by the control unit 21 and information used for various control processes. The storage unit 22 includes, for example, a non-volatile memory and a volatile memory. The non-volatile memory includes, for example, at least one of ROM, EPROM, EEPROM, and flash memory. Volatile memory includes, for example, RAM.

The acquisition unit 23 acquires the input information I from the user. The acquisition unit 23 includes an arbitrary configuration for acquiring the input information I of the user. The first example is a camera capable of acquiring an oral image P of the user's oral cavity. The oral image P includes still images and moving images. The oral cavity image P includes a three-dimensional image having depth information or a panoramic image composed by laminating a plurality of images. The camera may be a camera mounted on a smart device, or may be a separately independent camera. The independent camera is, for example, a pen-type camera provided with a camera at the tip suitable for photographing in the oral cavity or a camera capable of photographing in a range of 360 °. The independent camera is configured to enable wired or wireless communication with the control unit 21. The oral cavity image P taken by the independent camera is transmitted to the control unit 21 by wired or wireless communication. In the second example, the acquisition unit 23 is a user interface configured so that the user can input or select characters and the like. The user input information I is at least one of information about the user's lifestyle, information about the user's oral secretions, information about the user's oral flora, answer information to a predetermined question, and information about the user's own attributes. May further be included. Information about the user's lifestyle includes information about the user's dietary content, oral care behavior, wake-up or sleep time. Information about the user's oral secretions includes information about the amount of saliva, viscosity, hydrogen ion concentration, amount of antibacterial component, and amount of component related to tooth remineralization. Information about the user's oral flora includes information about the amount and type of major bacteria present in saliva, plaque, or mucous membranes and information about the type and amount of antibodies of the major bacteria. The information on the user's oral secretions and the information on the user's oral bacterial flora may be the result of an interview with the user, and are the results of inspection of the oral endocrines and the like existing in the user's oral cavity by a predetermined means. There may be. The answer information for a given question is the user's gum condition question, preferred brushing method for oral care, number of brushes per day, time to brush each time, time to brush, and dentures. Including the presence or absence. Questions about the condition of the user's gums include, for example, the presence or absence of bleeding from the gums when brushing or eating or drinking. Information about the user's own attributes is, for example, the user's own age, gender, height, weight, dominant arm, and medical history. In the third example, the acquisition unit 23 is a sensor that detects the state in the oral cavity. The sensor is, for example, a fluorescence sensor and a temperature sensor. The fluorescence sensor irradiates light of a predetermined wavelength, quantifies the amount of light, and measures the distribution and amount of a predetermined substance in the oral cavity. Predetermines are, for example, stains or plaques. The temperature sensor measures the temperature in the oral cavity. The sensor is configured to enable wired or wireless communication with the control unit 21. The information obtained by the sensor is transmitted to the control unit 21 by wired or wireless communication. In the present embodiment, the acquisition unit 23 may be configured by combining a plurality of the first to third examples. The sensor may be configured to be able to further measure at least one of masticatory force, quotient force, gum blood flow, bad breath, brush pressure strength during brushing, or toothbrush movement during brushing.

The communication unit 24 is configured to be able to communicate with the outside of the interface unit 20 based on the control of the control unit 21. The communication unit 24 is configured to be able to communicate via the Internet line N. The communication unit 24 may be configured to be able to communicate with the server 30 of the interface unit 20 by wired communication or wireless communication. For example, the communication unit 24 transmits the user input information I acquired by the acquisition unit 23 under the control of the control unit 21, and receives the output information O from the server 30.

The display unit 25 displays various information based on the control of the control unit 21. The various information is, for example, information regarding the input information I of the user and information regarding the output information O from the server 30. In one example, the display unit 25 is composed of a display. The display of the display unit 25 may be composed of a touch panel. When a part of the display unit 25 is composed of a touch panel, a part of the display unit 25 may also serve as a user interface function of the acquisition unit 23.

