Oral cavity state evaluation system

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 31 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 state of the first specific portion on the basis of the input information. 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, from analytic information of the state of the first specific portion.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an oral condition evaluation system for evaluating a user's oral condition.

Background Art

[0002] There is known a system that analyzes an oral condition from an image including the entire oral cavity and assists a judgment by an expert 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.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Obtaining information regarding the state of the entire oral cavity is difficult when it does not depend on an expert and an inspection instrument or device such as a probe. For this reason, it is required that the user can grasp his / her own oral condition by simple means. The present invention is an invention for solving the above problems, and an object thereof is to provide an oral condition evaluation system that can evaluate an oral condition by simple means.

Means for Solving the Problems

[0005] The oral condition evaluation system according to the present invention includes an information acquisition unit that acquires, as input information, an oral image including at least a first specific site in the oral cavity from an interface unit, and an information analysis unit that analyzes the state of the first specific site based on the input information. The information analysis unit estimates estimation information regarding the state of a second specific site in the oral cavity different from the first specific site from the analysis information of the state of the first specific site.

Effects of the Invention

[0006] According to the oral condition evaluation system related to the present invention, an oral condition evaluation system that can evaluate the oral condition by simple means can be provided.

Brief Description of Drawings

[0007]

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Mode for Carrying Out the Invention

[0008] (An example of a form that the oral condition evaluation system can take) (1) The oral condition evaluation system related to the present invention includes an information acquisition unit that acquires, as input information, an oral image including at least a first specific site in the oral cavity from an interface unit, and an information analysis unit that analyzes the state of the first specific site based on the input information. The information analysis unit estimates estimation information regarding the state of a second specific site in the oral cavity different from the first specific site from the analysis information of the state of the first specific site. According to the above oral condition evaluation system, the information analysis unit estimates estimation information regarding the condition of a second specific site in the oral cavity that is different from the first specific site from an oral cavity image including the first specific site. Regardless of whether the second specific site is included in the oral cavity image, the oral condition of the entire oral cavity can be evaluated. Therefore, the oral condition can be evaluated by simple means.

[0009] (2) According to an example of the oral condition evaluation system, the oral cavity image includes a tooth image and a gingiva image in the oral cavity. According to the above oral condition evaluation system, since the oral cavity image includes an image of an appropriate site in the oral cavity, the oral condition can be appropriately evaluated.

[0010] (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 above oral condition evaluation system, since the oral cavity image includes an image of an appropriate tooth in the oral cavity, the oral condition can be appropriately evaluated.

[0011] (4) According to an example of the oral condition evaluation system, the second specific site includes at least one of a premolar and a molar in the oral cavity, and the estimation information includes at least one of information regarding the premolar and the molar in the oral cavity. According to the above oral condition evaluation system, since at least one of the information regarding the premolar and the molar in the oral cavity is estimated, the oral condition can be appropriately evaluated.

[0012] (5) According to an example of the oral condition evaluation system, the estimation information includes information regarding the presence or degree of interdental space, gingival recession, overgrowth, gingival inflammation, remaining abrasion, tooth grinding, caries, tooth hypersensitivity, halitosis, and staining at the second specific site, information regarding whether periodontal disease exists, information regarding whether the masticatory function is normal, information regarding whether the occlusion is normal, information regarding whether the swallowing function is normal, and at least one of information regarding the condition of the tooth, gingiva, and oral mucosa corresponding to the second specific site. According to the above oral condition evaluation system, at least one of the conditions of teeth and gums at the second specific site can be estimated. Therefore, the oral condition can be appropriately evaluated.

[0013] (6) According to an example of the oral condition evaluation system, the system further includes an information storage unit that stores a learning model that has learned the oral image in advance for evaluating the condition in the oral cavity, and the information analysis unit analyzes the input information using the learning model. According to the above oral condition evaluation system, the estimation information is estimated by the learning model. Therefore, the oral condition can be more appropriately evaluated.

[0014] (7) According to an example of the oral condition evaluation system, the input information further includes at least one of information regarding the user's lifestyle, information regarding the user's oral secretions, information regarding the user's oral flora, information regarding the user's own attributes, and information obtained by a sensor that detects the condition in the oral cavity. According to the above oral condition evaluation system, the oral condition can be more appropriately evaluated.

