JP2023065876A - Mobile-based self oral cavity medical examination apparatus - Google Patents

Abstract

To provide a mobile-based self oral cavity medical examination apparatus which allows a user to easily perform self oral cavity medical examination to inspect an oral cavity state and manage even the oral cavities of his/her family by using a mobile application.SOLUTION: A mobile-based self oral cavity medical examination apparatus provides a corrected oral cavity image after correcting an oral cavity image of a user by a user terminal, and requests oral cavity medical examination diagnosis. An oral cavity management service server transfers the oral cavity image provided from the user terminal, requests the oral cavity medical examination diagnosis and transfers an oral cavity medical examination diagnosis result to a user. An oral cavity health management server diagnoses the transferred oral cavity image to generate oral cavity health information, encrypts the generated oral cavity health information, and transfers the information as the oral cavity medical examination diagnosis result to the oral cavity management service server to enable the oral cavity diagnosis in real time using the oral cavity image. Also, oral cavity management of his/her family can be integrally performed by using a mobile-based oral cavity medical examination application.SELECTED DRAWING: Figure 1

Description

TECHNICAL FIELD The present invention relates to a mobile-based self-oral examination device, and more particularly, a mobile device that enables a user to easily perform self-oral examination, check the oral condition, and manage the oral cavity of family members using a mobile application. It relates to a self-oral examination device based on.

Appropriate preventive treatment and maintenance of oral diseases can provide a high preventive effect, but many patients with oral diseases do not ask doctors for diagnosis or treatment unless they have oral health problems. It is a fact that recognition is insufficient for oral prevention.

In addition, in predicting the current oral condition of the patient, it is necessary to have highly accurate examination and diagnosis results and objective numerical information based on them, not the subjective opinion of the dentist. There is no system for objective calculation.

Diseases of the oral cavity broadly mean tooth decay (tooth decay) and periodontal disease. In particular, periodontal disease, which means periodontitis and gingivitis, does not cause pain in the early stages and progresses chronically. situation.

Therefore, various methods for promoting oral health and preventing oral diseases have been researched and proposed based on pre-diagnosis of such oral diseases. Conventionally proposed techniques are disclosed in Patent Documents 1 to 3 below.

In Patent Document 1, user condition information including information on at least one of the user's gender, age, periodontal disease, and goal setting information, and treatment information at the user's member medical institution are input from the user terminal. be. At the same time, information on oral care products is input from an oral care product member store. Thereafter, information on specific oral care products included in a specific category corresponding to the user's condition information and treatment information is provided to the user terminal to provide oral care services suitable for the user.

In addition, Patent Document 2 examines the patient's oral condition, converts the oral examination index calculation reference data including the patient's oral examination data, residence and age information into big data, analyzes it, and presents the patient Judge the impact of the oral health examination index calculation standard data on oral health according to the place of residence and age of the patient. Then, a weight is given to the oral examination index calculation reference data to quantify the reference data for oral examination index calculation, and oral health is managed.

In addition, Patent Document 3 collects user personal information, user's oral hygiene behavior data and oral interview data, and oral examination data generated by the user visiting a specialized medical institution. Each piece of information collected in this way is integrated and analyzed to evaluate the user's degree of risk of oral disease and provide services suitable for the individual.

However, the conventional oral disease management system as described above analyzes the user's personal information, oral data, interview data, and oral examination data generated by a specialized medical institution to generate oral disease management information. However, it is inferior in real-time performance, and since it is not a method for analyzing the current user's oral cavity image, there is a problem that the accuracy of oral disease analysis is inferior.

In addition, it is difficult for the conventional technology to allow the user to recognize his or her periodontal disease condition in real time.

Furthermore, the prior art fails to manage family oral care in an integrated manner.

Korean Patent Publication No. 10-2017-0050467 Korea registered patent 10-1868979 Korea registered patent 10-1788030

Therefore, the object of the present invention is to solve the problems of the conventional technology as described above, and use a mobile application to easily perform a self-oral examination to check the oral condition of the user, and also to check the oral cavity of the family. To provide a controllable mobile-based self-oral examination device.

