WO2019189971A1 - Procédé d'analyse par intelligence artificielle d'image d'iris et d'image rétinienne pour diagnostiquer le diabète et un pré-symptôme - Google Patents

Procédé d'analyse par intelligence artificielle d'image d'iris et d'image rétinienne pour diagnostiquer le diabète et un pré-symptôme Download PDF

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WO2019189971A1
WO2019189971A1 PCT/KR2018/003806 KR2018003806W WO2019189971A1 WO 2019189971 A1 WO2019189971 A1 WO 2019189971A1 KR 2018003806 W KR2018003806 W KR 2018003806W WO 2019189971 A1 WO2019189971 A1 WO 2019189971A1
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region
iris
image
diabetes
interest
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PCT/KR2018/003806
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English (en)
Korean (ko)
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남궁종
길용현
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주식회사 홍복
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Publication of WO2019189971A1 publication Critical patent/WO2019189971A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to a method of artificially analyzing iris images and retinal images to diagnose diabetes and prognostic symptoms, and can accurately predict diabetes and reliably predict the possibility of diabetes through iris and retinal images on a smartphone. It is about providing a service.
  • Diabetes is a type of metabolic disease, such as insufficient insulin secretion or normal functioning. It is characterized by high blood glucose, which increases the concentration of glucose in the blood, and causes many symptoms and signs and releases glucose from urine. .
  • Type 1 diabetes is a disease caused by the inability to produce insulin at all, usually occurs in children or adolescents
  • type 2 diabetes is caused by the inability to effectively burn glucose due to the insulin function that lowers blood sugar.
  • Blood glucose test is one of the most commonly used methods of diagnosing diabetes. If the fasting blood sugar (fasting blood sugar after fasting for more than 8 hours) is 126 mg / dL or more, and whether or not the measured blood sugar is 200 mg / dL or more, the diagnosis is diabetes. do.
  • the oral glucose test is performed to confirm the blood sugar level, which is not high enough to be called diabetes in the blood glucose test, but also in the normal range.
  • the test method is to perform venous blood collection without fasting for more than 8 hours.
  • the glycated hemoglobin test is an important diagnostic method for diagnosing diabetes as well as long-term glycemic control in the medical community.
  • the glycated hemoglobin test reflects the average blood glucose level, that is, the degree of blood glucose control over the last two to three months. The normal range is 4-6%, and 6.5% or more is diagnosed as diabetes. It is classified into impaired glucose tolerance, impaired fasting glucose, and diabetes mellitus according to blood glucose level.
  • the prior art has a variety of disease diagnosis services through artificial intelligence
  • the existing AI disease diagnosis service is a server-client structure, using MRI, PET, retina, etc. as a medical image for learning or diagnosis.
  • the characteristics of each disease are extracted from the convolutional neural network (CNN), and the location of the disease and which part is a problem with high accuracy.
  • CNN convolutional neural network
  • An object of the present invention is to diagnose diabetes and prognostic symptoms in a smart phone in order to solve the high cost, space constraints, discomfort that is a problem of disease diagnosis using the existing artificial neural network.
  • Diagnosing diabetes and prognostic symptoms with iris and retinal images taken with a dedicated iris authentication lens attached to the front of the smartphone has the advantage of low cost and no space limitation, while the limited hardware performance of the smartphone This can make the use of wide and deep neural networks less accurate. Therefore, even in an area where there is no network, the purpose is to provide a service for diagnosing diseases through iris and retinal imaging in a smartphone and diagnosing diabetes and prognostic symptoms with high accuracy.
  • a method of artificially analyzing an iris image and a retinal image to diagnose diabetes and prognostic symptoms may include: Obtaining; Preprocessing the captured image to extract a region of interest at the user terminal; Expanding the resolution of the extracted region of interest at the user terminal; Compressing, at the user terminal, the library for reducing the amount of data for the region of interest to be transmitted to a server; At the user terminal, performing user authentication through the iris image; Encrypting, at the user terminal, data of the iris image and the retina image to be stored in the server; Learning, at the server, an artificial neural network to perform the diagnosis of the diabetes and the prognostic symptoms based on the stored data; And predicting, in the user terminal, a probability of developing diabetes using the learned artificial neural network, and the preprocessing of the captured image includes graying out an area that is not extracted to the ROI and extracting only an ROI.