[0015] (8) According to an example of the oral condition evaluation system, the system further includes an information output unit that outputs information corresponding to the analysis information and the estimation information as output information, and the information output unit outputs the output information to at least the interface unit. According to the above oral condition evaluation system, the user can easily recognize the output information.

[0016] (9) According to an example of the oral condition evaluation system, the output information includes at least one of information regarding the user's current oral condition, information regarding prediction of the user's future oral condition, information regarding oral care methods for the user, and information regarding the user's health condition affected by the user's oral condition. According to the above oral condition evaluation system, the user can preferably recognize the output information regarding the oral condition.

[0017] (Embodiment) With reference to FIGS. 1 to 9, the oral condition evaluation system 10 of the present embodiment will be described. The oral condition evaluation system 10 is a system that analyzes input information I and estimates estimation information E based on the analysis information A obtained from the input information I. The oral condition evaluation system 10 may calculate output information O corresponding to the analysis information A and 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 performing information exchange with the server 30. The interface unit 20 is, for example, a smart device configured to be portable by the user. The smart device includes a tablet terminal or a smartphone. In another example, the interface unit 20 is a personal computer. The personal computer is installed in the user's residence, store, or dental clinic. The store includes a store that sells oral care items or a store that sells other products. The interface unit 20 and the server 30 are configured to be communicable with each other using, for example, the Internet line N. In another example, the interface unit 20 and the server 30 are integrally configured.

[0018] 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 device includes, for example, a CPU, a GPU, or an MPU.

[0019] 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 a ROM, an EPROM, an EEPROM, and a flash memory. The volatile memory includes, for example, a RAM.

[0020] The acquisition unit 23 acquires the input information I from the user. The acquisition unit 23 has an arbitrary configuration for acquiring the user's input information I. In the first example, it is a camera capable of acquiring an oral cavity image P of the user's oral cavity. The oral cavity 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 formed by stitching together a plurality of images. The camera may be a camera mounted on a smart device or a separately independent camera. The independent camera is, for example, a pen-type camera provided with a camera at a tip suitable for photographing inside the oral cavity or a camera capable of photographing in a 360° range. The independent camera is configured to be capable of wired or wireless communication with the control unit 21. The oral cavity image P captured 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 to allow the user to input or select characters or the like. The user's input information I may further include at least one of information regarding the user's lifestyle, information regarding the user's oral secretions, information regarding the user's oral flora, response information to a predetermined question, and information regarding the user's own attributes. The information regarding the user's lifestyle includes information regarding the user's diet, oral care behavior, wake-up or sleep time. The information regarding the user's oral secretions includes information regarding the amount, viscosity, hydrogen ion concentration, amount of antibacterial components, and amount of components related to tooth remineralization of saliva. The information regarding the user's oral flora includes information regarding the amount and type of main bacteria present in saliva, dental plaque, or mucosa and information regarding the type and amount of antibodies of the main bacteria. The information regarding the user's oral secretions and the information regarding the user's oral flora may be the result of a medical interview with the user or the result information obtained by examining oral secretions or the like present in the user's oral cavity by a predetermined means. The response information regarding a predetermined question includes questions regarding the state of the user's gums, the preferred brushing method during oral care, the number of times of brushing teeth per day, the time spent on brushing teeth each time, the time when brushing teeth is performed, the frequency of use of oral care items, and the presence or absence of dentures. The question regarding the state of the user's gums includes, for example, the presence or absence of bleeding from the gums during brushing or eating.Information regarding the user's own attributes includes, for example, the user's own age, gender, height, weight, dominant hand, and medical history. In the third example, the acquisition unit 23 is a sensor that detects the state inside 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. The predetermined substance is, for example, stain or plaque. The temperature sensor measures the temperature inside the oral cavity. The sensor is configured to be capable of wired or wireless communication with the control unit 21. 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 further configured to be capable of measuring at least one of chewing force, biting force, blood flow in the gingiva, bad breath, strength of brush pressure during brushing, or movement of the toothbrush during brushing.

