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WO2022114819A1 - Ai osteoporosis evaluation device and method - Google Patents

Ai osteoporosis evaluation device and method Download PDF

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Publication number
WO2022114819A1
WO2022114819A1 PCT/KR2021/017548 KR2021017548W WO2022114819A1 WO 2022114819 A1 WO2022114819 A1 WO 2022114819A1 KR 2021017548 W KR2021017548 W KR 2021017548W WO 2022114819 A1 WO2022114819 A1 WO 2022114819A1
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WIPO (PCT)
Prior art keywords
osteoporosis
reading
user
fluoroscopic image
bone density
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PCT/KR2021/017548
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French (fr)
Korean (ko)
Inventor
원영준
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Catholic Kwandong University Industry Cooperation Foundation
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Catholic Kwandong University Industry Cooperation Foundation
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Priority claimed from KR1020200159465A external-priority patent/KR102778165B1/en
Priority claimed from KR1020200159462A external-priority patent/KR102775678B1/en
Priority claimed from KR1020200159474A external-priority patent/KR102775679B1/en
Priority claimed from KR1020200159472A external-priority patent/KR102756572B1/en
Application filed by Catholic Kwandong University Industry Cooperation Foundation filed Critical Catholic Kwandong University Industry Cooperation Foundation
Publication of WO2022114819A1 publication Critical patent/WO2022114819A1/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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