  • the method may further include estimating the type of diabetes, based on the location and
  • the step of acquiring the captured image may include the iris image and the retina using an iris authentication-dedicated lens attachable to the user terminal.
  • the method may further include extracting an image.
  • a method of artificially analyzing an iris image and a retina image may include: preprocessing the captured image, extracting a region of interest including an iris region and a retina region from the captured image; Setting an axis for obtaining rotation rates of the iris region and the retinal region in the region of interest; Extracting only the iris region and the retina region from the region of interest; And repositioning the iris region and the retinal region based on the rotation rate.
  • a method of analyzing an iris image and a retinal image by artificial intelligence may further include setting the axis to set an axis for obtaining the rotation rate after performing eyelid segmentation. Repositioning the iris region and the retinal region is performed such that when the iris region and the retinal region are inclined with respect to the vertical direction, the iris region and the retinal region are 0 ° with respect to the vertical direction using the rotation rate. It characterized in that it further comprises the step of repositioning by rotation and interpolation to be.
  • the step of expanding the resolution may include: dividing the ROI into a specific patch size, Applying a convolutional neural network to further extend the resolution of the region of interest to a predetermined resolution, and before the convolutional neural network is applied, zero padding is added and the convolutional neural network is applied.
  • a dense block for re-learning the features of the previous step is used, and a skip connection is applied.
  • a method of artificially analyzing an iris image and a retinal image may include: learning the artificial neural network, storing the data of the iris image and the retinal image in a database; Learning on the stored data using the artificial neural network to perform parallel classification, detection and segmentation for diagnosing the diabetes or prognostic symptoms;
  • the artificial neural network includes a factorization method, a detachable convolution method, and a pointwise convolution method, classifying the data of the iris image and the retinal image, Extracting a feature map for the data; Extracting a region of interest corresponding to a predetermined anchor box by applying a region proposal network (RPN) to the feature map; Applying pooling to the learned region to convert regions of different sizes into regions of the same size; And classifying, detecting, and dividing a region corresponding to the diabetic or the prognostic symptoms based on the same size region.
  • RPN region proposal network
  • the step of predicting the occurrence of diabetes mellitus by applying the artificial neural network to the data of the iris image and the retinal image And predicting the probability of developing diabetes from the prognostic symptoms.
  • the user's terminal after predicting the probability of diabetes, the user's terminal, the individual constitution diagnosis service, complication diagnosis service and eating habits management service It characterized in that it further comprises the step of providing.
  • the server-client method Helps diagnose diabetes and prognostic symptoms in older areas where networks are not connected.
  • FIG. 1 is a conceptual diagram showing an entire system according to an embodiment of the present invention.
  • FIG. 2 is a flowchart illustrating a method of artificially analyzing an iris image and a retinal image to diagnose diabetes and prognostic symptoms according to an embodiment of the present invention.
  • FIG. 3 is a conceptual diagram of diabetes and prognostic symptoms AI diagnosis service through iris and retinal image on a smart phone according to an embodiment of the present invention.
  • FIG. 4 is a system configuration diagram of diabetes and prognostic symptoms artificial intelligence diagnosis service through iris and retinal image on a smart phone according to an embodiment of the present invention.
  • FIG. 5 is a schematic block diagram of an Image Super Resolution artificial intelligence neural network for raising the iris and retinal image shown in FIG. 3 from the low resolution to the high resolution.
  • FIG. 6 is a block diagram of a database structure for storing data in the neural network learning unit shown in FIG. 3 of the present invention.
  • FIG. 7 is a schematic block diagram of the artificial intelligence based disease diagnosis neural network shown in FIG. 4 of the present invention.
  • FIG. 8 is an additional service configuration diagram in addition to diabetes and prognostic symptoms diagnosis service in the service unit shown in FIG. 4 of the present invention.
  • FIG. 9 is a schematic diagram of an encryption method for storing data of an iris and retina image data encryption module in the security unit shown in FIG. 4 of the present invention.
  • ... unit described in the specification means a unit for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software.
  • “a” or “an”, “one”, and the like shall be, in the context of describing the present invention, both singular and plural unless the context clearly dictates otherwise or is clearly contradicted by the context. It can be used as a meaning including.