[0021] The communication unit 24 is configured to be capable of communicating 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 capable of communicating via the Internet line N. The communication unit 24 may be configured to be capable of communicating with the server 30 of the interface unit 20 by wired communication or wireless communication. For example, the communication unit 24 transmits the input information I of the user acquired by the acquisition unit 23 based on the control of the control unit 21 and receives the output information O from the server 30.

[0022] 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 user's input information I and information regarding the output information O from the server 30. In one example, the display unit 25 is configured by a display. The display of the display unit 25 may be configured by a touch panel. When a part of the display unit 25 is configured by a touch panel, that part may also serve as the function of the user interface of the acquisition unit 23.

[0023] The user communicates with the server 30 by, for example, inputting a predetermined URL into the interface unit 20 or reading a QR code (registered trademark) with the interface unit 20. Communication with the server 30 may be started by the user selecting an icon displayed on the display unit 25.

[0024] 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 to be able to acquire information. 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.

[0025] The information analysis unit 33 performs various analyses and controls. The information analysis unit 33 includes an arithmetic processing unit 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 using the learning model M by machine learning. In this case, the input information I includes the oral cavity image P of the user. The learning model M is, in one example, a supervised learning model which is one of the machine learning models. In the second example, the information analysis unit 33 analyzes the input information I by referring to the correspondence table stored in the information storage unit 34. In this case, the input information I includes, instead of or in addition to the oral cavity image P, at least one of information regarding the user's lifestyle, information regarding the user's oral cavity secretions, information regarding the user's oral cavity flora, and response information to a predetermined question. The correspondence table is a table in which the input information I and the analysis information A are associated. The correspondence table may also associate the input information I and the analysis information A with the estimation information E. When the input information I further includes in addition to the oral cavity image P, in one example, the learning model M further includes another learning model that analyzes the input information I other than the oral cavity image P. The other learning model is configured to be able to output, for example, parameters for correcting the analysis information A of the oral cavity image P. In another example, the learning model M is configured as a model capable of performing multimodal learning in which both the oral cavity image P and the input information I not including the oral cavity image P are combined for learning.

[0026] 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 necessary.

[0027] An analysis of input information I including an oral cavity image P using a learning model M executed by an information analysis unit 33 will be described. The analysis steps executed by the information analysis unit 33 include a plurality of processes. The plurality of processes include a first process of detecting an oral cavity region R from a user's oral cavity image P, a second process of calculating analysis information A by analyzing the oral cavity region R included in the oral cavity image P, a third process of estimating estimated information E from the analysis information A, and a fourth process of calculating output information O corresponding to the analysis information A and the estimated information E.

[0028] The first process is executed by the information analysis unit 33 by any means. In one example, a learned model suitable for detecting a face is used to detect the oral cavity region R from the oral cavity image P of the user's oral cavity. The oral cavity region R includes a first oral cavity region R1 including the user's upper jaw teeth and a second oral cavity region R2 including the user's lower jaw teeth. The learned model for detecting a face is introduced into the information analysis unit 33 by, for example, an API.

[0029] In the first process, when the oral cavity region R cannot be obtained from the oral cavity image P, the information analysis unit 33 outputs its content to the display unit 25 of the interface unit 20 via the information output unit 32. In one example, information is output to the display unit 25 of the interface unit 20 requesting the user to re-enter the input information I including the oral cavity image P because the oral cavity region R is not included in the oral cavity image P, the oral cavity image P is blurred, or the brightness of the oral cavity image P is not appropriate and the oral cavity region R cannot be recognized. When one of the first oral cavity region R1 and the second oral cavity region R2 can be obtained and the other cannot be obtained, the information analysis unit 33 may determine that the first process is completed, or may output information requesting the user to re-enter the input information I to the interface unit 20.