The user communicates with the server 30 by, for example, inputting a predetermined URL in the interface unit 20 or reading a QR code (registered trademark) in the interface unit 20. Communication with the server 30 may be started by the user selecting an icon displayed on the display unit 25.

The server 30 includes an information acquisition unit 31, an information output unit 32, an information analysis unit 33, and an information storage unit 34. The information acquisition unit 31 is configured so that information can be acquired. In one example, the information acquisition unit 31 acquires information from the communication unit 24 of the interface unit 20. The information output unit 32 is configured to be able to output information. In one example, the information output unit 32 outputs information to the communication unit 24 of the interface unit 20.

The information analysis unit 33 executes various analyzes and controls. The information analysis unit 33 includes an arithmetic processing device that executes a predetermined control program. The arithmetic processing unit includes, for example, a CPU or an MPU. The information analysis unit 33 is configured to be able to analyze the input information I from the user. In the first example, the information analysis unit 33 analyzes the input information I by the learning model M by machine learning. The input information I in this case includes the user's oral image P. The learning model M is, for example, a supervised learning model which is one of the models of machine learning. In the second example, the information analysis unit 33 analyzes the input information I with reference to the correspondence table stored in the information storage unit 34. The input information I in this case is changed to or in addition to the oral image P, and includes information on the user's lifestyle, information on the user's oral secretions, information on the user's oral bacterial flora, and answer information for a predetermined question. Includes at least one. The correspondence table is a table in which at least one of the input information I or the estimation information E and the output information O are associated with each other. When the input information I is further included in addition to the oral image P, in one example, the learning model M further includes another learning model that analyzes the input information I other than the oral image P. Another learning model is configured to be able to output parameters for correcting the estimation information E of the oral image P, for example. In another example, the learning model M is configured as a model capable of performing multimodal learning in which both the oral image P and the input information I not including the oral image P are combined and learned.

The information storage unit 34 stores at least one of the learning model M, the correspondence table, and various information. The information analysis unit 33 refers to the learning model M, the correspondence table, and various information stored in the information storage unit 34 as needed.

The analysis of the input information I including the oral cavity image P using the learning model M executed by the information analysis unit 33 will be described. The analysis step executed by the information analysis unit 33 includes a plurality of steps. The plurality of steps include the first step of detecting the oral cavity region R from the oral cavity image P of the user, the second step of estimating the estimated information E from the input information I, and the second step of calculating the output information O corresponding to the estimated information E. Includes 3 steps.

The first step is executed by the information analysis unit 33 by any means. In one example, the oral region R is detected from the oral image P of the user's oral cavity by using a trained model suitable for detecting the face. The oral region R includes a first oral region R1 including the teeth of the user's upper jaw and a second oral region R2 including the teeth of the user's lower jaw. The trained model for detecting the face is introduced into the information analysis unit 33 by, for example, API.

When the oral cavity region R cannot be acquired from the oral cavity image P in the first step, the information analysis unit 33 outputs the contents to the display unit 25 of the interface unit 20 via the information output unit 32. In one example, the oral cavity image P is included because the oral cavity image P does not include the oral cavity region R, the oral cavity image P is blurred, or the oral cavity image P cannot be recognized because the brightness of the oral cavity image P is not appropriate. The information requesting the user to input the input information I again is output to the display unit 25 of the interface unit 20. If one of the first oral region R1 and the second oral region R2 can be acquired and the other cannot be acquired, the information analysis unit 33 may determine that the first step has been completed, and the input information I may be input again. The information requested from the user may be output to the interface unit 20.