  • FIG. 1 is a conceptual diagram showing an entire system according to an embodiment of the present invention.
  • the user terminal may be an electronic device including a smart phone, a tablet PC, a laptop, and the like.
  • the user terminal may include a camera, and the camera may include an iris authentication dedicated camera or an iris authentication dedicated lens may be attached to the camera. Therefore, when the user's eye is photographed using the user terminal, a high resolution screen or image may be secured for a specific area including the iris and the retina.
  • the captured image obtained by photographing the eyes of the user through the user terminal may be used to predict and diagnose diabetes and prognostic symptoms.
  • FIG. 2 is a flowchart illustrating a method of artificially analyzing an iris image and a retinal image to diagnose diabetes and prognostic symptoms according to an embodiment of the present invention.
  • a user terminal acquires an image captured by a user's eyes.
  • the user terminal may preprocess the captured image to extract a region of interest.
  • a region of interest for example, an area that is not extracted from the region of interest is an unnecessary region, and thus a gray process is performed to extract only the region of interest, thereby reducing the amount of computation when diagnosing diabetes and prognostic symptoms from the image. More accurate diagnosis can be made.
  • the resolution of the extracted region of interest is expanded (S13), and the user terminal is compressed using a library for reducing the amount of data for the region of interest to be transmitted to the server (S14).
  • step S15 user authentication is performed through the iris image, and at the user terminal, data of the iris image and the retinal image to be stored in the server is encrypted (S16).
  • the artificial neural network to be diagnosed may be learned (S17), and the user terminal may predict the onset probability of diabetes using the learned artificial neural network (S18).
  • the type of diabetes can also be predicted based on the position and shape of the lesion area or the like in the region of interest.
  • Diabetes may be classified into type 1 diabetes, type 2 diabetes, and the like according to insulin function, or may be classified into impaired glucose tolerance and impaired fasting glucose according to blood glucose levels.
  • impaired glucose tolerance and impaired fasting glucose According to blood glucose levels.
  • Figure 3 shows a conceptual diagram of a diabetic and prognostic symptoms AI diagnosis service through iris and retinal image on a smart phone according to an embodiment of the present invention.
  • the diabetic and prognostic symptoms AI diagnosis service 100 through the iris and the retinal image on the smart phone includes an image capturing unit 101, an image preprocessing unit 102, and a neural network.
  • the learning unit 103 is provided through the disease diagnosis unit 104.
  • the image capturing unit 101 captures an eye image through a smartphone, and photographs using an iris authentication-dedicated lens that can be attached to the front of the smartphone, and acquires a captured image (S11). While cameras built into existing smartphones can capture eye images, iris-certified lenses use short-focus lenses and infrared LEDs to capture clear iris and retinal images with less impact on the environment. The sharper and less affected the light is, the better it is for iris recognition and diabetic and diagnosing AI symptoms. The captured image may be different for each iris color.
  • the image preprocessing unit 102 may extract only the iris and retinal regions necessary for diagnosing a disease from the captured eye image.
  • the region of interest refers to the minimum region necessary for extracting the iris and the retina.
  • the reason for extracting the region of interest is that the amount of computation for the extraction and relocation of the iris and retina can be reduced.
  • the grayed image connect the edges of the both ends of the eyelids with a line, and then set the axis, and set the axis's rotation rate based on 0 degree, and then rotate and interpolate the extracted iris and retinal images by the rotation rate. Relocate through the back.
  • the reason for relocating the iris and retinal images is that the location of lesions such as tears, pigmentation, and cockroaches is important information for diagnosing diabetic prognostic symptoms through iris and retinal imaging. Since it means that there is a problem with a specific organ according to the location, the iris and retinal images may be rearranged based on 0 ° to determine accurate prognostic symptoms (S12).
  • the artificial neural network converting the low resolution to the high resolution may be converted to the high resolution (S13).
  • Artificial neural networks that convert low resolution to high resolution can separate iris and retinal images into a specific patch size and then expand the resolution through a convolutional neural network (CNN). Since the original image is expanded by a specific patch unit, the image can be restored by merging the output images.