[0030] In the second step, the information analysis unit 33 analyzes the oral cavity image P. The information analysis unit 33 analyzes the oral cavity image P using the learning model M stored in the information storage unit 34. The analysis information A, which is the analysis result, includes at least one of information regarding the presence or degree of interdental space, gingival recession, overgrowth, gingival inflammation, remaining abrasion, tooth grinding, dental caries, tooth hypersensitivity, bad breath, and discoloration at the first specific site within the oral cavity region R, and information regarding the condition of the teeth, tooth roots, and oral mucosa corresponding to the first specific site. The information regarding the condition of the teeth, tooth roots, and oral mucosa includes at least one of, for example, the presence or degree of tooth wear, tooth loss, tooth fracture, blood color, dryness, and texture. The texture includes a plurality of textures including, for example, a somewhat hard texture that feels firm when touched with a finger and a soft texture that feels flabby when touched with a finger. When the results of the analysis information A in the first oral cavity region R1 and the analysis information A in the second oral cavity region R2 are different, it may be preset to adopt either one of the analysis information A, or the two pieces of analysis information A may be quantified and the average thereof may be adopted, or the two pieces of analysis information A may be quantified and the analysis information A with a larger absolute value may be adopted.

[0031] In the third step, the information analysis unit 33 estimates estimated information E from the input information I and the analysis information A. The estimated information E includes information regarding at least one of the presence, degree, and occurrence probability of a predetermined state. The estimated information E includes information regarding the presence or degree of interdental spaces, gingival recession, overgrowth, gingival inflammation, remaining abrasion, tooth grinding, dental caries, tooth sensitivity, bad breath, and discoloration at a second specific site different from the first specific site, or at least one of information regarding the state of the teeth, tooth roots, and oral mucosa corresponding to the second specific site. The second specific site includes at least one of the premolars and molars in the oral cavity. The estimated information E includes at least one of information regarding the premolars and molars in the oral cavity. The estimated information E may include at least one of information regarding whether periodontal disease exists in the user's oral cavity, information regarding whether the masticatory function is normal, information regarding whether the occlusion is normal, and information regarding whether the swallowing function is normal. The information regarding whether periodontal disease exists is information regarding at least one of the size, depth, and number of so-called periodontal pockets that occur between the gingiva and the teeth. The information regarding whether the masticatory function is normal is, for example, information regarding masticatory force, the presence or absence of unilateral chewing, and chewing sounds. The information regarding whether the occlusion is normal is, for example, information regarding the occlusal force and the meshing of the upper jaw teeth and the lower jaw teeth. The information regarding whether the swallowing function is normal is, for example, information regarding the presence or absence of swallowing disorders.

[0032] In the fourth step, the information analysis unit 33 calculates output information O corresponding to at least one of the analysis information A and the estimated information E. The output information O includes at least one of information regarding the user's current oral state, information regarding prediction of the user's future oral state, information regarding oral care methods for the user, and information regarding the user's health state affected by the user's oral state.

[0033] A learning model M for analyzing the oral region R will be described. The learning model M includes a plurality of learned models. The learning model M includes a first learned model M1 that determines whether the oral region R can be evaluated and a second learned model M2 that actually evaluates the oral region R.

[0034] The first pre-trained model M1 includes at least one of a learning model M11 for determining whether the oral region R of the oral cavity image P has an analyzable image quality, a learning model M12 for determining whether overgrowth evaluation is possible, a learning model M13 for determining whether interdental space evaluation of the upper jaw teeth is possible, a learning model M14 for determining whether interdental space evaluation of the lower jaw teeth is possible, and a learning model M15 for determining whether gingival recession evaluation is possible.

[0035] The second pre-trained model M2 includes at least one of a learning model M21 for determining the presence or absence of overgrowth in the upper jaw teeth, a learning model M22 for determining the presence or absence of overgrowth in the lower jaw teeth, a learning model M23 for determining the presence or absence of interdental space in the upper jaw teeth, a learning model M24 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 recession.

[0036] The creation of the learning model M will be described. The learning model M was created based on approximately 10,000 oral cavity images P. The developer classified the oral cavity images P into learning images, verification images, and test images. The learning images are the supervised data when creating the learning model M. The verification images are images for correcting the operation of the learning model M based on the learning images. The test images are images for finally confirming the operation of the learning model M. The test images are used, for example, to confirm whether the learning model M is overfitting. The learning images account for approximately 56 percent of the whole. The verification images account for approximately 24% of the whole. The test images account for 20% of the whole. In this embodiment, the learning model M outputs the result of classification. The learning model M outputs at least one of information regarding the presence or absence or degree of interdental space, gingival recession, overgrowth, gingival inflammation, remaining abrasion, tooth grinding, dental caries, tooth sensitivity, bad breath, and staining, information regarding the state of the teeth, tooth roots, and oral mucosa corresponding to the second specific site, information regarding whether periodontal disease exists in the user's oral cavity, information regarding whether the masticatory function is normal, information regarding whether the occlusion is normal, and information regarding whether the swallowing function is normal.

