Oral condition 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 cavity condition evaluation system for evaluating the oral cavity condition of a user. [Background technology]

[0002] There is known a system that analyzes the condition of the oral cavity from an image including the entire oral cavity and assists the judgment of a specialist such as a dentist. Patent Document 1 discloses a dental analysis system that extracts lesions from an X-ray image that captures the entire oral cavity of a user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-208831 Summary of the Invention [Problem to be solved by the invention]

[0004] It is difficult to obtain information about the overall condition of the oral cavity without the use of experts and examination tools or devices such as probes. Therefore, there is a need for a simple means for users to understand their own oral condition. The present invention is an invention for solving the above-mentioned problems, and aims to provide an oral cavity condition evaluation system that can evaluate the oral cavity condition using simple means. [Means for solving the problem]

[0005] The oral cavity condition evaluation system of the present invention comprises an information acquisition unit that acquires an oral cavity image including at least a first specific site in the oral cavity as input information from an interface unit, and an information analysis unit that analyzes the state of the first specific site based on the input information, and the information analysis unit estimates estimated information regarding the state of a second specific site in the oral cavity that is 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 cavity condition evaluation system of the present invention, it is possible to provide an oral cavity condition evaluation system that can evaluate the oral cavity condition using simple means. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram showing the configuration of an oral cavity condition evaluation system according to an embodiment. [Figure 2] FIG. 10 is a diagram showing an oral cavity image as an example of input information. [Figure 3] A partially enlarged schematic diagram of the oral cavity image in Figure 2. [Figure 4] FIG. 10 is a diagram showing an example of an oral cavity condition when crowding occurs. [Figure 5] FIG. 10 is a diagram showing an example of an oral cavity condition when there is no crowding. [Figure 6] FIG. 10 is a diagram showing an example of an oral cavity condition when there is an interdental gap. [Figure 7] FIG. 10 is a diagram showing an example of the state of the oral cavity when there is no interdental space. [Figure 8] FIG. 10 is a diagram showing an example of an oral cavity condition when gingival recession is present. [Figure 9] FIG. 10 is a diagram showing an example of an oral cavity condition when there is no gingival recession. DETAILED DESCRIPTION OF THE INVENTION

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

[0009] (2) According to one example of the oral cavity condition evaluation system, the oral cavity image includes an image of teeth and gums in the oral cavity. According to the above-described oral cavity condition evaluation system, the oral cavity image includes images of appropriate areas within the oral cavity, so that the oral cavity condition can be appropriately evaluated.

[0010] (3) According to one example of the oral cavity condition evaluation system, the tooth images include images of central incisors, lateral incisors, and canine teeth in the oral cavity. According to the above-described oral cavity condition evaluation system, the oral cavity image includes images of appropriate teeth in the oral cavity, so that the oral cavity condition can be appropriately evaluated.

[0011] (4) According to one 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 estimated information includes at least one of information regarding the premolar and a molar in the oral cavity. According to the above-described oral cavity condition evaluation system, at least one of information relating to premolars and molars in the oral cavity is estimated, and therefore the oral cavity condition can be appropriately evaluated.

[0012] (5) According to one example of the oral condition evaluation system, the estimated information includes at least one of information regarding the presence or degree of interdental spaces, gingival recession, crowding, gingival inflammation, missed spots, teeth grinding, caries, hypersensitivity, bad breath, and staining in the second specific area, information regarding the presence or degree of periodontal disease, information regarding whether chewing function is normal, information regarding whether occlusion is normal, information regarding whether swallowing function is normal, and information regarding the condition of the teeth, gums, and oral mucosa corresponding to the second specific area. The above-described oral cavity condition evaluation system can estimate the condition of at least one of the teeth and gums at the second specific region, thereby enabling an appropriate evaluation of the oral cavity condition.

[0013] (6) According to one example of the oral condition evaluation system, the system further includes an information storage unit that stores a learning model that has been previously trained on the oral images to evaluate the condition of the oral cavity, and the information analysis unit analyzes the input information using the learning model. According to the oral cavity condition evaluation system, the estimated information is estimated using a learning model, which allows for more appropriate evaluation of the oral cavity condition.

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

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

[0016] (9) According to one 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 a prediction of the user's future oral condition, information regarding oral care methods for the user, and information regarding the user's health condition that is affected by the user's oral condition. According to the above-described oral cavity condition evaluation system, the user can appropriately recognize the output information regarding the oral cavity condition.