  • the neural network that converts the low resolution into high resolution uses a dense block to enhance and reuse the feature propagation of the previous stage and extracts the input image to learn more about the boundary of the image. You can use the Skip Connection to connect to the previous video.
  • the neural network learning unit 103 is composed of a database storing iris and retinal image data and a neural network model learning from the stored data.
  • a database storing iris and retinal image data and a neural network model learning from the stored data.
  • the information to be stored in the database must be stored encrypted with bio information, unique identification information (resident registration number, passport number, driver's license number, alien registration number) and password corresponding to personal information according to the Personal Information Protection Act. Since the iris and retinal image data correspond to bioinformation, the iris and retinal image data should be stored after encryption (S16). Encryption methods for storing data include application self-encryption, DB server encryption, DBMS self-encryption, DBMS encryption function call, and operating system encryption.
  • DB encryption method should be selected in consideration of constraints.
  • the encryption key uses a code value extracted from iris and retinal image data, encrypts it using the encryption key, and stores it in a database.
  • the artificial intelligence neural network for diagnosing diabetes and prognostic symptoms can be learned (S17). Diagnosis of Diabetes and Prognostic Symptoms through Iris and Retinal Imaging
  • the AI neural network should be trained to be classified, detected, or segmented for diabetic precursor symptoms. Therefore, this artificial intelligence neural network can pre-train the neural network part that classifies the precursor symptoms for more accurate classification in learning.
  • the artificial neural network used for the classification part takes up a large part of the entire neural network, this part is made as lightweight as possible so that it can be performed smoothly on mobile.
  • There are many ways to lighten the AI neural network For example, multiplying a large filter size of 5 x 5 into 3 x 3 and small filters of 3 x 3 and performing a multiplication can reduce the factorization by 20-30% of the computation performed by the existing large filter.
  • the conventional convolution layer learns filters in horizontal-vertical (two-dimensional) and channels (one-dimensional), and each filter simultaneously requires mapping and spatial correlation.
  • Depthwise Separable Convolution method which reduces the amount of computation by 1 / N + 1 / D_K compared to the conventional convolution, by processing the inter-channel and spatial correlation separately Instead of dealing with spatial correlation in the conventional convolution layer, only the correlation between channels is used to increase or decrease the dimension of the output feature map.
  • Using a combination of pointwise convolution methods to reduce the artificial intelligence neural network After performing pre-training on the classification part, we can learn to find the location of the bounding box of the probable part with the extracted feature map. Since the extracted bounding boxes are different sizes, ROI pooling is performed to fit a certain size, and the classification of diabetic prognostic symptoms is carried out through a convolutional neural network (CNN). Can learn / Detection / Segmentation. For classes that are not prognostic symptoms, students learn to penalize penalties for more accurate classification, detection, and segmentation.
  • CNN convolutional neural network
  • the disease diagnosis unit 104 may diagnose diabetes by detecting diabetes mellitus and prognostic symptoms using the iris and retinal images preprocessed with the artificial intelligence neural network learned by the neural network learning unit 103 (S18).
  • FIG. 4 is a system configuration diagram of diabetes and prognostic symptoms artificial intelligence diagnosis service through iris and retinal image on a smart phone according to an embodiment of the present invention.
  • the diabetic and prognostic symptoms AI diagnosis system 200 through the iris and the retina image on the smart phone is a user unit 201, platform unit 202, security unit 203 ), A service unit 204, a data server unit 205, and a neural network learning server unit 206.
  • the user unit 201 may be an individual or a hospital and may be an institution that desires diagnostic services through the iris and retina.
  • the platform unit 202 may be Android, IOS, WINDOWS, etc., because it must be serviced on the mobile.
  • the security unit 203 may be divided into a part for authenticating a person with an iris recognition module and a part for encrypting iris and retinal image data in order to receive diabetic and prognostic symptom diagnosis services through iris and retinal images.
  • the service unit 204 is capable of diagnosing diabetes and prognostic symptoms through the learned AI-based disease diagnosis neural network, individual constitution diagnosis service comparing the iris constitution features with collected iris data, and probable occurrences based on the extracted prognostic symptoms. Complications diagnostic services, and personalized eating habits management services through the diagnosis of constitution and prognostic symptoms.
  • the data server unit 205 may store iris and retinal image data necessary for the AI neural network to learn.