In the second step, the information analysis unit 33 analyzes the oral image P by the learning model M stored in the information storage unit 34, and estimates the estimated information E. The estimation information E includes information regarding the presence / absence of a predetermined state, the degree, and at least one of the occurrence probabilities. Estimated information E relates to interdental space, gingival recession, crowding, gingival inflammation, unpolished, bruxism, caries, hypersensitivity, halitosis, and the presence or absence or degree of coloring in a second specific site different from the first specific site. It contains at least one piece of information, or information about the condition of the teeth, gums, and oral mucosa corresponding to the second particular site. Information about the condition of the teeth, gums, and oral mucosa includes, for example, at least one of tooth wear, tooth defects, presence or absence or degree of tooth fracture, complexion, dryness, and texture. The texture includes a plurality of textures including, for example, a hard texture that feels firm when touched with a finger and a soft texture that feels bumpy when touched with a finger. The second specific site comprises at least one of the premolars and molars in the oral cavity. Estimated information E includes at least one piece of information about the premolars and molars in the oral cavity. The estimation information E is information on whether or not periodontal disease is present in the user's oral cavity, information on whether or not the masticatory function is normal, information on whether or not the occlusion is normal, and information on whether or not the swallowing function is normal. May contain at least one of. Information regarding the presence or absence of periodontal disease is information regarding at least one of the size, depth, and number of so-called periodontal pockets that occur between the gingiva and the tooth. The information on whether or not the masticatory function is normal is, for example, information on masticatory force, presence or absence of uneven mastication, and masticatory sound. Information on whether or not the occlusion is normal is, for example, information on the occlusal force and the engagement between the teeth of the upper jaw and the teeth of the lower jaw. The information regarding whether or not the swallowing function is normal is, for example, information regarding the presence or absence of dysphagia.

In the second step, the learning model M further calculates the correspondence between the first information and the second information. The first information is, for example, the presence or absence of crowding at the first specific site. The second information is, for example, the presence or absence of an interdental space in the second specific site. The information analysis unit 33 calculates, for example, the relationship between the first information and the second information by statistical analysis. When the statistical analysis by the learning model M reveals that there is a correlation between the presence of crowding in the first specific site and the presence of interdental space in the second specific site, the information analysis unit 33 will perform the information analysis unit 33. Add the content to the estimation information E. In another example, the first information is information about the user's dominant arm. The second information is the presence or absence of gingival recession at the first specific site. According to the analysis by the learning model M, for example, when the degree of gingival recession on the same side as the dominant arm is larger than the degree of gingival recession on the opposite side of the dominant arm in the first specific site, there is a possibility that the user's care method is biased. be. The information analysis unit 33 adds the content to the estimation information E.

In the third step, the information analysis unit 33 calculates the output information O corresponding to at least one of the estimated information E. The output information O is at least one of information about the user's current oral condition, information about the user's future oral condition prediction, information about the oral care method for the user, and information about the user's health condition affected by the user's oral condition. Including one.

A learning model M for analyzing the oral region R will be described. The training model M includes a plurality of trained models. The learning model M includes a first trained model M1 for determining whether or not the oral region R can be evaluated and a second trained model M2 for actually evaluating the oral region R.

The first trained model M1 is a learning model M11 for determining whether the oral region R of the oral image P has an analyzable image quality, a learning model M12 for determining whether crowding evaluation is possible, and an interdental space in the upper jaw teeth. A learning model M13 for determining whether or not the gap can be evaluated, a learning model M14 for determining whether or not the interdental space in the lower jaw tooth can be evaluated, and a learning model for determining whether or not the gingival recession can be evaluated. Includes at least one of M15.

The second trained model M2 is a learning model M21 for determining the presence or absence of crowding in the upper jaw teeth, a learning model M22 for determining the presence or absence of crowding in the lower jaw teeth, and a determination for the presence or absence of interdental gaps in the upper jaw teeth. It includes at least one of a learning model M23 for determining the presence or absence of interdental space in the lower jaw teeth, and a learning model M25 for determining the presence or absence of interdental retraction.