In order to achieve the above objects, the mobile-based self-oral examination device according to the present invention corrects a user's oral image, provides the corrected oral image, and provides a user terminal requesting an oral examination diagnosis. , an oral care service server for transferring the corrected oral image provided from the user terminal, requesting an oral examination diagnosis, and transferring an oral examination diagnosis result to the user; an oral health management server that diagnoses an oral image, generates oral health information, encrypts the generated oral health information, and transfers the result to the oral health management service server as an oral examination diagnosis result. .

In the above, the user terminal, the oral care service server, and the oral health care server transmit and receive encrypted oral examination diagnosis results, thereby improving security even when materials are leaked by hacking.

In addition, when transferring the oral examination diagnosis result, the oral health management server limits the authority of the access target by using an access token that uses a user terminal ID and user information, thereby improving security. improves.

The user terminal includes a photograph input unit for inputting a photograph of a photographed oral cavity image as an image for oral health examination, a photograph correcting unit for correcting the oral cavity image input by the photograph input unit to enhance clarity, and The oral cavity image corrected by the photo correction unit is transferred to the oral cavity management service server to request oral health examination, the received oral health examination result is displayed on the screen, and in response to the user's key input request, and an oral care service processor for servicing hospital appointments or family oral care.

Further, the user terminal includes an inquiry response unit that generates inquiry response information using the user's response information to the oral examination inquiry.

When the user selects simple examination from the examination mode, the oral care service processing unit requests an oral examination diagnosis using only the oral cavity image corrected by the photo correcting unit and the user's personal information. When general examination is selected from the mode, an oral examination diagnosis is requested using the oral cavity image corrected by the photograph correction unit, inquiry response information generated by the inquiry response unit, and user's personal information.

The oral health management server learns the received oral photos with a CNN (Convolutional Neural Network) algorithm, which is an artificial intelligence algorithm, determines whether the photos can be analyzed, whether correction can be performed, and whether the teeth can be extracted. , Tooth caries state information and prosthesis state information are obtained by analyzing by individual detection (Object Detection), and the obtained tooth caries state information and the prosthesis state information are analyzed by an ANN (Artificial Neural Network) algorithm. An oral health calculation unit that learns and determines an oral health condition, and the oral health information determined by the oral health calculation unit are generated as the oral examination diagnosis result, and the generated oral examination diagnosis result is encrypted. , and an oral health information providing unit for transmitting to the user terminal.

The oral health calculation unit learns the photo registration unit that registers the oral photo transferred from the user terminal as an oral examination diagnosis target, and the image registered in the photo registration unit by the CNN algorithm, and obtains the photo. Determine whether analysis is possible, whether correction is possible, and whether tooth extraction is possible, analyze the determined result information by individual detection, acquire the dental caries state information and the prosthesis state information, and obtain the orthodontic information and tooth extraction information. and an oral diagnosis unit that learns the dental caries status information and the prosthesis status information by an ANN algorithm to determine oral health status.

According to the present invention, the user can diagnose the oral condition in real time by simply uploading the oral cavity photograph using the mobile application, and provide the diagnosis result, so that the user can self-diagnose oral health in real time. It can be performed.

In addition, according to the present invention, a mobile application can be used to integrally manage the oral health of a family member, and to conveniently make a hospital appointment.

1 is an overall block diagram of a mobile-based self-oral examination device according to the present invention; FIG. 2 is a configuration diagram of a user terminal in FIG. 1; FIG. FIG. 2 is a configuration diagram of an oral health calculation unit in FIG. 1; FIG. 4 is a diagram showing an artificial intelligence algorithm for analyzing oral cavity images in the present invention; FIG. 4 is a diagram showing an artificial intelligence algorithm for analyzing oral cavity images in the present invention; FIG. 4 is a diagram showing an artificial intelligence algorithm for analyzing oral cavity images in the present invention; FIG. 4 is a diagram showing an artificial intelligence algorithm for analyzing oral cavity images in the present invention; FIG. 4 is a diagram showing the process of self-oral examination, hospital appointment, and family health management using a mobile application in the present invention; FIG. 4 is a diagram showing the process of self-oral examination, hospital appointment, and family health management using a mobile application in the present invention; FIG. 4 is a diagram showing the process of self-oral examination, hospital appointment, and family health management using a mobile application in the present invention; FIG. 4 is a diagram showing the process of self-oral examination, hospital appointment, and family health management using a mobile application in the present invention; FIG. 4 is a diagram showing the process of self-oral examination, hospital appointment, and family health management using a mobile application in the present invention;

Hereinafter, a mobile-based self-oral examination device according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

The terms and words used in the invention described below should not be construed as being limited to their ordinary or lexical meanings, and the inventors have used them in order to best describe their invention. , should be analyzed into meanings and concepts that match the technical idea of the present invention in accordance with the principle that terms can be properly defined in terms of concepts.