  • the neural network learning server unit 206 may learn the artificial intelligence neural network through the stored iris and retinal image data.
  • FIG. 5 is a schematic block diagram of an Image Super Resolution artificial intelligence neural network 300 that raises the iris and retinal images shown in FIG. 3 from low resolution to high resolution. If the iris and retinal images have low resolution, there is less data to refer to when diagnosing a disease, so the accuracy of the iris and retinal images is relatively low. Therefore, it is necessary to raise the low resolution iris and retinal images to high resolution.
  • the low resolution iris and retinal images coming into the input are divided into a specific patch size, and the divided images are input as inputs and the reconstructed output image is extracted through a multiplication neural network.
  • the use of a generalized composite neural network reduces the size of the output feature map, making it impossible to make full use of information about pixels near the image boundary.
  • FIG. 6 is a block diagram illustrating a structure of a database 400 for storing iris and retinal image data in the neural network learning unit illustrated in FIG. 3 of the present invention.
  • server processes in the database can be allocated to process the SQL.
  • the server process uses the handler to find the handlers in the library cache that contain information about the SQL. If the handler exists, the server parses the SQL statement. Hard Parsing) procedure.
  • Hard parsing includes syntax checking (SQL spelling and SQL grammar checking), semantic checking (table / column existence), query transformation (optimizer rewrites SQL for better performance), authorization checking, and DML (Data) During Manipulation Language), lock type check, execution plan, parsing tree creation, and log record (LOG record generated during DML) are processed. Data is stored in a specific block in the data buffer cache, and when a commit is performed, the data of the block is written to the data file. Through this process, iris and retinal image data can be stored in a database.
  • the AI-based disease diagnosis neural network shown in FIG. 4 of the present invention is composed of a disease classification unit 501 and a disease location detection unit 502, and the disease classification unit 501 is a diabetic precursor symptom (pancreas) from iris and retinal images.
  • a multiplicative neural network can be used to classify site lesions, triglycerides, stress rings, etc.).
  • the disease location detection unit 502 may extract the features for the prognostic symptoms while passing through the composite product neural network of the disease classification unit 501 and process the feature map output from the last output layer.
  • the disease location detecting unit 502 may detect and segment the precursor symptom site based on a feature map from the disease classification unit 501.
  • the region is subjected to a Region Proposal Network (RPN).
  • RPN Region Proposal Network
  • the region of interest is extracted according to a predetermined anchor box.
  • the region of interest may represent a detection region for prognostic symptoms in the iris and retinal images. Since the extracted regions of interest differ in size, they cannot be used in convolutional neural networks that require performance at a fixed size. Therefore, ROIs of different sizes are converted into the same size through the ROI pooling layer.
  • the precursor symptom classification / detection / division of the corresponding region of interest may be performed in parallel to output to the iris and retinal images which precursor symptoms are detected at which positions. Based on the prognostic symptoms in the iris and retina of a person with diabetes, the predicted outcome of the neural network can be predicted.
  • FIG. 8 is an additional service configuration diagram in addition to diabetes and prognostic symptoms diagnosis service in the service unit shown in FIG. 4 of the present invention.
  • the additional service includes an image capturing unit 601, a data server unit 602, a constitution diagnosis unit 603, a disease diagnosis unit 604, a complication diagnosis unit 605, and a eating habit management unit 606. It can be provided using.
  • the image capturing unit 601 may allow the user to photograph the iris and the retina using an iris authentication dedicated lens attached to the front of the smartphone.
  • the data server unit 602 may encrypt and store the photographed iris and retina image data.
  • the constitution diagnosis unit 603 may perform constitution diagnosis by the sun person, Taein person, Soyangin person, and the noise person in comparison with the iris constitution property from the personal iris and retinal image data stored in the database server.
  • the disease diagnosis unit 604 may diagnose diabetes and prognostic symptoms using the captured iris and retinal image data.
  • the complication diagnosis unit 605 may diagnose complications associated with the prognostic symptoms from the disease diagnosis unit 604.
  • the eating habit management unit 606 may provide a personalized diet based on the constitution diagnosed by the constitution diagnosis unit 603 (sun person, Taein person, Soyangin person, and noise person) and the prognostic symptoms diagnosed by the disease diagnosis unit 604.
