WO2024071943A1 - Procédé faisant appel à l'intelligence artificielle pour fournir des informations sur une scoliose - Google Patents

Procédé faisant appel à l'intelligence artificielle pour fournir des informations sur une scoliose Download PDF

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WO2024071943A1
WO2024071943A1 PCT/KR2023/014735 KR2023014735W WO2024071943A1 WO 2024071943 A1 WO2024071943 A1 WO 2024071943A1 KR 2023014735 W KR2023014735 W KR 2023014735W WO 2024071943 A1 WO2024071943 A1 WO 2024071943A1
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scoliosis
confirmed
artificial intelligence
image
type
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PCT/KR2023/014735
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Korean (ko)
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이정섭
이상학
박강현
고태식
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부산대학교 산학협력단
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    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1071—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring angles, e.g. using goniometers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1079—Measuring physical dimensions, e.g. size of the entire body or parts thereof using optical or photographic means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
    • A61B5/4561—Evaluating static posture, e.g. undesirable back curvature
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • 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
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

  • the present invention relates to convergence technology that combines the fields of medicine and artificial intelligence.
  • Scoliosis is a disease in which the spine is curved in three dimensions. The prevalence is reported to be 2-3%, and it is especially reported in children and adolescent girls aged 10 or older. If it occurs in children and adolescents, the spinal curve continues to progress along with growth, and in this process, the patient may end up living in a bent posture to make himself comfortable, which often worsens the condition. In hospitals, it is treated using braces or surgically. If the condition worsens, it may lead to death due to respiratory failure.
  • the patient only comes to the hospital after the scoliosis has progressed to the point where the curvature can be seen with the naked eye or pain is felt. Moreover, since there is a significant shortage of pediatric spine orthopedic specialists and specialized hospitals in Korea, the timing of visit to the hospital is further delayed. If you come to the hospital late, the treatment period will be longer, the effects will be delayed, and the process will be painful.
  • the person or their parents must have the ability and will to recognize it with the naked eye, or visit the hospital regularly and receive a diagnosis through professional medical images such as X-ray images or CT scan images. Even if you visit a hospital, if the professional medical staff lacks experience, it is not easy to diagnose scoliosis using only external images, so professional medical images such as X-ray images or CT imaging images are required. In this process, children/adolescents may be exposed to unnecessary radiation, and the burden of personal and national medical costs increases.
  • An artificial intelligence model can be considered as a technology that determines whether there is scoliosis by inputting only an external image.
  • medical-related image analysis is a field in which significant progress is being made.
  • Patents that use artificial intelligence to analyze images and provide medical-related information can be classified into two criteria.
  • the first classification standard is to distinguish between technology based on professional medical images and technology based on general external images (images taken with smartphones, etc.), and the second classification standard is technology that calculates information related to diseases other than scoliosis and technology that calculates information related to diseases other than scoliosis. It is classified using technology to calculate information related to scoliosis. Accordingly, patents in a total of four fields are divided, and patents are reviewed accordingly.
  • Korean Patent No. 10-2181701 discloses a technology for detecting certain diseases using images of nail wrinkle capillaries.
  • Korean Patent Publication No. 10-2020-0110878 discloses a technology for confirming the gum area and performing early diagnosis of cavities by using the specular reflection image of the oral cavity image.
  • U.S. Patent No. 10,468,142 discloses a technology for predicting disease using corneal photographs.
  • Korean Patent Publication No. 10-2022-0057793 discloses a technology for checking atopic dermatitis using images taken of the skin.
  • Korean Patent No. 10-2388337 discloses a technology for checking the accuracy of temporomandibular joint movement based on video footage of the jaw movement situation.
  • Korean Patent No. 10-2354980 discloses a technology for confirming the presence or absence of anterior segment disease based on images taken of the anterior segment.
  • Korean Patent No. 10-2274330 discloses a technology for determining whether a person has a stroke using an image of the face.
  • Korean Patent No. 10-2251925 discloses a technology for recording abnormal walking based on videos of walking situations.
  • Korean Patent No. 10-2214756 discloses a technology for checking the location and progress of cavities using oral imaging images.
  • 10-2047237 discloses a technology for predicting health status and likelihood of disease using images representing skin color.