Therefore, the embodiments described in this specification and the configurations shown in the drawings are merely preferred embodiments of the present invention, and do not represent all the technical ideas of the present invention. It should be understood that there may be various alternative equivalents and alternatives.

FIG. 1 is an overall block diagram of a mobile-based self-oral examination device according to the present invention, which includes a user terminal 100, an oral care service server 200, and an oral health care server 300. As shown in FIG.

The user terminal 100, the oral care service server 200, and the oral health care server 300 transmit and receive encrypted oral examination diagnosis results, thereby improving security even when materials are leaked by hacking.

In addition, the oral health management server 300 uses a web access token that uses a user terminal ID and user information to limit the authority of an access target when transferring the oral examination diagnosis result, thereby improving security. can be improved.

The user terminal 100 and the oral care service server 200 are connected by various wired and wireless networks, and a real-time communication interface is performed. The reason why a plurality of servers such as the oral care service server 200 and the oral health care server 300 are implemented is to improve security of diagnosis results and prevent personal information from being leaked. In fact, health management and personal information are all managed by the oral health management server 300, and information is transferred through web tokens to improve security. In order to ensure prompt service, only services such as

After correcting the user's oral cavity image, the user terminal 100 serves to provide the corrected oral cavity image and request an oral examination diagnosis. Here, the user uses the user terminal 100 to provide user personal information, general data such as interview data, and the like. There are simple examinations and general examinations in the form of oral examinations. In the case of simple examinations, it is sufficient to simply provide the user's personal information (device ID, user information) and oral images. Information, interview data, and oral images may be provided.

The user receives the results of the oral examination through the user terminal 100, thereby recognizing his/her oral health condition. to manage oral health. Such a user terminal 100 can be implemented as a mobile device such as a smart phone and a smart pad, a personal computer and a notebook computer that can connect to the Internet, etc. In the present invention, a smart phone using a mobile application is implemented as an embodiment. Assume that

Such a user terminal 100 includes a photo input unit 101 for inputting a photograph of a photographed oral cavity image as an image for oral health examination, and correcting the oral cavity image input by the photo input unit 101 to increase clarity. The photo correcting unit 102 and the oral image corrected by the photo correcting unit 102 are transferred to the oral care service server 200 to request an oral health examination, and the received oral health examination result is displayed on the screen, and the user's and an oral care service processor 104 for servicing hospital appointments or family oral care in response to key input requests.

Furthermore, the user terminal 100 includes an inquiry response unit 103 that generates inquiry response information using the user's response information to the oral examination inquiry.

When the user selects simple examination from the examination mode, the oral care service processing unit 104 requests oral examination using only the oral cavity image corrected by the photo correcting unit 102 and the user's personal information. If general examination is selected, the oral examination is requested using the oral cavity image corrected by the photograph correction unit 102, the inquiry response information generated by the inquiry response unit 103, and the user's personal information. Here, user personal information includes user information and device ID information. Such user personal information can later be used as security web token (access token) issuance information when transferring oral examination result information.

The oral cavity management service server 200 transfers the oral cavity image provided from the user terminal 100, requests the oral health examination diagnosis result, and transfers the oral health examination diagnosis result to the user.

The oral health management server 300 diagnoses the oral image transferred from the oral management service server 200, generates oral health information, encrypts the generated oral health information, and outputs it to the oral health management service server as an oral examination diagnosis result. 200.

The oral health management server 300 learns the received oral photos with a CNN (Convolutional Neural Network) algorithm, which is an artificial intelligence algorithm, determines whether the photos can be analyzed, whether correction can be performed, and whether the tooth can be extracted. , Tooth caries state information and prosthesis state information are acquired by analyzing by individual detection (Object Detection), and the acquired tooth caries state information and prosthesis state information are learned by an ANN (Artificial Neural Network) algorithm. Then, the oral health calculation unit 310 for determining the oral health condition and the oral health information determined by the oral health calculation unit 310 are generated as oral examination diagnosis results, the generated oral examination diagnosis results are encrypted, and the user terminal 100 and a database 330 storing big data, personal information, encryption algorithms, etc. for oral diagnosis.