The creation of the learning model M will be described. The learning model M was created based on approximately 10,000 oral images P. The developer classified the oral image P into a learning image, a verification image, and a test image. The learning image is supervised data at the time of creating the learning model M. The verification image is an image for modifying the operation of the learning model M based on the learning image. The test image is an image for finally confirming the operation of the learning model M. The test image is used, for example, to confirm whether or not the learning model M is overfitting. The learning image is about 56% of the whole. The verification image is about 24% of the whole. The test image is 20% of the total. In this embodiment, the learning model M outputs the result of classification. The learning model M includes interdental space, gingival retraction, crowding, gingival inflammation, unpolished, gingival, caries, hypersensitivity, mouth odor, and information on the presence or absence or degree of coloring, and periodontal disease in the user's oral cavity. Information on whether or not to do, information on whether or not chewing function is normal, information on whether or not occlusion is normal, information on whether or not swallowing function is normal, and teeth, gums, and teeth corresponding to the second specific site, and , Output at least one piece of information about the condition of the oral mucosa.

FIG. 2 shows an example of the oral cavity image P of the user. The oral image P includes at least the first specific site in the oral cavity. The first specific site includes a middle incisor T1, a tooth corresponding to the middle incisor T1, a side incisor T2, a tooth corresponding to the side incisor T2, a canine T3, and a tooth stem corresponding to the canine T3. The oral image P includes a tooth image and a gum image in the oral cavity. Tooth images include intraoral central incisor T1 images, lateral incisor T2 images, and canine T3 images. The gingival image includes an image of the gingiva corresponding to the central incisor T1, the lateral incisor T2, and the canine T3. The oral image P includes at least one first specific site among the four first specific sites on the top, bottom, left, and right in the user's oral cavity.

With reference to FIG. 3, a preferable range of the tooth image included in the oral image P will be described. In determining the presence or absence of crowding, it is preferable that the range from the end E1 of the central incisor T1 to the broken line L2 is included in the oral image P. In determining the presence or absence of the interdental space, it is preferable that the oral image P includes the range from the end E1 of the central incisor T1 to the two-dot chain line L3. In determining the presence or absence of gingival recession, it is preferable that the range from the end E1 of the central incisor T1 to the solid line L1 is included in the oral image P. Since the image of the central incisor T1 in this range is included in the oral image P, the image of the corresponding gum is included in the oral image P.

Each oral image P was supervised by a specialist who analyzes the oral cavity. 4 to 9 show an example of a supervised oral image P. Professionals who analyze the oral cavity are, for example, dentists, dental hygienists, researchers who study oral conditions, or developers who develop oral care products. An expert who analyzes the oral cavity determines the degree of crowding, the degree of gingival recession, and the degree of interdental space for each of the first oral region R1 and the second oral region R2 of the oral image P of the oral cavity. rice field. Experts analyzing the oral cavity made decisions based on the type of crowding in determining the degree of crowding. Experts who analyze the oral cavity made multiple stages of judgment of gingival recession, from no gingival recession to total gingival recession. Experts analyzing the oral cavity performed multiple stages of evaluation of the interdental space, from no interdental space to severe interdental space, for each of the upper and lower jaws.

The state at the first specific site in the oral cavity correlates with the state at the second specific site different from the first specific site in the oral cavity. In one example, in a study of 75 women in their 40s and 70s, the gingival condition in the anterior teeth and the gingival condition in the back teeth were highly correlated. That is, when gingival recession occurs at the first specific site, gingival recession also occurs at the second specific site. The same applies to the degree of interdental space and crowding.

The information analysis unit 33 outputs the estimated information E to a predetermined configuration. In the first example, the information analysis unit 33 outputs to the information output unit 32. In the second example, the information analysis unit 33 outputs to the information storage unit 34. In the third example, the information analysis unit 33 outputs the estimated information E to both the information output unit 32 and the information storage unit 34.

The estimation information E is associated with the output information O. The output information O will be described. The output information O is at least information about the user's current oral condition, information about the user's future oral condition prediction, information about the oral care method for the user, and information about the user's health condition affected by the user's oral condition. Includes one. The information about the user's current oral condition includes at least one of information about the presence or absence of crowding in the entire oral cavity of the user, the presence or absence of gingival recession, and the presence or absence of interdental space. Information about the user's current oral condition may further include at least one of gingival inflammation, unpolished condition, unpolished condition, bruxism, hyperesthesia, and halitosis. ..