  • U.S. Patent Publication No. 2022-0133215 discloses a technology for analyzing lesions by checking skin color in an image.
  • U.S. Patent Publication No. 2021-0345971 discloses a technology for diagnosing Lyme disease using digital images of the skin.
  • Korean Patent No. 10-2389067 discloses a technology that automatically determines the tilt of each vertebra using X-ray images, which are professional medical images, and determines whether there is scoliosis based on this.
  • Korean Patent No. 10-2383857 also discloses a technology for estimating Cobb angle and diagnosing scoliosis using X-ray images, which are professional medical images.
  • Japanese Patent No. 3234668 discloses a technology for recognizing scoliosis by segmenting the vertebral body, prevertebral body, and iliac regions in an X-ray image.
  • the above technologies commonly identify vertebrae individually and calculate each tilt or estimate the shape of each vertebrae itself. This method is highly accurate because it utilizes professional medical images, but it requires professional medical images such as X-ray photographs. Therefore, the patented technologies cannot be used before visiting the hospital, and the problem of lack of early diagnosis described above cannot be solved.
  • Japanese Patent Publication No. 2021-115471 discloses a technology for generating skeletal data based on images or videos taken of the subject's body, detecting skeletal deformation over time, diagnosing posture, and diagnosing diseases such as scoliosis.
  • This prior art is a technology that detects changes between images by securing multiple images with different shooting times (e.g., 1 week, etc.) with time set as a variable, so the information related to scoliosis at the time when only one image was captured is cannot be provided.
  • shooting times e.g., 1 week, etc.
  • Japanese Patent Publication No. 2020-040763 discloses a technology for estimating the scoliotic angle through an external image and its mirror image. Although this technology helps identify areas of asymmetry in images of a subject's back, the fact that an asymmetric back was imaged does not directly lead to scoliosis. For example, if the photograph is taken in a crooked position, there is a high possibility of being misdiagnosed as having scoliosis.
  • Japanese Patent No. 6280676 discloses a technology for estimating the spine alignment and calculating the Cobb angle, turning angle, etc., using Moire images after photographing the back.
  • moiré images there is a problem that only the extent to which the spine protrudes outside the skin is used as the basis for judgment. For example, if the spine does not protrude beyond the skin due to obesity or other reasons, the alignment of the spine is not correctly determined, and the portion of the spine that protrudes outside the skin is symmetrical, but scoliosis actually progresses, or vice versa. Since it is significant, diagnostic accuracy is greatly reduced and the possibility of misdiagnosis is high.
  • Korean Patent Publication No. 10-2021-0157684 discloses a technology for 3D modeling individual vertebrae using X-rays or MRI images, which are professional medical images.
  • Korean Patent No. 10-2062539 discloses a technology to model each lumbar vertebrae and check for lumbar disease using the center point and rotation angle.
  • Korean Patent No. 10-1968144 discloses a method of extracting professional medical images from PACS (medical image storage system), extracting images of the spine or cervical spine, and diagnosing the inclination angle through contour processing and inclination calculation of specific bones.
  • PACS medical image storage system
  • Patent Document 1 Korean Patent No. 10-2181701
  • Patent Document 2 Korean Patent Publication No. 10-2020-0110878
  • Patent Document 3 US Patent No. 10,468,142
  • Patent Document 4 Korean Patent Publication No. 10-2022-0057793
  • Patent Document 5 Korean Patent No. 10-2388337
  • Patent Document 6 Korean Patent No. 10-2354980
  • Patent Document 7 Korean Patent No. 10-2274330
  • Patent Document 8 Korean Patent No. 10-2251925
  • Patent Document 9 Korean Patent No. 10-2214756
  • Patent Document 10 Korean Patent No. 10-2047237
  • Patent Document 11 U.S. Patent Publication No. 2022-0133215
  • Patent Document 12 U.S. Patent Publication No. 2021-0345971
  • Patent Document 13 Korean Patent No. 10-2389067
  • Patent Document 14 Korean Patent No. 10-2383857
  • Patent Document 15 Japanese Patent No. 3234668
  • Patent Document 16 Japanese Patent Publication No. 2021-115471
  • Patent Document 17 Japanese Patent Publication No. 2020-040763
  • Patent Document 18 Japanese Patent No. 6280676
  • Patent Document 19 Korean Patent Publication No. 10-2021-0157684
  • Patent Document 20 Korean Patent No. 10-2062539
  • Patent Document 21 Korean Patent No. 10-1968144
  • the present invention was created to solve the above problems.