[0017] (Embodiment) An oral condition evaluation system 10 according to this embodiment will be described with reference to FIGS. 1 to 9. The oral condition evaluation system 10 analyzes input information I and estimates estimated information E based on analysis information A obtained from the input information I. The oral condition evaluation system 10 may also calculate output information O corresponding to the analysis information A and the estimated information E and output the output information O to a predetermined device. The main component of the oral condition evaluation system 10 is a server 30. Preferably, the oral condition evaluation system 10 includes an interface unit 20 for exchanging information with the server 30. In one example, the interface unit 20 is a smart device that can be carried by a 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 a user's residence, a store, or a dental clinic. The store may be a store selling oral care items or other products. The interface unit 20 and the server 30 are configured to be able to communicate with each other, for example, via an internet connection N. In another example, the interface unit 20 and the server 30 are integrated into one unit.

[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 a processing unit that executes a predetermined control program. The processing unit 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 nonvolatile memory and a volatile memory. The nonvolatile 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 input information I from a user. The acquisition unit 23 has any configuration for acquiring the user's input information I. In a first example, the acquisition unit 23 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 videos. The oral cavity image P includes a 3D image having depth information or a panoramic image formed by stitching together multiple images. The camera may be a camera mounted on a smart device or a separate, independent camera. The independent camera may be, for example, a pen-shaped camera with a camera attached to a tip suitable for capturing images of the oral cavity or a camera capable of capturing images in a 360-degree range. The independent camera is configured to be able to communicate with the control unit 21 via wired or wireless communication. The oral cavity image P captured by the independent camera is transmitted to the control unit 21 via wired or wireless communication. In a second example, the acquisition unit 23 is a user interface configured to allow the user to input or select characters, etc. The user's input information I may further include at least one of information on the user's lifestyle habits, information on the user's oral secretions, information on the user's oral bacterial flora, information on answers to predetermined questions, and information on the user's attributes. The information about the user's lifestyle habits includes information about the user's diet, oral care behavior, and wake-up or sleep time. The information about the user's oral secretions includes information about the amount, viscosity, hydrogen ion concentration, amount of antibacterial components, and amount of components related to tooth remineralization. The information about the user's oral flora includes information about the amount and type of major bacteria present in saliva, dental plaque, or mucosa, and information about the type and amount of antibodies to the major bacteria. The information about the user's oral secretions and the information about the user's oral flora may be the results of a medical interview with the user or may be information obtained by testing the oral secretions present in the user's oral cavity using a predetermined means. The answer information to the predetermined questions includes questions about the user's gum condition, preferred brushing method for oral care, number of times per day, time spent brushing per session, time of day brushing, frequency of use of oral care items, and whether or not the user wears dentures. The questions about the user's gum condition include, for example, whether or not the gums bleed when brushing or eating.The information about the user's attributes includes, for example, the user's age, gender, height, weight, dominant hand, and medical history. In a third example, the acquisition unit 23 is a sensor that detects the condition of the oral cavity. The sensor is, for example, a fluorescent sensor and a temperature sensor. The fluorescent sensor irradiates light of a predetermined wavelength and measures the amount of light to measure 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 in the oral cavity. The sensor is configured to be able to communicate with the control unit 21 via wired or wireless communication. Information obtained by the sensor is transmitted to the control unit 21 via wired or wireless communication. In this embodiment, the acquisition unit 23 may be configured by combining two or more of the first to third examples. The sensor may be configured to further measure at least one of masticatory force, occlusal force, blood flow in the gums, bad breath, strength of brushing pressure during brushing, or toothbrush movement during brushing.

[0021] The communication unit 24 is configured to be able to communicate with the outside of the interface unit 20 under the control of the control unit 21. The communication unit 24 is configured to be able to communicate via an 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. The communication unit 24 transmits, for example, user input information I acquired by the acquisition unit 23 under the control of the control unit 21, and receives output information O from the server 30.