As shown in FIG. 3, the oral health calculation unit 310 registers the oral photograph transferred from the user terminal 100 as an oral health prediction target, and registers the image registered in the photograph registration unit 311. , learning with the CNN algorithm to determine whether the photo can be analyzed, whether it can be corrected, and whether the tooth can be extracted; It includes an oral diagnosis unit 312 that learns the obtained orthodontic information, tooth extraction information, dental caries condition information, and prosthesis condition information using the ANN algorithm to determine the oral health condition.

The operation of the mobile-based self-oral examination apparatus constructed as above according to the present invention will be described in detail as follows.

First, a user downloads a mobile-based oral examination application for self-examination and management of his/her own oral health condition and integrated management of the oral health of his/her family from the oral management service server 200, Store in 100.

Here, the oral examination application is an oral self-diagnosis application based on artificial intelligence (AI), and it is an application that allows the user to easily perform an oral examination anytime and anywhere to check his/her oral condition. Periodic oral examinations prevent oral diseases and are useful for preventing systemic diseases, and an oral examination application, which is one application, is simply installed on the user terminal 100, which is a smartphone. You will be able to manage your health.

The user runs the oral examination application and logs into the oral care service server 200 in order to check the oral health condition. For login, information such as ID and PW is registered by normal membership registration. At this time, the user registers the information of his or her family members who want to manage oral health in addition to him/herself through items such as addition of family members.

Here, for login, basic user personal information (name, gender, date of birth, blood type, etc.), medical history items, medications taken, or specific items of the body (high blood pressure, low blood pressure, diabetes, heart disease, constipation, pregnancy, etc.). You can also add your profile picture, etc.

When login is performed, a main screen such as that shown in FIG. 8 is displayed. From this main screen, it is possible to check the current status of dental appointments and medication information of the set family members, and view appointment details and check medications. can be done.

For example, through the family selection item, reservation and medication information can be filtered and checked by family. FIG. 9 shows a screen for confirming appointments among family schedule confirmations, and FIG. 10 shows a screen for taking medication. From the detailed menu for each family, you can check the medical appointment status and prescription list for each family, and perform oral self-examination based on artificial intelligence. In addition, by checking the calendar, it is possible to check the appointment information and medication items for the selected date, and by clicking, it is possible to check the detailed information. By selecting the main menu, it is possible to move to the home, dentist search, client dentist, and my information pages.

Also, a family member can be added through the add family item in the main menu. FIG. 11 is a screen showing an example of adding family members.

Next, for oral examination, the user selects an oral examination subject from the main menu and selects an inquiry item. Here, if the oral examination target is a family member, the family member to be examined can be specified and selected from among the registered family members, and if it is the patient himself/herself, the question item can be selected immediately. Since oral health examinations of family members and self are performed in the same way, it is assumed that oneself is designated as an oral health examination subject for convenience of explanation. When an inquiry item is selected, an examination list is displayed on a detailed screen, and the user selects an examination mode from this detailed screen. Here, the check-up list has a simple check-up mode and a general check-up mode.

Simple examination is a mode in which only the user's personal information and oral photographs to be diagnosed are provided and self-examination is requested, and general examination provides the user's personal information, oral photographs, and interview information and self-examination. This is the requested mode. Here, when a self-examination is requested, the device ID information of the user terminal 100 can be provided, or the ID information of the user device can be checked and matched with the user's personal information when registering as a user member. .

When requesting self-examination, the user uses the imaging guide information guided through the oral examination application to photograph the oral cavity or selects an intraoral image that has been photographed in advance. That is, when the user obtains an oral cavity image by photographing through the user terminal or selects a pre-registered oral cavity image, the photo selected in the photo input unit 101 or the photographed photo is set as the oral examination target image. do. Next, the photograph correcting unit 102 corrects the set oral cavity image to enhance the sharpness. In other words, the input oral cavity photograph is a non-stylized photograph, and the results of the medical examination differ depending on the camera and the photographer, as the image quality, photographing position, and the like change.

Therefore, it is necessary to correct the non-stylized photograph.