  • One embodiment of the present invention to solve the above problems includes the steps of: (a) collecting an external image in the image collection unit 110; -
  • the external image includes an image of the back, (b) inputting the collected external image to the artificial intelligence learning unit 100; (c) adding a guideline to the input external image and inputting it into the artificial intelligence learning unit 100; -
  • the guideline includes a line indicating the protrusion of one of the left and right sides of the back, the outline of both shoulders and the outline of both sides,
  • the professional diagnosis information includes any one type of scoliosis among normal and multiple types.
  • the artificial intelligence learning unit 100 uses the external image with the guideline added as input data. and generating an artificial intelligence model by learning a learning dataset using the professional diagnosis information as output data.
  • step (e) a step of inputting an appearance image and a guideline corresponding to the input appearance image into the artificial intelligence model generated in step (d) by the image input unit 210. ; (g) calculating scoliosis information indicating normal or one type of scoliosis among multiple types by the artificial intelligence model; and (h) outputting the calculated scoliosis information by the output unit 220. It is preferable to further include.
  • steps (f) to (h) are performed by a terminal, the terminal includes a camera, and the external image input in step (f) is an image captured by the camera of the terminal. desirable.
  • Step (i) transmitting the external appearance image input in step (f) and the scoliosis information calculated in step (g) to a preset hospital medical management system 300; and (j) confirming professional diagnosis information corresponding to the external image transmitted in step (i) in the hospital medical management system 300, wherein the terminal further includes a communication module, Step (i) is preferably a step in which the information is transmitted to the hospital medical management system 300 by the communication module of the terminal.
  • step (k) the hospital medical management system 300 uses the external image transmitted in step (i) and the professional diagnosis information confirmed in step (j) to the artificial intelligence learning unit ( 100) transmitting; and (l) the artificial intelligence learning unit 100 reinforcing the artificial intelligence model using the appearance image and professional diagnosis information transmitted in step (k). It is preferable to further include.
  • step (j) (m) generating a changed appearance image different from the appearance image input in step (f) by the camera of the terminal; (o) inputting the changed appearance image generated in step (m) by the image input unit 210 and the guideline corresponding to the input changed appearance image into the artificial intelligence model; (p) calculating altered scoliosis information indicating normal or one type of scoliosis among multiple types by the artificial intelligence model; and (q) outputting the calculated changed scoliosis information by the output unit 220.
  • scoliosis examples include: Type 1 - Thoracic scoliosis, Type 2 - Double Thoracic scoliosis, and Type 3 - Double Major-Thoracic/Lumbar.
  • it includes scoliosis, Type 4 - Triple Curve scoliosis, and Type 5 - Lumbar/Thoracolumbar scoliosis.
  • step (c) identifies the back portion of the human body in the input appearance image, confirms the left and right central axes in the identified back portion, and pixels of the back portion based on the left and right central axes in the input appearance image. Check the saturation difference, and if it is more than the preset saturation difference, the side with low saturation is judged to be a case of protrusion of the left and right sides of the back, and that part is marked with a line, and the upper outline of the back is used to mark the two shoulders. It is preferable to further include the step of marking the outline and marking the outline of both sides using the left and right outlines in the back.
  • one side of the back is protruding through the protruding lines on one of the left and right sides of the back, and the other shoulder is not raised through the outlines of both shoulders, and one side is retracted through the outlines of both sides. If this is not confirmed, it is confirmed as type 1 scoliosis, and one side of the back is protruded by the protrusion line on one of the left and right sides of the back, the other shoulder is raised by the outline of both shoulders, and both sides are confirmed to be raised.
  • one side is not confirmed through the outline, it is confirmed as type 2 scoliosis, one side of the back is protruded by the protrusion line on one of the left and right sides of the back, and the other shoulder is raised by the outline of both shoulders. If it is not confirmed and one side is confirmed through the outlines of both sides, type 3 scoliosis is confirmed, and one-sided back protrusion is confirmed by the protrusion lines on one of the left and right sides of the back, and the outlines of both shoulders are confirmed.