[0022] The display unit 25 displays various information under the control of the control unit 21. The various information is, for example, information relating to input information I from the user and information relating to output information O from the server 30. In one example, the display unit 25 is configured with a display. The display of the display unit 25 may be configured with a touch panel. When a part of the display unit 25 is configured with a touch panel, that part may also function as a user interface for 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. The user may also start communication with the server 30 by 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 a calculation processing unit that executes a predetermined control program. The calculation processing unit includes, for example, a CPU or an MPU. The information analysis unit 33 is configured to analyze input information I from a user. In a first example, the information analysis unit 33 analyzes the input information I using a learning model M based on machine learning. In this case, the input information I includes an oral cavity image P of the user. The learning model M is, for example, a supervised learning model, which is a machine learning model. In a second example, the information analysis unit 33 analyzes the input information I by referring to a correspondence table stored in the information storage unit 34. In this case, the input information I includes at least one of information on the user's lifestyle habits, information on the user's oral secretions, information on the user's oral bacterial flora, and information on answers to predetermined questions, instead of or in addition to the oral cavity image P. The correspondence table associates the input information I with the analysis information A. The correspondence table may also associate the input information I, the analysis information A, and the estimated information E. In a case where input information I is included 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 learning is performed by combining both the oral cavity image P and the input information I that does not include the oral cavity image P.

[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 needed.

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

[0028] The first step is performed by the information analysis unit 33 by any means. In one example, an oral cavity region R is detected from an oral cavity image P of a user's oral cavity using a trained model suitable for face detection. 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 trained model for face detection is introduced into the information analysis unit 33 by, for example, an API.

[0029] In the first step, if the oral cavity region R cannot be acquired from the oral cavity image P, the information analysis unit 33 outputs the content to the display unit 25 of the interface unit 20 via the information output unit 32. In one example, the oral cavity region R cannot be recognized because the oral cavity image P does not include the oral cavity region R, the oral cavity image P is blurred, or the brightness of the oral cavity image P is inappropriate. Therefore, information requesting the user to re-input the input information I including the oral cavity image P is output to the display unit 25 of the interface unit 20. In a case where one of the first oral cavity region R1 and the second oral cavity region R2 can be acquired but the other cannot, the information analysis unit 33 may determine that the first step is complete and may output information requesting the user to re-input 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 result, analysis information A, includes at least one of information regarding the presence or degree of interdental gaps, gingival recession, crowding, gingival inflammation, incomplete brushing, teeth grinding, caries, hypersensitivity, bad breath, and staining in a first specific region within the oral cavity region R, and information regarding the condition of the teeth, gums, and oral mucosa corresponding to the first specific region. The information regarding the condition of the teeth, gums, and oral mucosa includes at least one of tooth wear, tooth loss, the presence or degree of tooth fracture, blood color, dryness, and texture. The texture includes a plurality of textures, for example, a hard texture that feels firm when touched with a finger, and a soft texture that feels squishy when touched with a finger. If 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 one of the analysis information A, or the two pieces of analysis information A may be quantified and their average may be adopted, or the two pieces of analysis information A may be quantified and the analysis information A with the 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 on at least one of the presence or absence, degree, and occurrence probability of a predetermined condition. The estimated information E includes at least one of information on the presence or degree of interdental gaps, gingival recession, crowding, gingival inflammation, incomplete brushing, teeth grinding, caries, hypersensitivity, halitosis, and staining in a second specific region different from the first specific region, or information on the condition of the teeth, gums, and oral mucosa corresponding to the second specific region. The second specific region includes at least one of premolars and molars in the oral cavity. The estimated information E includes at least one of information on premolars and molars in the oral cavity. The estimated information E may include at least one of information on whether periodontal disease is present in the user's oral cavity, information on whether masticatory function is normal, information on whether occlusion is normal, and information on whether swallowing function is normal. The information regarding whether or not periodontal disease exists is information regarding at least one of the size, depth, and number of so-called periodontal pockets formed between the gums and the teeth. The information regarding whether or not masticatory function is normal is, for example, information regarding masticatory force, the presence or absence of biased mastication, and masticatory sounds. The information regarding whether or not occlusion is normal is, for example, information regarding occlusion force and the occlusion between the upper and lower jaw teeth. The information regarding whether or not swallowing function is normal is, for example, information regarding the presence or absence of swallowing disorders.

[0032] In a 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 on the user's current oral condition, information on a predicted future oral condition of the user, information on an oral care method for the user, and information on the user's health condition affected by the user's oral condition.