Definitions

  • the present invention relates to an AI osteoporosis reading apparatus and method, and more particularly, to an AI osteoporosis reading apparatus and method for determining whether a patient suspected of osteoporosis has osteoporosis through AI learning based on a fluoroscopic image of the osteoporotic patient.
  • bone mineral density BMD
  • osteoporosis in which a hole is formed in the bone. If you suffer from osteoporosis, even a small impact causes very serious consequences, such as broken bones and not easily joining broken bones, so periodic diagnosis is required to prevent osteoporosis or worsen symptoms.
  • Osteoporosis refers to a state in which bone mass is excessively reduced compared to that of a normal person, and is a clinical condition accompanied by fractures and deformation of bone morphology. That is, osteoporosis is a condition in which bone mass is abnormally reduced and is a pathological condition accompanied by fractures of the spine and femur, and bone deformation.
  • Conventional bone density information may be obtained by a medical professional reading a picture through a patient, such as an X-ray or ultrasound.
  • a medical professional who reads a fluoroscopic picture of a patient has low skill level, there is a risk of misdiagnosis.
  • AI artificial intelligence
  • the need to accurately determine whether a patient has osteoporosis by reading a fluoroscopic picture of the patient is emerging.
  • An object of the present invention is to provide an AI osteoporosis reading apparatus and method capable of determining whether a suspected patient has osteoporosis through AI learning based on a fluoroscopic image of the osteoporotic patient.
  • Another technical object of the present invention is to provide an AI osteoporosis reading apparatus and method capable of checking changes in bone density by storing fluoroscopic images for each user according to time series, and determining whether or not osteoporosis is based thereon.
  • a database in which fluoroscopic images of a plurality of osteoporosis patients are stored; Measuring unit for measuring the fluoroscopic image of the suspected patient; And based on the reading range, which is a specific region for measuring bone density in the fluoroscopic image stored in the database, through AI learning, the bone density reading information for the osteoporosis patient is generated, and the bone density reading information and the suspicious patient received from the measurement unit Comparing the bone density of the fluoroscopic images of the determining unit to determine whether the suspected patient has osteoporosis; Including, wherein the reading range includes a wrist portion of the fluoroscopic image, it provides an AI osteoporosis reading device.
  • the reading range includes: a) a range including the distal portion of the wrist, the inner area of the radius and steel frame, and the outer area of the radius and steel frame, b) a range including at least one of the middle portion of the clavicle, the distal portion and the lower portion of the humerus head; c) at least one of mandibular cortical bone thickness and mandibular medullary bone density; device can be provided.
  • the reading range may provide an AI osteoporosis reading device in which a preset area A is set from a preset tooth position of the mandible, but is set to an area excluding the teeth.
  • the read information in the fluoroscopic image of the osteoporosis patient, characterized in that at least one of the preset area A and oral length data is used, wherein the oral length data includes the mandible and the total length of the teeth, the tooth head and the gums. It is possible to provide an AI osteoporosis reader, which is any one of the length and the length of the eroded gum.
  • the database may include: a data unit for each user in which a perspective image for each user is stored; and a learning data unit for storing fluoroscopy images of a plurality of osteoporosis patients, wherein the data unit for each user stores fluoroscopic images for each user measured by the measurement unit for each user, providing an AI osteoporosis reading device can do.
  • the determination unit by comparing the fluoroscopic image for a specific user received from the measurement unit with the fluoroscopic image for the specific user pre-stored in the user-specific data unit, provides an AI osteoporosis reading device capable of confirming the state change of bone density can do.
  • storing a plurality of fluoroscopic images of osteoporosis patients in a database generating, by a determination unit, bone density reading information on bone density of an osteoporosis patient through AI learning based on a reading range that is a specific region for measuring bone density in the fluoroscopic image stored in the database; measuring, by the measuring unit, a fluoroscopic image of the suspected patient; storing the fluoroscopy image of the suspected patient measured by the measurement unit in the database; Comprising the step of determining, by the determination unit, the bone density reading information and the bone density of the fluoroscopic image of the suspected patient received from the measurement unit to determine whether the suspected patient has osteoporosis, wherein the reading range includes the wrist portion of the fluoroscopic image It provides a method for reading AI osteoporosis, characterized in that.
  • the reading range includes: a) a range including the distal portion of the wrist, the inner area of the radius and steel frame, and the outer area of the radius and steel frame, b) a range including at least one of the middle portion of the clavicle, the distal portion and the lower portion of the humerus head; c) at least one of mandibular cortical bone thickness and mandibular medullary bone density; method can be provided.
  • the reading range may provide an AI osteoporosis reading method in which a preset area A is set from a preset tooth position of the mandible, but is set to an area excluding the teeth.