  • Another embodiment of the present invention to solve the above problems provides a computer program stored in a computer recording medium on which the above-described method is performed, or a computer recording medium in which the program is stored.
  • the accuracy is high.
  • a common problem with patents that calculate scoliosis-related information using external images was the possibility of misdiagnosis due to the limitations of each technique.
  • the present invention sets new guidelines and provides learning based on them. By performing this, the possibility of diagnosing scoliosis was increased. Accuracy is high because actual diagnostic information from professional medical staff is used during learning. Excellent accuracy was confirmed in the verification experiment described later.
  • diagnostic accuracy can be improved through data reinforcement, while patients can easily set visit schedules, accurately inform hospitals of their current situation, and treatment effects can be continuously monitored.
  • the problem with artificial intelligence technology using medical images is the lack of data containing diagnostic information by professional medical staff, and the present invention can fundamentally solve this problem.
  • the effect can be monitored by the patient and professional medical staff together, and in some cases, the need to change the orthosis or exercise method can be quickly confirmed and the treatment effect can be improved.
  • diagnosis results of professional medical staff are managed, they can be used to strengthen an artificial intelligence model.
  • Figure 1 discloses a system in which the method according to the invention is performed.
  • Figure 2 is a diagram for explaining scoliosis types in the method according to the present invention, and for each type, an actual external image and a professional medical image are disclosed together.
  • Figure 3 is a conceptual diagram illustrating elements for determining the type of scoliosis in the method according to the present invention.
  • Figure 4 is a flow chart for explaining the method according to the present invention.
  • Figure 5 is a conceptual diagram showing an example in which the method according to the present invention is applied mainly to schools and hospitals.
  • Figure 6 shows the results of verifying the importance of each guideline element after executing the method according to the present invention.
  • “appearance image” refers to an image of the patient's back observed with the naked eye through an image taken with a general camera (for example, a smartphone camera). In other words, it is an image, not a professional medical video.
  • professional medical images refers to images, such as X-ray images and CT images, that are accessible in hospitals and must be captured by professional medical staff.
  • professional diagnosis information refers to information determined by a professional medical staff as to whether the patient has scoliosis and, if so, what type of scoliosis it is.
  • guideline means a line that overlaps on the external image.
  • FIG. 2 it is shown as a circle and a straight red line.
  • scoliosis information is information calculated by an artificial intelligence model, and refers to information determined as to whether scoliosis is present and, if so, what type of scoliosis it is. That is, it includes normal scoliosis or any one type of scoliosis among multiple scoliosis types.
  • scoliosis type refers to classification according to the type of curve.
  • the present invention proposes five types of scoliosis. This is a type that can be classified only based on external images, but has been decided to be a classification that will help establish a quick and accurate professional diagnosis and treatment method when the information is delivered to professional medical staff.
  • the system for performing the method according to the present invention includes an artificial intelligence learning unit 100 and a scoliosis information calculation unit 200.
  • the artificial intelligence learning unit 100 collects data for learning, sets a learning dataset, and performs the function of creating an artificial intelligence model through learning.
  • the type of artificial intelligence used here is not limited.
  • the artificial intelligence learning unit 100 includes an image collection unit 110 that collects images, a guideline provision unit 120 that includes guidelines in the image, and professional diagnosis information that inputs professional diagnosis information from a professional medical staff into the image. Includes an input unit 130.
  • the image collection unit 110 collects multiple external images. Appearance images showing the presence and type of scoliosis must be collected by professional medical staff.
  • the guideline providing unit 120 manually or automatically includes guidelines in the external image. The specific method will be described later.
  • the professional diagnosis information input unit 130 performs a function of inputting professional diagnosis information, which is information about the presence and type of scoliosis diagnosed by a professional medical staff, to the external image for each collected external image.
  • the artificial intelligence learning unit 100 is data confirmed by the image collection unit 110, the guideline provision unit 120, and the professional diagnosis information input unit 130, and uses the external image with the guideline added as input data and the professional diagnosis information input unit 130. Set up a learning dataset with diagnostic information as output data and learn it to create an artificial intelligence model.