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

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

[0035] The second trained model M2 includes at least one of a learning model M21 that determines whether or not there is crowding in the upper jaw teeth, a learning model M22 that determines whether or not there is crowding in the lower jaw teeth, a learning model M23 that determines whether or not there is interdental space in the upper jaw teeth, a learning model M24 that determines whether or not there is interdental space in the lower jaw teeth, and a learning model M25 that determines whether or not there is 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 supervised data used when creating the learning model M. The verification images are images used to correct the operation of the learning model M based on the learning images. The test images are images used to finally confirm the operation of the learning model M. The test images are used, for example, to confirm whether the learning model M has overfitted. The learning images make up approximately 56 percent of the total. The verification images make up approximately 24% of the total. The test images make up 20% of the total. In this embodiment, the learning model M outputs the results of class classification. The learning model M outputs at least one of the following information: information regarding the presence or degree of interdental gaps, gingival recession, crowding, gingival inflammation, incomplete brushing, teeth grinding, caries, hypersensitivity, bad breath, and staining; information regarding the condition of the teeth, gums, and oral mucosa corresponding to the second specific site; information regarding whether periodontal disease is present in the user's oral cavity; information regarding whether chewing function is normal; information regarding whether occlusion is normal; and information regarding whether swallowing function is normal.

[0037] FIG. 2 shows an example of an oral cavity image P of a user. The oral cavity image P includes at least a first specific region in the oral cavity. The first specific region includes a central incisor T1, a gum corresponding to the central incisor T1, a lateral incisor T2, a gum corresponding to the lateral incisor T2, a canine T3, and a gum corresponding to the canine T3. The oral cavity image P includes images of teeth and gums in the oral cavity. The tooth images include images of the central incisor T1, a lateral incisor T2, and a canine T3 in the oral cavity. The gum images include images of the gums corresponding to the central incisor T1, the lateral incisor T2, and the canine T3. The oral cavity image P includes at least one first specific region out of four first specific regions in the oral cavity, i.e., top, bottom, left, and right, within the user's oral cavity.

[0038] With reference to FIG. 3, a preferred range of tooth images to be included in the oral cavity image P will be described. When determining whether crowding is present, it is preferable that the range from the end E1 of the central incisor T1 to the dashed line L2 be included in the oral cavity image P. When determining whether an interdental space is present, it is preferable that the range from the end E1 of the central incisor T1 to the two-dot chain line L3 be included in the oral cavity image P. When determining whether gingival recession is present, it is preferable that the range from the end E1 of the central incisor T1 to the solid line L1 be included in the oral cavity image P. By including an image of the central incisor T1 in this range in the oral cavity image P, an image of the corresponding gums will be included in the oral cavity image P.

[0039] Each oral image P was supervised by an oral cavity analysis expert. Figures 4 to 9 show examples of supervised oral cavity images P. The oral cavity analysis expert may be, for example, a dentist, dental hygienist, a researcher studying oral conditions, or a developer developing oral care products. The oral cavity analysis expert assessed 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 cavity image P. The oral cavity analysis expert assessed the degree of crowding based on the type of crowding. The oral cavity analysis expert assessed gingival recession using multiple levels, ranging from no gingival recession to total gingival recession. The oral cavity analysis expert assessed interdental space using multiple levels, ranging from no interdental space to severe interdental space, for both the upper and lower jaws.

[0040] The condition of a first specific site in the oral cavity correlates with the condition of a second specific site different from the first specific site in the oral cavity. For example, in a study of 75 women in their 40s to 70s, the condition of the gums on the front teeth was highly correlated with the condition of the gums on the back teeth. In other words, when gingival recession occurs in the first specific site, gingival recession also occurs in the second specific site. The same is true for the degree of interdental space and crowding.

[0041] The information analysis unit 33 outputs the analysis information A and the estimated information E to a predetermined configuration. In a first example, the information analysis unit 33 outputs to the information output unit 32. In a second example, the information analysis unit 33 outputs to the information storage unit 34. In a 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.

[0042] The analysis information A and the estimation information E are associated with output information O. The output information O will be described. The output information O 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 the user's health condition affected by the user's oral condition. The information on the user's current oral condition includes at least one of information on the presence or absence of crowding in the user's entire oral cavity, the presence or absence of gingival recession, and the presence or absence of interdental spaces. The information on the user's current oral condition may further include at least one of the presence or absence of gingival inflammation, the presence or absence of incompletely brushed areas, the state of incompletely brushed areas, the presence or absence of teeth grinding, the presence or absence of hypersensitivity, and the presence or absence of bad breath.

[0043] The information regarding the user's future oral health prediction includes an estimated image showing the user's oral health condition after a predetermined period of time has passed. The information regarding the user's oral care method includes information regarding oral care products suitable for the user's oral health condition and how to use them. The information regarding the user's health condition affected by the user's oral health condition includes, for example, information regarding periodontal disease or non-oral health conditions related to the oral health condition.