  • the read information in the fluoroscopic image of the osteoporosis patient, characterized in that at least one of the preset area A and oral length data is used, wherein the oral length data includes the mandible and the total length of the teeth, the tooth head and the gums. It is possible to provide an AI osteoporosis reading method, which is any one of the length and the length of the eroded gum.
  • the database may include: a data unit for each user in which a perspective image for each user is stored; and a learning data unit for storing fluoroscopy images of a plurality of osteoporosis patients, wherein the storing of the fluoroscopic images of the suspected patient measured by the measurement unit in the database includes, in the data unit for each user, the fluoroscopy images measured by the measurement unit It is possible to provide an AI osteoporosis reading method, further comprising the step of storing the fluoroscopy image for each user for each user.
  • the determination unit After the step of storing the fluoroscopy image of the suspected patient measured by the measurement unit in the database, the determination unit, the fluoroscopy image for the specific user received from the measurement unit, is pre-stored in the data unit for each user. Comparing with the image, it is possible to provide an AI osteoporosis reading method further comprising the step of confirming a change in the state of bone density.
  • the change in bone density can be checked, and based on this, it is possible to determine whether osteoporosis is present.
  • FIG. 1 is a view showing an AI osteoporosis reading apparatus according to an embodiment of the present invention.
  • FIGS. 2 to 6 are diagrams illustrating a reading range in a perspective image according to various embodiments of the present invention.
  • FIG. 7 is a flowchart illustrating an AI osteoporosis reading method according to an embodiment of the present invention.
  • FIG. 8 is a flowchart of storing information for each user according to an embodiment of the present invention.
  • FIGS. 2 to 6 are diagrams showing a reading range in a fluoroscopic image according to various embodiments of the present invention.
  • the AI osteoporosis reading apparatus 10 may include a measurement unit 102 , a control unit 104 , a determination unit 106 , a communication unit 108 , and a database 110 .
  • the measurement unit 102 may generate a fluoroscopic image necessary for reading bone density by photographing the user.
  • the user is a person who takes a fluoroscopic image through the AI osteoporosis reading device 10 , and may mean an osteoporosis patient or a patient suspected of osteoporosis.
  • the measurement unit 102 may generate a fluoroscopic image necessary for reading a patient's bone density, and may provide it to the control unit 104 .
  • the fluoroscopic image is a photograph for reading bone density, and may include at least one of an X-ray image, a tomography image, an ultrasound image, and a magnetic resonance image (MRI).
  • the tomography may be, for example, CT.
  • the magnetic resonance may be, for example, nuclear magnetic resonance (NMR).
  • the measurement unit 102 may check a reading range before taking a fluoroscopic image, and may take a fluoroscopic image for the reading range.
  • the reading range is a specific area for measuring bone density in the fluoroscopic image, and may include, for example, at least one of a distal part of the wrist, an inner area of a radius and a steel frame, and an outer area of a radius and a steel frame.
  • the reading range is described as including at least one of the distal part of the wrist, the inner area of the radius and the steel frame, and the outer area of the radius and the steel frame, but is not limited thereto, and the reading range can be set to various parts according to the user's setting. of course there is
  • the reading range may belong to one of a range of 1.5 cm to 2.5 cm of the distal wrist and a range of 3 cm to 5 cm between the radius and the steel frame.
  • the reading range is in the range of 0.5 cm to 1.5 cm of the middle part of the clavicle, the range of 0.5 cm to 1.5 cm of the distal part by dividing the distal part into 3 parts, and the range of 0.5 cm to 1.5 cm of the part located far from the ribs, and 0.5 cm of the lower part of the humerus head to 1.5 cm.
  • the reading range may be set to an area excluding teeth while setting a preset area A from a preset tooth position of the mandible.
  • the reading range may be any one of L2, L3, and L4 of the thoracolumbar region.
  • the reading range may include at least one of the femoral neck and the femoral electron load among the thighs, or an angle formed by the femoral neck and the femoral electron load.
  • the controller 104 may control the AI osteoporosis measuring device 10 . Specifically, the control unit 104 may control the measurement unit 102 to take a fluoroscopic image of the user. Also, the control unit 104 may control the communication unit 106 to receive external information through communication with an external device and to transmit a see-through image.
  • the determination unit 106 may generate bone density reading information on the bone density of the osteoporosis patient through AI learning based on the reading range, which is a specific region for measuring bone density in the fluoroscopic image stored in the database 110 .
  • the determination unit 106 may be referred to as, for example, a “processor”.
  • the determination unit 106 may be implemented using at least one of a server, a computer, a PCB, a logic circuit, and a laptop.
  • the determination unit 106 may include an AI engine.
  • the AI engine may be referred to as an “artificial intelligence engine”.
  • the AI engine may perform AI learning using machine learning and/or deep learning.
  • AI learning may mean generating bone density reading information for the osteoporosis patient's bone density based on the reading range in the fluoroscopic image stored in the database 110 .
  • the bone density reading information may refer to information about a numerical value or an image about the bone density of an osteoporosis patient.