For correction of the non-stylized photograph, the input photograph image is divided into a predetermined size and the direction is adjusted so that it becomes a front image. Orientation can be perceived in a position-aware manner through the oral structures and adjustments can be made frontally. The orientation-adjusted split images are then filtered with an Image Sharpen Filter to enhance sharpness. Here, since the technique of enhancing the sharpness of a photograph with an image sharpening filter is already known in the field of image processing, a detailed description thereof will be omitted.

After the image correction has been performed on the non-stylized photograph, the oral cavity management service processing unit 104 confirms the diagnosis item selected by the user, and determines that the user has selected the simple diagnosis. and request an oral examination using only the user's personal information. On the other hand, if the user confirms the diagnostic items selected by the user and selects a general diagnosis, an oral examination is requested using the corrected image, the user's personal information, and the inquiry response information. Here, the questionnaire response information is obtained by looking at the questionnaire linked to the menu items displayed on the main screen when the oral examination application installed by the user is executed, and responding (checking) for each item using the key input unit. . When the responses to the questionnaire are completed, the questionnaire response unit 103 uses the questionnaire containing the responses to generate the questionnaire response information.

The oral cavity management service server 200 , upon receiving oral cavity photographs, personal information and/or inquiry response information from the user terminal 100 connected via the network, transfers them to the oral health management server 300 .

The photo registration unit 311 of the oral health calculation unit 310 of the oral health management server 300 registers the transferred oral photo in the internal database as an oral health examination target image. Here, when an individual directly photographs an oral cavity image, the photographing environment differs from person to person, so there is no standard for the color of the actual photographed image. Therefore, a reference point is taken, and all photos are automatically color-corrected in the same way as the reference point, and then saved.

Next, the oral cavity diagnosis unit 312 learns oral cavity photographs (images) using the CNN algorithm, determines whether the photographs can be analyzed, whether correction is possible, and whether the tooth can be extracted. Caries status information, periodontitis status information, and prosthesis status information are acquired, and the acquired dental caries status information and prosthesis status information are learned by the ANN algorithm to determine oral health status.

For example, the CNN algorithm learns oral cavity images to determine the presence or absence of oral cavity photographs. FIG. 4 shows the process of learning the oral cavity images by the CNN algorithm and determining the presence or absence of oral cavity photographs. For this reason, it is assumed that a CNN model has been built to determine if the previously taken and requested registration photo is an intraoral photo.

Pre-built CNN models are used in already known CNN models, such as VGG16 (https://neurohive.io/en/popular-networks/vgg16/), ResNet50, etc., with the addition of oral photography categories. It is desirable to The learning data may be intraoral photo data and photo data of a category to be added.

Using such oral photo data, it is possible to learn with a CNN model and judge the presence or absence of oral photos, but in this case, it takes a long time to learn according to the amount of training data added. cause inconvenience.

Therefore, as another method for determining the presence or absence of oral photographs, instead of using the CNN algorithm, the oral examination application is used to guide the user not to register other images of oral photographs. can be omitted. Here, guidance text can be used as a method of guiding the user not to register images other than the oral cavity photograph.

As a result of confirming the presence or absence of the oral cavity photograph, if it is determined as an oral cavity image, the oral cavity image is learned by the CNN algorithm shown in FIG. 5 to determine whether or not correction is possible. Here, FIG. 5 is an example of a CNN model for determining whether correction is possible from an input oral cavity image. Such a CNN model uses AutoKeras to construct the CNN model. Transfer learning using pre-built VGG16, ResNet50, etc. can also be used. The necessary data is oral photo data with/without a straightener, and by adding two categories of oral photos (with/without a straightener) to the CNN model, it is possible to easily judge whether or not correction is possible. can.

Such a CNN model can be used to determine whether correction is possible, but this also requires sufficient learning time and data, and an increase in learning data results in an increase in the amount of calculation. Therefore, the learning process of the CNN algorithm for determining whether correction is possible can be omitted by guiding the user to select whether or not correction is possible by creating a questionnaire for the user.