[0037] Figure 2 shows an example of the oral image P of the user. The oral image P includes at least a first specific part in the oral cavity. The first specific part includes the central incisor T1, the gingiva corresponding to the central incisor T1, the lateral incisor T2, the gingiva corresponding to the lateral incisor T2, the canine tooth T3, and the gingiva corresponding to the canine tooth T3. The oral image P includes a tooth image and a gingiva image in the oral cavity. The tooth image includes a central incisor T1 image, a lateral incisor T2 image, and a canine tooth T3 image in the oral cavity. The gingiva image includes images of the gingiva corresponding to the central incisor T1, the lateral incisor T2, and the canine tooth T3. The oral image P includes at least one of the four first specific parts in the upper, lower, left, and right of the user's oral cavity.

[0038] Referring to Figure 3, the preferred range of the tooth image included in the oral image P will be described. In the determination of the presence or absence of overgrowth, it is preferable that the range from the end E1 of the central incisor T1 to the dashed line L2 is included in the oral image P. In the determination of the presence or absence of interdental space, it is preferable that the range from the end E1 of the central incisor T1 to the two-dot chain line L3 is included in the oral image P. In the determination of 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. By including the image of the central incisor T1 in this range in the oral image P, the image of the corresponding gingiva is included in the oral image P.

[0039] Each oral image P was trained by an expert in analyzing the oral cavity. Figures 4 to 9 show an example of the oral image P that has been trained. The expert in analyzing the oral cavity is, for example, a dentist, a dental hygienist, a researcher conducting research on oral conditions, or a developer developing oral care products. The expert in analyzing the oral cavity made a determination of the degree of overgrowth, a determination of the degree of gingival recession, and a determination of 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. The expert in analyzing the oral cavity made a determination based on the type of overgrowth in the determination of the degree of overgrowth. The expert in analyzing the oral cavity made a determination of multiple stages from no gingival recession to overall gingival recession in the determination of gingival recession. The expert in analyzing the oral cavity made an evaluation of multiple stages from no interdental space to severe interdental space for each of the upper jaw and the lower jaw in the determination of interdental space.

[0040] The state at the first specific site in the oral cavity is correlated with the state at a second specific site different from the first specific site in the oral cavity. In one example, in a test involving 75 women in their 40s to 70s, the state of the gums in the front teeth is highly correlated with the state of the gums in the back teeth. That is, when gum recession occurs at the first specific site, gum recession also occurs at the second specific site. The same applies to the interdental space and the degree of overgrowth.

[0041] The information analysis unit 33 outputs the analysis information A and the estimation information E in 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 estimation information E to both the information output unit 32 and the information storage unit 34.

[0042] The analysis information A and the estimation information E are associated with the output information O. The output information O will be described. The output information O includes at least one of information regarding the user's current oral state, information regarding prediction of the user's future oral state, information regarding oral care methods for the user, and information regarding the user's health state affected by the user's oral state. The information regarding the user's current oral state includes at least one of information regarding the presence or absence of overgrowth, the presence or absence of gum recession, and the presence or absence of interdental space in the entire oral cavity of the user. The information regarding the user's current oral state may further include at least one of the presence or absence of gum inflammation, the presence or absence of grinding residue, the state of the grinding residue, the presence or absence of tooth grinding, the presence or absence of hypersensitivity, and the presence or absence of bad breath.

[0043] The information regarding prediction of the user's future oral state includes an estimated image showing the user's oral state after a predetermined period has elapsed. The information regarding oral care methods for the user includes information regarding oral care supplies suitable for the user's oral state and usage methods. The information regarding the user's health state affected by the user's oral state includes, for example, information regarding periodontal disease or health states outside the oral cavity related to the oral state.

[0044] The correspondence between the analysis information A, the estimated information E, and the output information O is executed by any means by the information analysis unit 33. In the first example, it is executed by a correspondence table in which the analysis information A, the estimated information E, and the output information O are associated in advance by a researcher who conducts research on oral conditions or a developer who develops oral care products. In the second example, the correspondence between the analysis information A, the estimated information E, and the output information O is executed by a machine learning model.