  • the determination unit 106 may determine whether the suspected patient has osteoporosis using the user medical history information, the bone density reading information, and the fluoroscopic image of the suspected patient.
  • the user medical history information may mean an individual's osteoporosis and fracture risk factors, disease history, drug history, personal genetic information, and the like.
  • the determination unit 106 compares and analyzes the bone density reading information generated through AI learning and the fluoroscopic image of the suspected patient measured by the measurement unit 102, but different reference points for each user through the user history information of the suspected patient established, and using this, it is possible to determine whether the suspected patient has osteoporosis.
  • the determination unit 106 may predict a change in the bone density state of the user by AI learning the change in the state of bone density for each user. Specifically, the determination unit 106 compares the perspective image of the specific user received from the measurement unit 102 with the perspective image of the specific user pre-stored in the database unit 110 for the specific user (ie, the measurement unit 102). A change in the state of bone density of a specific user of the fluoroscopy image received from In addition, the determination unit 106 AI learns the change in the fluoroscopic image for each user stored in the database 110, and compares the change in the bone density state of the specific user identified above with the AI learning result to predict the change in the bone density state of the user. can
  • the communication unit 108 may communicate with an external organization (not shown).
  • the external organization refers to an external device of an external organization, and may mean a server, system, or terminal of the external organization.
  • the communication unit 108 may transmit and receive the first signal S1 to and from an external organization.
  • the first signal S1 may include data.
  • the first signal S1 may include a fluoroscopic image necessary for reading osteoporosis.
  • the database 110 may store data necessary for the AI osteoporosis reading device 10 .
  • the database 110 may store fluoroscopic images of a plurality of osteoporosis patients.
  • the fluoroscopic images of the plurality of osteoporosis patients may include a fluoroscopic image taken through the measurement unit 102 as well as a fluoroscopic image received from the outside through the communication unit 108 .
  • the database 110 may include a data unit for each user and a learning data unit.
  • the data unit for each user stores the perspective images for each user in time series, and the perspective images for each user measured by the measurement unit 102 may be continuously updated for each user.
  • the learning data unit may store fluoroscopic images of a plurality of osteoporosis patients.
  • FIG. 7 is a flowchart illustrating an AI osteoporosis reading method according to an embodiment of the present invention.
  • the AI osteoporosis reading device 10 stores the fluoroscopic image of the osteoporosis patient ( S302 ). Specifically, the AI osteoporosis reading device 10 may store a fluoroscopic image of an osteoporosis patient. The AI osteoporosis reading device 10 may receive and store a fluoroscopic image of an osteoporosis patient from an external device, or may be captured and stored by the measurement unit 102 .
  • the AI osteoporosis reading device 10 may store the fluoroscopy image of the suspected patient captured through the measurement unit 102 in a pre-stored fluoroscopy image storage location of the user. Specifically, the AI osteoporosis reading apparatus 10 may store the fluoroscopic images for each user measured by the measurement unit 102 in the database 110 for each user.
  • the AI osteoporosis reading device 10 may check the bone density state change of the suspected patient.
  • the AI osteoporosis reading device 10 is a fluoroscopic image received from the measurement unit 102 when the previous fluoroscopy image of a specific user of the fluoroscopic image received from the measurement unit 102 is pre-stored in the database 110 .
  • a change in the state of bone density can be confirmed by comparing the fluoroscopic image of a specific user with the pre-stored fluoroscopy image.
  • the AI osteoporosis reading device 10 may confirm the reading range in the fluoroscopic image of the suspected patient (S308). Specifically, the AI osteoporosis reading device 10 may identify a position corresponding to the bone density reading information in the fluoroscopic image of the suspected patient.
  • the AI osteoporosis reading device 10 may determine whether the suspected patient has osteoporosis. Specifically, the AI osteoporosis reading device 10 may determine whether the suspected patient has osteoporosis by comparing the reading range confirmed in the fluoroscopic image of the suspected patient with the bone density reading information generated by AI learning.
  • the AI osteoporosis reading device 10 may receive basic user information ( S402 ).
  • the user basic information is basic information of a user who uses the AI osteoporosis reading device 10, and may mean basic information of the user, such as name, age, gender, blood type, and the like.
  • the AI osteoporosis reading apparatus 10 may determine whether the received user information is pre-stored information (S404). Specifically, the AI osteoporosis reading device 10 stores the user's basic information together with the fluoroscopic image of the user from the measurement unit 102 , and may check whether the corresponding user basic information is pre-stored information.
  • the AI osteoporosis reading apparatus 10 may store the user's fluoroscopy image measured by the measurement unit 102 together with the user's pre-stored fluoroscopic image (S406).
  • the AI osteoporosis reading device 10 stores fluoroscopy images for each user, so that changes in bone density of the patient can be checked in time series.