  • Encryption methods for storing data include application program encryption 701, DB server encryption 702, DBMS self encryption 703, DBMS encryption function call 704, and operating system encryption 705.
  • Application self-encryption (701) is a method in which the encryption / decryption module is installed in each application server in the form of API library to call the encryption / decryption module within the application, but it does not affect the DB server. Modifications may be necessary.
  • DB server encryption 702 is easy to implement because the encryption / decryption module is installed on the DB server and calls the encryption / decryption module connected to the plug-in from the DBMS. Therefore, it is necessary to create a view corresponding to the existing DB schema and add a table to encrypt, and there may be a load on the DB server.
  • the DBMS self encryption (703) is processed at the DBMS kernel level by performing encryption / decryption processing using the encryption function built in the DBMS, so that modification of the existing application or modification of the DB schema is rarely required. Load can occur.
  • the DBMS encryption function call 704 provides an API for the DBMS to perform its own encryption / decryption function, and the application needs to be modified in such a way that the application executes to use the function, and the DB server may be overloaded. have.
  • the operating system encryption 705 is an encryption / decryption method using input / output system calls generated by the OS, but does not require modification of an application program or a DB schema, but may cause a load on the file server and the DB server to encrypt the entire DB file.
  • the present invention accumulates big data indicating the possibility of diabetes and the degree of diabetes progression according to the position and shape of the lesion area associated with diabetes mellitus, and based on the big data, Learning and determining the possibility of diabetes mellitus and the progression of diabetes mellitus, and according to the possibility of diabetes mellitus and the degree of diabetes mellitus, an additional test including a blood glucose test, an oral glucose load test and a glycated hemoglobin (HbA1c) test is required. Notification can be made in real time. In addition, even when the degree of diabetes progressed exponentially in recent years, the progress of the diabetes may be notified in real time through the user terminal.
  • the real time notification through the user terminal may be configured as a pop-up or push alarm.
  • the above-described method may be written as a program executable on a computer, and may be implemented in a general-purpose digital computer which operates the program using a computer readable medium.
  • the structure of the data used in the above-described method can be recorded on the computer-readable medium through various means.
  • Computer-readable media that store executable computer code for carrying out the various methods of the present invention include, but are not limited to, magnetic storage media (eg, ROM, floppy disks, hard disks, etc.), optical reading media (eg, CD-ROMs, DVDs). Storage media).

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Abstract

Un mode de réalisation de la présente invention concerne un procédé d'analyse par intelligence artificielle d'une image d'iris et d'une image rétinienne pour diagnostiquer le diabète et un pré-symptôme sur un Smartphone, comprenant les étapes consistant à : acquérir une image capturée de l'œil d'un utilisateur par un terminal d'utilisateur ; pré-traiter, par le terminal d'utilisateur, l'image capturée pour extraire une région d'intérêt ; étendre, par le terminal d'utilisateur, la résolution de la région d'intérêt extraite ; la compresser, en utilisant une bibliothèque pour réduire la quantité de données, la région d'intérêt à transmettre à un serveur, par le terminal d'utilisateur ; réaliser, par le terminal d'utilisateur, une authentification d'utilisateur sur l'image d'iris ; crypter des données de l'image d'iris et de l'image de rétine à stocker dans le serveur par le terminal d'utilisateur ; apprendre, par le serveur, un réseau neuronal artificiel pour diagnostiquer le diabète et le pré-symptôme sur la base des données stockées ; et prédire un risque de développer un diabète en utilisant le réseau neuronal artificiel appris par le terminal d'utilisateur. L'étape consistant à pré-traiter l'image capturée comprend en outre une étape consistant à traiter des nuances de gris d'une zone non extraite en tant que région d'intérêt de l'image capturée pour extraire uniquement la région d'intérêt, et l'étape consistant à prédire le risque de développer un diabète comprend en outre une étape consistant à prédire le type de diabète sur la base de la position et de la forme d'une zone de lésion.
PCT/KR2018/003806 2018-03-30 2018-03-30 Procédé d'analyse par intelligence artificielle d'image d'iris et d'image rétinienne pour diagnostiquer le diabète et un pré-symptôme WO2019189971A1 (fr)

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