  • the scoliosis information calculation unit 200 includes a generated artificial intelligence model and includes an image input unit 210 and an output unit 220.
  • the external image to be input to the artificial intelligence model created through the image input unit 210 is confirmed.
  • the output unit 220 outputs scoliosis information in which the external image input by the image input unit 210 is the result of an artificial intelligence model.
  • the scoliosis information calculation unit 200 is preferably a program or application that can be executed on a terminal that the user can easily access.
  • the terminal is preferably a smartphone that includes a camera and a communication module. In this case, if the user's back is photographed using the smartphone's camera, it is recognized as an external image, and the image can be easily input into the artificial intelligence model by uploading it to the application, and the results can also be easily confirmed.
  • system for performing the method according to the present invention may be further linked to the hospital medical management system 300.
  • the external image and scoliosis information (i.e., presence and type of scoliosis) confirmed by the scoliosis information calculation unit 200 may be automatically transmitted to the hospital medical management system 300.
  • the hospital medical management system 300 identifies patients using a separate method, stores and uploads the relevant information in a digital chart, and makes it accessible to professional medical staff.
  • scoliosis information calculation unit 200 that is, the patient's smartphone, and is integrated and managed. It may be possible.
  • the patient continuously creates a modified appearance image by taking pictures of his/her back using a smartphone, and this information is entered into the scoliosis information calculation unit 200 to change the scoliosis. Information is generated, and the generated information is transmitted back to the hospital medical management system 300. Through this, patients can check the treatment progress themselves, and professional medical staff can also monitor it.
  • professional diagnosis information which is information about the diagnosis by professional medical staff, is generated, and the external image input to the image input unit 210 and generated by the hospital medical management system 300
  • the received professional diagnosis information can form an additional learning dataset, enabling reinforcement learning of the artificial intelligence model.
  • the present invention is a form that can be classified only by external image, and at the same time, when the information is delivered to professional medical staff, it helps in setting up a quick and accurate professional diagnosis and treatment method. It divides scoliosis into five types according to the curvature of the spine. We suggest a way to differentiate.
  • type 1 is thoracic scoliosis
  • type 2 is double thoracic scoliosis
  • type 3 is double major-thoracic/lumbar.
  • Scoliosis Type 4 is Triple Curve scoliosis
  • Type 5 is Lumbar/Thoracolumbar scoliosis.
  • This classification can be performed by professional medical staff, and is largely based on three factors as shown in FIG. 3. This includes (A) whether one side of the back (either left or right) sticks out, (B) whether the shoulder on the other side (i.e., the opposite side if the back sticks out) rises, and (C) whether the side of the side goes in. These may appear in combinations of one or more. Therefore, the scoliosis type is determined through the combination of these. A combination not shown (for example, a case where one side of the back sticks out and the other shoulder goes up but one side does not go in) can be understood clinically as an extremely exceptional case.
  • the external image includes guidelines for identifying them. Therefore, the guideline includes a line indicating the protrusion of one of the left and right sides of the back, the outlines of both shoulders, and the outlines of both sides.
  • An example of the guidelines can also be found in Figure 2.
  • the saturation difference between the pixels in the back area based on the left and right central axes in the external image. If the saturation difference is more than the preset, the side with low saturation is judged to be a case where one of the left and right sides of the back protrudes, and that part is marked with a line. Display. Additionally, the outline of both shoulders is marked using the upper outline of the back. Additionally, the outlines of both sides are marked using the left and right outlines on the back.
  • the part of the shoulder that is more raised is confirmed as shoulder rise, and if the difference in the degree of indentation toward the left and right central axes of the outlines of both sides is more than the preset inclination, the part that is more indented is confirmed as the side indentation. You can check by entering.
  • the criteria for determining these may not be specified and may vary depending on artificial intelligence learning using a learning dataset containing professional diagnosis information. It may vary depending on reinforcement learning, which will be described later. In either case, there will be a change in the direction of increasing the accuracy of professional diagnosis information.