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Abstract

The present invention may provide an AI osteoporosis evaluation device comprising: a database in which fluoroscopic images of a plurality of osteoporosis patients are stored; a measurement unit for performing measurements on fluoroscopic images of a suspected patient; and a determination unit which generates bone mineral density evaluation information about the bone mineral density of an osteoporosis patient through AI learning on the basis of an evaluation range that is a specific portion in which the bone mineral density is measured in the fluoroscopic images stored in the database, and compares the bone mineral density evaluation information with the bone mineral density of the suspected patient's fluoroscopic images received from the measurement unit, wherein the evaluation range includes a wrist portion of the fluoroscopic images.

Description

AI 골다공증 판독장치 및 방법AI osteoporosis reader and method

본 발명은 AI 골다공증 판독장치 및 방법에 관한 것으로, 더욱 상세하게는 골다공증 환자의 투시 이미지를 기반으로 AI 학습을 통해 골다공증 의심 환자의 골다공증 여부를 판단하는 AI 골다공증 판독장치 및 방법에 관한 것이다.The present invention relates to an AI osteoporosis reading apparatus and method, and more particularly, to an AI osteoporosis reading apparatus and method for determining whether a patient suspected of osteoporosis has osteoporosis through AI learning based on a fluoroscopic image of the osteoporotic patient.

사람들은 나이가 들어감에 따라 뼈의 밀도가 점차 저하되며, 이와 같이 골밀도(BMD; Bone Mineral Density)가 감소되면서 뼈에 구멍이 형성되는 골다공증에 걸리게 된다. 골다공증에 걸리게 되면, 작은 충격에도 뼈가 부러지고 부러진 뼈가 쉽게 접합되지 않는 등 매우 심각한 결과가 초래되므로, 골다공증을 예방하거나 증상의 악화를 방지하기 위하여 주기적인 진단이 요구된다.As people age, bone density gradually decreases, and as such, bone mineral density (BMD) decreases, leading to osteoporosis, in which a hole is formed in the bone. If you suffer from osteoporosis, even a small impact causes very serious consequences, such as broken bones and not easily joining broken bones, so periodic diagnosis is required to prevent osteoporosis or worsen symptoms.

골다공증(Osteoporosis)은 정상인에 비하여 과도하게 골량이 감소된 상태를 일컫는 것으로 골절 및 골 형태의 변형 등을 수반하는 임상적 상태이다. 즉, 골다 공증은 비정상적으로 골량이 감소된 상태로 척추 및 대퇴부 등의 골절, 골의 변형 등을 동반하는 병적인 상태이다. Osteoporosis refers to a state in which bone mass is excessively reduced compared to that of a normal person, and is a clinical condition accompanied by fractures and deformation of bone morphology. That is, osteoporosis is a condition in which bone mass is abnormally reduced and is a pathological condition accompanied by fractures of the spine and femur, and bone deformation.

종래 골밀도 정보는, 엑스레이 또는 초음파와 같이 환자를 투시하는 사진을 의료 전문가가 판독함으로써, 얻어질 수 있다. 그런데 환자를 투시한 사진을 판독하는 의료 전문가의 숙련도가 낮은 경우, 오진이 발생할 우려가 있다. 인공지능 (AI; Artificial Intelligence)의 발달에 따라 환자를 투시한 사진을 인공지능이 판독함으로써, 환자의 골다공증 여부를 정확히 판단할 필요성이 대두되고 있다.Conventional bone density information may be obtained by a medical professional reading a picture through a patient, such as an X-ray or ultrasound. However, when a medical professional who reads a fluoroscopic picture of a patient has low skill level, there is a risk of misdiagnosis. With the development of artificial intelligence (AI), the need to accurately determine whether a patient has osteoporosis by reading a fluoroscopic picture of the patient is emerging.

본 발명이 이루고자 하는 기술적 과제는, 골다공증 환자의 투시 이미지를 기반을 AI 학습을 통해 의심 환자의 골다공증 여부를 판단할 수 있는 AI 골다공증 판독장치 및 방법을 제공하는 것이다.An object of the present invention is to provide an AI osteoporosis reading apparatus and method capable of determining whether a suspected patient has osteoporosis through AI learning based on a fluoroscopic image of the osteoporotic patient.

본 발명의 다른 기술적 과제는, 사용자별로 투시 이미지를 시계열에 따라 저장함으로써, 골밀도의 변화를 확인하고, 이를 기반으로 골다공증 여부를 판단할 수 있는 AI 골다공증 판독장치 및 방법을 제공하는 것이다.Another technical object of the present invention is to provide an AI osteoporosis reading apparatus and method capable of checking changes in bone density by storing fluoroscopic images for each user according to time series, and determining whether or not osteoporosis is based thereon.