[0045] 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 by the information acquisition unit 31. The information analysis unit 33 analyzes the input information I using the learning model M stored in the information storage unit 34 and calculates the analysis information A of the first specific site. The information analysis unit 33 calculates the estimated information E including the second specific site from the analysis information A. The information analysis unit 33 calculates the output information O corresponding to the analysis information A and the estimated 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 on the display unit 25.

[0046] (Modification example) The description of the embodiment is an example of a form that the oral condition evaluation system according to the present invention can take, and is not intended to limit that form. The present invention can take forms other than the embodiment, for example, modification examples of the following embodiments, and forms in which at least two modification examples that do not contradict each other are combined.

[0047] · 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 overgrowth, the degree of interdental space, and the degree of gingival recession.

[0048] ·At least one of the learned model M1 and the learned model M2 may be a model learned by unsupervised learning or reinforcement learning. At least one of each of the learning models M11 to M15 and each of the learning models M21 to M25 may be a model learned by unsupervised learning or reinforcement learning.

[0049] ·The learning model M may be a first learned model M1 that determines whether 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 learned model M2 that performs the evaluation. The learning model M may be a first learned model M1 that recognizes a set of teeth and gums in the oral image P and determines whether evaluation is possible, and a second learned model M2 that performs the evaluation. Further, the learning model M may be one that recognizes a set of teeth and gums in the oral region R respectively, and executes analysis when the majority is evaluable. Also, it may be preset to adopt the respective analysis information A of a set of teeth and gums, the respective analysis information A of a set of teeth and gums may be quantified and its average may be adopted, or the respective analysis information A of a set of teeth and gums may be quantified and the analysis information A with a large absolute value may be adopted.

[0050] ·The learning model M may be configured to display the oral image P in pseudo colors as the output information O. In one example, the area used by the learning model M for analysis in the oral image P, or the area where interdental spaces, gingival recession, and overgrowth occur, is displayed in red. The user can easily recognize the area used for analysis and the area where problems exist in the oral condition.

[0051] ·When the output information O includes information regarding the user's oral care products and usage methods, it may be configured to include purchase information for purchasing the corresponding oral care products. In one example, the purchase information is information regarding the store where the corresponding oral care products can be purchased or experienced. In another example, the purchase information is the website information on the web that sells the corresponding oral care products.

[0052] ·The input information I may be acquired via an IoT device. In one example, the input information I is acquired by connecting an IoT device to an oral care item used for brushing. In one example, the input information I includes information regarding the number of times of brushing in a day and the usage frequency of the oral care item. 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.

[0053] ·At least one of the learning model M, the correspondence table, and various information may be stored in a location 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.

[0054] The input information I may include first input information I1 including the user's current state information and second input information I2 which is the user's past state information. When the first input information I1 and the second input information I2 include the user's oral cavity image P, the oral cavity image P included in the second input information I2 is an image of the user's oral cavity before a predetermined time than when the first input information I1 is acquired. When the first input information I1 and the second input information I2 include information regarding the user's oral care, the information regarding the oral care included in the second input information I2 is information regarding the oral care before a predetermined time than when the first input information I1 is acquired. The predetermined time is, for example, a time of one month or more. In another example, the predetermined time is a time of one year or more. The second input information I2 may be transmitted to the server 30 via the communication unit 24 before the acquisition of the first input information I1 by the acquisition unit 23, or may be transmitted to the server 30 via the communication unit 24 simultaneously with the transmission of the first input information I1. 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 third input information I3 acquired before a predetermined time than the second input information I2. The information analysis unit 33 calculates analysis information A and estimation information E using at least one of the first input information I1 to the third input information I3. By adding the third input information I3, the accuracy is further improved. The interval of the predetermined time may be changed for each input information I. The input information I may further include input information I after the fourth input information.