  • the protrusion of one side of the back is confirmed by the protrusion line on one of the left and right sides of the back, the raising of the other shoulder is not confirmed through the outlines of both shoulders, and the retraction of one side is confirmed through the outlines of both sides. If this is not the case, it is confirmed as type 1 scoliosis, and one-sided back protrusion is confirmed by the protruding line on one of the left and right sides of the back, the other shoulder is raised through the outline of both shoulders, and one side is confirmed by the outline of both sides.
  • the retraction is not confirmed, it is confirmed as type 2 scoliosis, and the protrusion of one side of the back is confirmed by the protrusion line on one of the left and right sides of the back, and the protrusion of the other shoulder is not confirmed by the outline of both shoulders and the outline of both sides. If one side is confirmed to be indented, it is confirmed as type 3 scoliosis, one side of the back is protruded by the protruding line on one of the left and right sides of the back, the other shoulder is raised through the outlines of both shoulders, and both sides are confirmed to be raised.
  • Type 4 scoliosis is confirmed when one side is confirmed through the outline of the back, and when one side of the back is not protruded by the protrusion line on one side of the left or right side of the back, the other shoulder is raised through the outline of both shoulders. If it is not confirmed and unilateral indentation is confirmed through the outlines of both sides, it can be confirmed as type 5 scoliosis.
  • the guidelines included in the image are automatically recognized and used as one of the input data.
  • the external image collected in the image collection unit 110 is input to the artificial intelligence learning unit 100, and a guideline is added to the input external image and input to the artificial intelligence learning unit 100. Additionally, professional diagnosis information corresponding to the input external image is further input.
  • the artificial intelligence learning unit 100 generates an artificial intelligence model by learning a learning dataset using the appearance image with the guideline added as input data and the professional diagnosis information as output data (S110).
  • an external image is input to the artificial intelligence model by the image input unit 210 (S210), and when a guideline corresponding to the input external image is input together (S220), the artificial intelligence model determines whether the image is normal or one of multiple types.
  • Scoliosis information indicating one type of scoliosis is calculated and output by the output unit 220 (S230).
  • the hospital that developed the present invention can introduce the present invention to schools or related government departments [A1], and it can be introduced to students or parents through schools, etc. [A2].
  • Diagnosed information can be transmitted to the hospital.
  • the external image input by the user and the scoliosis information calculated by the artificial intelligence model are transmitted to the preset hospital medical management system 300 (S310) [C1]. This transmission can be easily accomplished through the communication module of the smartphone.
  • the hospital can check this and set a visit schedule [C2], and when the patient visits the hospital and receives a direct diagnosis by a professional medical staff, professional diagnosis information is generated and uploaded to the hospital medical management system 300 [C3]. Therefore, the hospital medical management system 300 confirms the external image and the corresponding professional diagnosis information, sets it as an additional learning dataset, and transmits it to the artificial intelligence learning unit 100. Accordingly, the artificial intelligence model is reinforced learning (S320)[C4].
  • the patient i.e., the user of the present invention
  • a terminal such as a smartphone
  • This is input into the artificial intelligence model through a program or application installed on the terminal, and guidelines are also input.
  • information on the changed scoliosis according to the changed external image, that is, scoliosis of either normal or multiple types is confirmed (S410) [C5]. If the patient confirms this, he or she can self-diagnose the progress of the treatment, and it is transmitted to the hospital medical management system (300) to be confirmed by the medical staff, allowing the patient to track the progress of the treatment and, if necessary, change the brace or exercise method or set a visit schedule again. You can.
  • the applicant entered and executed some data into the program and performed a verification experiment comparing the data with the professional diagnosis results of professional medical staff on patients representing the data, and found that the accuracy was about 80%. Appearance was confirmed.

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Abstract

La présente invention concerne un procédé de création d'un modèle d'intelligence artificielle et un procédé de fourniture d'informations l'utilisant, des utilisateurs téléchargeant en amont des images de leurs dos prises avec des smartphones ou des dispositifs similaires vers une application ou un programme installé sur le smartphone pour obtenir des informations relatives à une scoliose.
PCT/KR2023/014735 2022-09-26 2023-09-26 Procédé faisant appel à l'intelligence artificielle pour fournir des informations sur une scoliose WO2024071943A1 (fr)

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