본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 기술적 과제로 제한되지 않으며, 언급되지 않은 또 다른 기술적 과제들은 아래의 기재로부터 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자에게 명확하게 이해될 수 있을 것이다.The technical problems to be achieved by the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. There will be.

본 발명의 일 실시예에 따르면, 복수의 골다공증 환자의 투시 이미지가 저장되는 데이터베이스; 의심 환자의 투시 이미지를 측정하는 측정부; 및 상기 데이터베이스에 저장된 투시 이미지에서 골밀도를 측정하는 특정 부위인 판독 범위를 바탕으로 AI 학습을 통해 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성하고, 상기 골밀도 판독 정보와 상기 측정부로부터 수신 받은 의심 환자의 투시 이미지의 골밀도를 비교하여 의심 환자의 골다공증 여부를 판단하는 판단부;를 포함하되, 상기 판독 범위는 투시 이미지의 손목 부분을 포함하는 것을 특징으로 하는, AI 골다공증 판독장치를 제공한다.According to an embodiment of the present invention, a database in which fluoroscopic images of a plurality of osteoporosis patients are stored; Measuring unit for measuring the fluoroscopic image of the suspected patient; And based on the reading range, which is a specific region for measuring bone density in the fluoroscopic image stored in the database, through AI learning, the bone density reading information for the osteoporosis patient is generated, and the bone density reading information and the suspicious patient received from the measurement unit Comparing the bone density of the fluoroscopic images of the determining unit to determine whether the suspected patient has osteoporosis; Including, wherein the reading range includes a wrist portion of the fluoroscopic image, it provides an AI osteoporosis reading device.

상기 판독 범위는, a) 손목의 원위부, 요골과 철골의 내부 면적 및 요골과 철골의 외부 면적을 포함하는 범위, b) 쇄골의 중위부, 원위부 및 상완골두의 하부 중 적어도 하나를 포함하는 범위, c) 하악 피질골 두께 및 하악 수질골 밀도 중 적어도 하나, 및 d) 척추뼈 L2, L3, L4, 대퇴경부 및 대퇴전자하 중 적어도 하나를 포함하는 범위 중 어느 하나인 것을 특징으로 하는, AI 골다공증 판독장치를 제공할 수 있다.The reading range includes: a) a range including the distal portion of the wrist, the inner area of the radius and steel frame, and the outer area of the radius and steel frame, b) a range including at least one of the middle portion of the clavicle, the distal portion and the lower portion of the humerus head; c) at least one of mandibular cortical bone thickness and mandibular medullary bone density; device can be provided.

상기 판독 범위는, 기 설정된 하악골의 치아 위치에서부터 기 설정된 면적 A를 설정하되, 치아를 제외한 영역으로 설정되는, AI 골다공증 판독장치를 제공할 수 있다.The reading range may provide an AI osteoporosis reading device in which a preset area A is set from a preset tooth position of the mandible, but is set to an area excluding the teeth.

상기 판독 정보는, 상기 골다공증 환자의 상기 투시 이미지에서, 상기 기 설정된 면적 A 및 구강 길이 데이터 중 적어도 하나를 이용하는 것을 특징으로 하되, 상기 구강 길이 데이터는, 하악 및 치아 전체 포함 길이, 치아 두부와 잇몸 길이 및 부식되어 들어간 잇몸의 길이 중 어느 하나인, AI 골다공증 판독장치를 제공할 수 있다.The read information, in the fluoroscopic image of the osteoporosis patient, characterized in that at least one of the preset area A and oral length data is used, wherein the oral length data includes the mandible and the total length of the teeth, the tooth head and the gums. It is possible to provide an AI osteoporosis reader, which is any one of the length and the length of the eroded gum.

상기 데이터베이스는, 각 사용자에 대한 투시 이미지가 저장된 사용자별 데이터부; 및 복수의 골다공증 환자의 투시 이미지가 저장되는 학습 데이터부;를 포함하고, 상기 사용자별 데이터부에는, 상기 측정부에서 측정되는 각 사용자에 따른 투시 이미지가 사용자별로 저장되는, AI 골다공증 판독장치를 제공할 수 있다.The database may include: a data unit for each user in which a perspective image for each user is stored; and a learning data unit for storing fluoroscopy images of a plurality of osteoporosis patients, wherein the data unit for each user stores fluoroscopic images for each user measured by the measurement unit for each user, providing an AI osteoporosis reading device can do.