Information about the user's future oral prediction includes an estimated image showing the user's oral condition after a predetermined period of time. Information on oral care methods for the user includes information on oral care products and usage methods suitable for the user's oral condition. Information about the user's health condition affected by the user's oral condition includes, for example, information about non-oral health related to periodontal disease or oral condition.

The correspondence between the estimation information E and the output information O is executed by the information analysis unit 33 by any means. In the first example, the estimation information E and the output information O are associated with each other by a researcher who studies the oral condition in advance or a developer who develops oral care products. In the second example, the correspondence between the estimation information E and the output information O is executed by a machine learning model.

The operation of the oral condition evaluation system 10 of the present embodiment will be described. The user inputs the input information I to the acquisition unit 23. The control unit 21 controls the communication unit 24 to output the input information I to the server 30. The server 30 acquires the input information I in the information acquisition unit 31. The information analysis unit 33 calculates the estimation information E from the input information I by using the learning model M stored in the information storage unit 34. The information analysis unit 33 calculates the output information O corresponding to the estimation information E. The server 30 outputs the output information O from the information output unit 32 to the interface unit 20. The control unit 21 acquires the output information O from the communication unit 24 and displays it on the display unit 25. The user recognizes the output information O through the display of the display unit 25.

According to the oral condition evaluation system 10 of the present embodiment, the following effects can be further obtained.
The information analysis unit 33 analyzes the relationship between the first information and the second information in any combination. Therefore, by the analysis by the learning model M executed by the information analysis unit 33, it is possible to evaluate the oral condition that was not manifested by the analysis by the expert who analyzes the oral cavity.

(Modification example)
The description of the embodiments is an example of possible forms of the oral condition evaluation system and the oral care recommendation method according to the present invention, and is not intended to limit the forms. In addition to the embodiments, the present invention may take, for example, a modification of the embodiment shown below and a combination of at least two modifications that do not contradict each other.

-The learning model M may be configured to output the result of regression analysis. In this case, the learning model M quantifies and outputs at least one of the degree of crowding, the degree of interdental space, and the degree of gingival recession.

-At least one of the trained model M1 and the trained model M2 may be a model trained by unsupervised learning or reinforcement learning. At least one of each learning model M11 to M15 and each learning model M21 to M25 may be a model learned by unsupervised learning or reinforcement learning.

The learning model M is a first trained model M1 for determining whether or not evaluation is possible without distinguishing between the first oral region R1 and the second oral region R2 in the oral image P, and a second trained model M for evaluation. It may be model M2. The learning model M may be a first trained model M1 that recognizes a set of teeth and gums in the oral image P and determines whether or not evaluation is possible, and a second trained model M2 that performs evaluation. good. Further, it may be a learning model M that recognizes a set of teeth and gums in the oral cavity region R and executes analysis when the majority can be evaluated.

The learning model M may be configured so that the oral cavity image P is displayed as output information O in a pseudo color. In one example, the region used for analysis by the learning model M in the oral image P, or the region where the interdental space, gingival recession, and crowding occur is displayed in red. The user can easily recognize the area used for the analysis and the area where the oral condition has a problem.

-Any combination is selected as the combination of the first information and the second information in the second step. Both the first information and the second information may be different input information I, and both the first information and the second information may be different estimation information E.

-When the output information O includes information on the user's oral care products and usage, the output information O may be configured to include purchase information for purchasing the corresponding oral care products. In one example, the purchase information is information about the store where the relevant oral care product can be purchased or experienced. In another example, the purchase information is website information on the web that sells the relevant oral care product.

-The input information I may be acquired via the IoT device. In one example, input information I is acquired by connecting an IoT device to an oral care item used for brushing. In one example, input information I includes information about the number of tooth brushes per day and the frequency of use of oral care items. The IoT device may transmit the input information I to the acquisition unit 23, or may transmit the input information I to the server 30.