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

[0045] The operation of the oral condition evaluation system 10 of this embodiment will be described. The user inputs 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 using 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 to calculate analyzed information A of the first specific region. The information analysis unit 33 calculates estimated information E including the second specific region from the analyzed information A. The information analysis unit 33 calculates output information O corresponding to the analyzed information A and 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 via the display on the display unit 25.

[0046] (Variation) The description of the embodiments is merely an example of possible forms of the oral condition evaluation system of the present invention, and is not intended to limit the forms. In addition to the embodiments, the present invention may also take the form of, for example, modified examples of the embodiments shown below, or a combination of at least two mutually consistent modified examples.

[0047] The learning model M may be configured to output the results of the 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.

[0048] 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 the trained models M11 to M15 and the trained models M21 to M25 may be a model trained by unsupervised learning or reinforcement learning.

[0049] The learning model M may be a first trained model M1 that determines whether evaluation is possible and a second trained model M2 that performs the evaluation without distinguishing between a first oral cavity region R1 and a second oral cavity region R2 in the oral cavity image P. The learning model M may be a first trained model M1 that recognizes a set of teeth and gums in the oral cavity image P and determines whether evaluation is possible and a second trained model M2 that performs the evaluation. The learning model M may also be a learning model M that recognizes each set of teeth and gums in the oral cavity region R and performs analysis if a majority of the teeth and gums are evaluable. The learning model M may also be preset to adopt analysis information A for each set of teeth and gums, or may digitize the analysis information A for each set of teeth and gums and adopt the average of the digits, or may digitize the analysis information A for each set of teeth and gums and adopt the analysis information A with the largest absolute value.

[0050] The learning model M may be configured to display the oral cavity image P as output information O in false colors. In one example, the areas of the oral cavity image P that the learning model M used for analysis, or areas where interdental spaces, gingival recession, and crowding occur, are displayed in red. The user can easily recognize the areas used for analysis and areas where problems with the oral condition exist.

[0051] When the output information O includes information about the user's oral care product and usage method, it may be configured to include purchase information for purchasing the oral care product. In one example, the purchase information is information about a store where the oral care product can be purchased or tried out. In another example, the purchase information is information about a website selling the oral care product.

[0052] The input information I may be acquired via an IoT device. In one example, the input information I is acquired by connecting the IoT device to an oral care item used for brushing. In one example, the input information I includes information on the number of times teeth are brushed per day and the frequency of use of the oral care item. The IoT device may transmit the input information I to the acquisition unit 23 or 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, they are stored in a storage unit provided in the interface unit 20. In another example, they are stored in a database configured in an external environment.

[0054] The input information I may include first input information I1 including the user's current condition information and second input information I2 including the user's past condition information. When the first input information I1 and the second input information I2 include an image P of the user's oral cavity, the image P included in the second input information I2 is an image of the user's oral cavity taken a predetermined time before the first input information I1 was acquired. When the first input information I1 and the second input information I2 include information about the user's oral care, the information about the oral care included in the second input information I2 is information about the oral care taken a predetermined time before the first input information I1 was acquired. In one example, the predetermined time is 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, 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 a predetermined time before the second input information I2. The information analysis unit 33 calculates the analysis information A and the estimated information E using at least one of the first input information I1 to the third input information I3. The addition of the third input information I3 further improves accuracy. The predetermined time interval may be changed for each piece of input information I. The input information I may further include fourth and subsequent pieces of input information I. [Explanation of symbols]

[0055] 10: Oral condition evaluation system 20: Interface section 30: Server 31: Information acquisition department 32: Information output section 33: Information analysis department 34: Information storage section A: Analysis information E: Estimated 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 the 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 that is 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 within 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 spaces, gingival recession, crowding, gingival inflammation, incomplete brushing, teeth grinding, caries, hypersensitivity, halitosis, and staining at the second specific site, information on the presence or absence of periodontal disease, information on the normality of masticatory function, information on the normality of occlusion, information on the normality of swallowing function, and information on the state 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. An information storage unit that stores a learning model that has previously learned 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 region 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 an oral cavity region 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 region. 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 for upper jaw teeth is possible, a learning model for determining whether interdental space evaluation for lower jaw teeth is possible, 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 training model for determining the presence or absence of crowding in upper jaw teeth, a training model for determining the presence or absence of crowding in lower jaw teeth, a training model for determining the presence or absence of interdental spaces in upper jaw teeth, a training model for determining the presence or absence of interdental spaces in lower jaw teeth, and a training 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 the 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 about the user's current oral condition, information about a prediction of the user's future oral condition, information about an oral care method for the user, and information about the user's health condition 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 the 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 condition 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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