When the inquiry information is provided by creating an inquiry form, the inquiry information is classified by an FFNN (Feed Forward Neural Network) algorithm. Here, the FFNN algorithm is a machine learning algorithm whose input is a questionnaire response (user information) and whose output is a diagnosis result. It is desirable to use Python-based libraries such as Tensorflow and Keras to design and use an FFNN adapted to the input/output structure. Recently studied and analyzed FFNN algorithms are available for activation functions, loss functions, optimization methods, parallel computer architectures, overfitting problems, and so on. Input data (questionnaire responses) are preprocessed, and the preprocessed data are divided into training data and validation data. Then, the learning data is trained through neural network learning, and the verification data and learning result data are learned by the newly designed neural network through verification and complementation procedures. Then, when verification and complementation are completed, the final model is output as a questionnaire diagnosis result.

Here, it is possible to determine whether correction is possible using the constructed CNN model, but additionally, using the same CNN model, it is also possible to determine whether tooth extraction is possible. By adding the tooth extraction photography category to the CNN model, it is possible to easily determine whether tooth extraction is possible.

Next, the determined information on whether correction is possible and the information on whether extraction is possible are analyzed by individual detection, and dental caries state information and prosthesis state information are obtained. For this reason, an individual detection algorithm for judging oral disease (dental caries) and prosthesis detection is constructed from the section photographs (images) input as shown in FIG. Individual detection algorithms for determining oral disease and prosthesis detection are based on previously developed Faster R-CNN, SSD, YOLO (W. Liu et al., 2015, arXiv (http://arxiv.org/abs/1512. 02325)) can be used. Photographic data showing oral diseases and prostheses can be used as necessary data for acquiring dental caries state information and prosthesis state information by the individual detection algorithm. Accuracy can be improved by classifying whether or not a straightening machine can be worn and learning. The advantage is that one algorithm can detect both dental caries and prostheses. Here, the dental caries status information includes presence/absence information of dental caries, and information on the number of dental caries if there is dental caries, and the prosthesis status information includes prosthesis presence/absence information; and prosthesis count information, if prostheses are present. Tooth caries image learning confirms which part of the oral cavity photo provided by the user is in the whole oral cavity. After confirming the position of the oral cavity part image, image learning is performed. and extract the number of dental caries. In a similar manner, the presence or absence of the prosthesis and the number of prostheses are also extracted.

Next, the orthodontic feasibility information and tooth extraction feasibility information, dental caries state information and prosthesis state information are learned by the ANN algorithm (2019 NIMS Industrial Mathematical Problem Solving Workshop proposed model) as shown in FIG. determine health status; Here, the ANN algorithm, which is an artificial intelligence algorithm, determines the oral health condition (oral health degree) by inputting the orthodontic information, the extraction information, the dental caries condition information, and the prosthesis condition information. do. For this reason, it is possible to use the patient's oral health degree determination data corresponding to the reliable photographic data acquired from the specialist doctor or the attending doctor. Once the oral health determination is completed, oral health prediction information is generated and stored in an internal database.

Next, the oral health information providing unit 320 transfers the oral health examination diagnosis result acquired from the determination to the user terminal 100 . At this time, an encryption module is used to encrypt the oral examination results in order to prevent unauthorized leakage of the oral examination results. The encryption uses a method of generating a 256-bit web access token using the device ID of the user terminal and user information transmitted or registered in advance.

The encrypted oral examination diagnosis result is transferred to the user terminal 100 via the oral cavity management service server 200 . Here, the oral examination diagnosis result is the type of oral examination report.

Even if the results of the oral examination are leaked without permission by a method such as hacking before being transferred from the oral health management server 300 to the user terminal 100, the web token is encrypted with the user device ID and user information. Therefore, other people who do not know the user device ID and the user information cannot check the oral examination result information, thereby improving security.

When the oral health examination diagnosis result, which is oral health examination diagnosis result information, arrives, the oral health management service processing unit 104 of the user terminal 100 uses a decryption module to generate a web access token using the registered user device ID and user information. to decrypt. Decryption is the reverse order of encryption.

If the user device ID and user information match, the encrypted oral exam results can be opened.

In this way, by using the encrypted information when issuing the web token, it is possible to fundamentally prevent unauthorized access to the oral examination results.

The oral care service processing unit 104 stores the decoded oral examination result information in an internal memory and displays it on the screen so that the user can check the oral examination results in real time.

Based on the results of the oral diagnosis, if the oral condition is in a dangerous situation, a dental appointment is displayed on the screen of the examination report, which is the result of the oral examination. FIG. 12 shows an oral examination diagnosis result screen. When dental appointment is selected, dental information is displayed on the screen through map search based on the current location. A dental information search based on a map search displays dental information in order of closest distance from the current position. If the user selects a specific location (choose a ward or building based on Seoul), dental information is displayed on the screen in order of distance from the selected location based on the current location.