Explanation of Signs

[0055] 10: Oral cavity state evaluation system 20: Interface unit 30: Server 31: Information acquisition unit 32: Information output unit 33: Information analysis unit 34: Information storage unit A: Analysis information E: Estimation information I: Input information O: Output information

Claims

1. an information acquisition unit that acquires an oral cavity image including at least a first specific site in the oral cavity from an interface unit as input information; an information analysis unit that analyzes a state of the first specific portion based on the input information, The information analysis unit estimates estimated information regarding a state of a second specific site in the oral cavity different from the first specific site from the analysis information of the state of the first specific site and the input information. Oral condition assessment system.

2. The oral cavity image includes an image of teeth and gums in the oral cavity. The oral cavity condition evaluation system according to claim 1 .

3. The tooth images include central incisor images, lateral incisor images, and canine images in the oral cavity. The oral cavity condition evaluation system according to claim 2 .

4. The second specific site includes at least one of a premolar and a molar in the oral cavity; The estimated information includes at least one of information regarding premolars and molars in the oral cavity. The oral cavity condition evaluation system according to any one of claims 1 to 3.

5. The estimated information on the second specific site includes at least one of information on the presence or degree of interdental gaps, gingival recession, crowding, gingival inflammation, unbrushed areas, bruxism, caries, hypersensitivity, halitosis, and staining in the second specific site, information on the presence or degree of periodontal disease, information on whether or not masticatory function is normal, information on whether or not occlusion is normal, information on whether or not swallowing function is normal, and information on the condition of the teeth, gums, and oral mucosa corresponding to the second specific site. The oral cavity condition evaluation system according to any one of claims 1 to 4.

6. Further, an information storage unit stores a learning model that has been previously trained on the oral cavity image in order to evaluate the state of the oral cavity, The information analysis unit analyzes the input information using the learning model. The oral cavity condition evaluation system according to any one of claims 1 to 5.

7. the information analysis unit calculates the analysis information of the state of the first specific site by analyzing the input information using a learning model that has previously learned the oral cavity image in order to evaluate the state of the oral cavity; The learning model includes a first trained model that determines whether or not an oral cavity area detected from the oral cavity image included in the input information can be evaluated, and a second trained model that actually evaluates the oral cavity area. The oral cavity condition evaluation system according to any one of claims 1 to 5.

8. The first trained model includes at least one of a learning model for determining whether the image quality of the oral cavity region is analyzable, a learning model for determining whether crowding evaluation is possible, a learning model for determining whether interdental space evaluation is possible for upper jaw teeth, a learning model for determining whether interdental space evaluation is possible for lower jaw teeth, and a learning model for determining whether gingival recession evaluation is possible. The oral cavity condition evaluation system according to claim 7.

9. The second trained model includes at least one of a learning model for determining the presence or absence of crowding in the upper jaw teeth, a learning model for determining the presence or absence of crowding in the lower jaw teeth, a learning model for determining the presence or absence of interdental spaces in the upper jaw teeth, a learning model for determining the presence or absence of interdental spaces in the lower jaw teeth, and a learning model for determining the presence or absence of interdental recession. The oral cavity condition evaluation system according to claim 7 or 8.

10. The input information further includes at least one of information on the user's lifestyle, information on the user's oral secretions, information on the user's oral bacterial flora, information on the user's attributes, and information obtained by a sensor that detects the condition of the oral cavity. The oral cavity condition evaluation system according to any one of claims 1 to 9.

11. The information analysis unit analyzes a state of the first specific part based on the oral cavity image included in the input information and the at least one piece of information. The oral cavity condition evaluation system according to claim 10.

12. an information output unit that outputs information corresponding to the analysis information and the estimation information as output information, The information output unit outputs the output information to at least the interface unit. The oral cavity condition evaluation system according to any one of claims 1 to 11.

13. The output information includes at least one of information on the user's current oral condition, information on a prediction of the user's future oral condition, information on an oral care method for the user, and information on a health condition of the user that is affected by the user's oral condition. The oral cavity condition evaluation system according to claim 12.

14. the information analysis unit analyzes a state of the first specific part based on an oral cavity area detected from the oral cavity image included in the input information; The information analysis unit calculates the analysis information of the state of the first specific part based on at least one of analysis information of a first oral cavity region including the user's upper jaw teeth in the oral cavity region and analysis information of a second oral cavity region including the user's lower jaw teeth in the oral cavity region. The oral cavity condition evaluation system according to any one of claims 1 to 13.

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