-At least one of the learning model M, the correspondence table, and various information may be stored in a place other than the information storage unit 34 of the server 30. In one example, it is stored in a storage unit provided in the interface unit 20. In another example, it is stored in a database configured in the external environment.

The input information I may include a first input information I1 including the current state information of the user and a second input information I2 which is the past state information of the user. When the first input information I1 and the second input information I2 include the oral image P of the user, the oral image P included in the second input information I2 is the user's oral image P before the acquisition of the first input information I1. It is an image in the oral cavity. When the first input information I1 and the second input information I2 include the information about the oral care of the user, the information about the oral care contained in the second input information I2 is a predetermined time before the acquisition of the first input information I1. Information about oral care. The predetermined time is, in one example, one month or more. In another example, the predetermined time is one year or more. The second input information I2 may be transmitted to the server 30 via the communication unit 24 before the acquisition unit 23 acquires the first input information I1, and the second input information I2 may be transmitted to the server 30 at the same time as the transmission of the first input information I1 via the communication unit 24. May be transmitted to the server 30. In this case, the interface unit 20 further includes a storage unit that stores at least the second input information I2. The input information I may further include the third input information I3 acquired before the second input information I2 by a predetermined time. The information analysis unit 33 calculates the estimation information E using at least one of the first input information I1 to the third input information I3. The accuracy is further improved by adding the third input information I3. The interval of the predetermined time may be changed by each input information I. The input information I may further include the input information I after the fourth input information.

10: Oral condition evaluation system 20: Interface unit 30: Server 31: Information acquisition unit 32: Information output unit 33: Information analysis unit 34: Information storage unit E: Estimated information I: Input information O: Output information

Claims (9)

An information acquisition unit that acquires an oral image including at least the first specific part in the oral cavity as input information from the interface unit.
It is provided with an information analysis unit that analyzes the input information by a learning model that has learned the oral image in advance in order to evaluate the state in the oral cavity.
The information analysis unit is an oral condition evaluation system that estimates estimated information about a state in a second specific part in the oral cavity different from the first specific part. The oral condition evaluation system according to claim 1, wherein the oral image includes a tooth image and a gum image in the oral cavity. The tooth image includes a central incisor image, a lateral incisor image, and a canine image in the oral cavity.
The oral condition evaluation system according to claim 2. The second specific site comprises at least one of the premolars and molars in the oral cavity.
The oral condition evaluation system according to any one of claims 1 to 3, wherein the estimated information includes at least one of information regarding premolars and molars in the oral cavity. The estimated information includes interdental space, gingival recession, crowding, gingival inflammation, unpolished, gingival, caries, hypersensitivity, mouth odor, and information on the presence or absence or degree of coloring at the second specific site, and periodontal disease. Information on the presence or absence, information on whether the chewing function is normal, information on whether the occlusion is normal, information on whether the swallowing function is normal, and the teeth and gums corresponding to the second specific site. , And the oral condition evaluation system according to any one of claims 1 to 4, which comprises at least one piece of information regarding the condition of the oral mucosa. Further provided with an information storage unit for storing the learning model.
The oral condition evaluation system according to any one of claims 1 to 5. The input information includes information on the lifestyle of the user, information on the oral secretions of the user, information on the bacterial flora in the oral cavity of the user, information on the attributes of the user himself, and a sensor for detecting the state in the oral cavity. The oral condition evaluation system according to any one of claims 1 to 6, further comprising at least one of the information obtained by. Further provided with an information output unit that outputs information corresponding to the estimated information as output information.
The oral condition evaluation system according to any one of claims 1 to 7, wherein the information output unit outputs the output information to at least the interface unit. The output information includes information on the user's current oral condition, information on the user's future oral condition prediction, information on the oral care method for the user, and the user's health condition affected by the user's oral condition. The oral condition assessment system according to claim 8, which comprises at least one piece of information relating to.

Images