According to the present invention, when a user photographs and uploads his or her oral cavity, the oral health condition is diagnosed using an artificial intelligence algorithm, the diagnosis result is encrypted, and an oral health report is sent to the user in real time. By providing it, users can easily manage their oral health and prevent deterioration of oral health in advance.

In addition, oral care for registered family members is possible in the same way, so you can conveniently and comprehensively manage oral care for yourself and your family with a single mobile-based app.

Although the invention made by the present inventors has been specifically described with reference to the above-described embodiments, the present invention is not limited to the above-described embodiments, and can be variously modified without departing from the scope of the invention. This is obvious to those of ordinary skill in the art.

100 User terminal 101 Photo input unit 102 Photo correction unit 103 Questionnaire response unit 104 Oral management service processing unit 200 Oral management service server 300 Oral health management server 310 Oral health calculation unit 311 Photo registration unit 312 Oral diagnosis unit 320 Oral health information provision unit 330 database

Claims (7)

A device for diagnosing oral health by analyzing oral photographs with an artificial intelligence algorithm,
a user terminal for providing the corrected oral cavity image after correcting the oral cavity image of the user and requesting an oral examination diagnosis;
an oral care service server that transfers the corrected oral cavity image provided from the user terminal, requests an oral health examination diagnosis, and transfers oral health examination results to the user;
Oral health management for diagnosing the oral image transferred from the oral care service server, generating oral health information, encrypting the oral health information generated, and transferring it to the oral health care service server as an oral examination diagnosis result. a server;
The oral health management server learns the received oral photos with the CNN algorithm, which is an artificial intelligence algorithm, determines whether the photos can be analyzed, whether correction is possible, and whether the teeth can be extracted, and analyzes the determined result information by individual detection. and acquires dental caries state information and prosthesis state information, learns the acquired dental caries state information and prosthesis state information by an ANN algorithm, and determines oral health state. and an oral health information providing unit that generates the oral health information determined by the oral health calculation unit as the oral health examination diagnosis result, encrypts the generated oral health examination diagnosis result, and transfers it to the user terminal; A mobile-based self-oral examination device comprising: The user terminal, the oral care service server, and the oral health care server transmit and receive the encrypted oral examination diagnosis results, thereby improving security even when materials are leaked by hacking. 2. The mobile-based self-oral examination device according to claim 1. The oral health management server is characterized in that, when transferring the oral health examination diagnosis results, security is improved by limiting the authority of access targets using an access token that uses a user terminal ID and user information. The mobile-based self-oral examination device of claim 1. The user terminal includes a photograph input unit for inputting a photograph of a photographed oral cavity image as an image for oral health examination, a photograph correcting unit for correcting the oral cavity image input by the photograph input unit to enhance clarity, and The oral cavity image corrected by the photo correction unit is transferred to the oral cavity management service server to request oral health examination, the received oral health examination result is displayed on the screen, and in response to the user's key input request, and an oral care service processor for servicing hospital appointments or family oral care. 5. The mobile-based self-oral examination device according to claim 4, wherein the user terminal further comprises a questionnaire responder that generates questionnaire response information using the user's response information to the oral examination questionnaire. When the user selects a simple examination from an examination mode, the oral care service processing unit requests an oral examination diagnosis using only the oral cavity image corrected by the photo correcting unit and the user's personal information. When a general examination is selected from the mode, an oral examination diagnosis is requested using the oral cavity image corrected by the photo correction unit, the inquiry response information generated by the inquiry response unit, and the user's personal information. The mobile-based self-oral examination device according to claim 5. The oral health calculation unit learns the photo registration unit that registers the oral photo transferred from the user terminal as an oral examination diagnosis target, and the image registered in the photo registration unit by the CNN algorithm, and obtains the photo. Determine whether analysis is possible, whether correction is possible, and whether tooth extraction is possible, analyze the determined result information by individual detection, acquire the dental caries state information and the prosthesis state information, and obtain the orthodontic information and tooth extraction information. and an oral diagnosis unit that learns the dental caries status information and the prosthesis status information with an ANN algorithm to determine oral health status. Self-oral examination device.

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