상기 판단부는, 상기 측정부로부터 수신받은 특정 사용자에 대한 투시 이미지를 상기 사용자별 데이터부에 기 저장된 특정 사용자에 대한 투시 이미지와 비교하여, 골밀도의 상태 변화를 확인할 수 있는, AI 골다공증 판독장치를 제공할 수 있다.The determination unit, by comparing the fluoroscopic image for a specific user received from the measurement unit with the fluoroscopic image for the specific user pre-stored in the user-specific data unit, provides an AI osteoporosis reading device capable of confirming the state change of bone density can do.

본 발명의 일 실시예에 따르면, 데이터베이스에 복수의 골다공증 환자의 투시 이미지가 저장되는 단계; 판단부가, 상기 데이터베이스에 저장된 투시 이미지에서 골밀도를 측정하는 특정 부위인 판독 범위를 바탕으로 AI 학습을 통해 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성하는 단계; 측정부가, 의심 환자의 투시 이미지를 측정하는 단계; 상기 데이터베이스에 상기 측정부에서 측정된 의심 환자의 상기 투시 이미지가 저장되는 단계; 상기 판단부가, 상기 골밀도 판독 정보와 상기 측정부로부터 수신 받은 의심 환자의 투시 이미지의 골밀도를 비교하여 의심 환자의 골다공증 여부를 판단하는 단계를 포함하되, 상기 판독 범위는 투시 이미지의 손목 부분을 포함하는 것을 특징으로 하는, AI 골다공증 판독 방법을 제공한다.According to an embodiment of the present invention, storing a plurality of fluoroscopic images of osteoporosis patients in a database; generating, by a determination unit, bone density reading information on bone density of an osteoporosis patient through AI learning based on a reading range that is a specific region for measuring bone density in the fluoroscopic image stored in the database; measuring, by the measuring unit, a fluoroscopic image of the suspected patient; storing the fluoroscopy image of the suspected patient measured by the measurement unit in the database; Comprising the step of determining, by the determination unit, the bone density reading information and the bone density of the fluoroscopic image of the suspected patient received from the measurement unit to determine whether the suspected patient has osteoporosis, wherein the reading range includes the wrist portion of the fluoroscopic image It provides a method for reading AI osteoporosis, characterized in that.

상기 판독 범위는, a) 손목의 원위부, 요골과 철골의 내부 면적 및 요골과 철골의 외부 면적을 포함하는 범위, b) 쇄골의 중위부, 원위부 및 상완골두의 하부 중 적어도 하나를 포함하는 범위, c) 하악 피질골 두께 및 하악 수질골 밀도 중 적어도 하나, 및 d) 척추뼈 L2, L3, L4, 대퇴경부 및 대퇴전자하 중 적어도 하나를 포함하는 범위 중 어느 하나인 것을 특징으로 하는,AI 골다공증 판독 방법을 제공할 수 있다.The reading range includes: a) a range including the distal portion of the wrist, the inner area of the radius and steel frame, and the outer area of the radius and steel frame, b) a range including at least one of the middle portion of the clavicle, the distal portion and the lower portion of the humerus head; c) at least one of mandibular cortical bone thickness and mandibular medullary bone density; method can be provided.

상기 판독 범위는, 기 설정된 하악골의 치아 위치에서부터 기 설정된 면적 A를 설정하되, 치아를 제외한 영역으로 설정되는, AI 골다공증 판독 방법을 제공할 수 있다.The reading range may provide an AI osteoporosis reading method in which a preset area A is set from a preset tooth position of the mandible, but is set to an area excluding the teeth.

상기 판독 정보는, 상기 골다공증 환자의 상기 투시 이미지에서, 상기 기 설정된 면적 A 및 구강 길이 데이터 중 적어도 하나를 이용하는 것을 특징으로 하되, 상기 구강 길이 데이터는, 하악 및 치아 전체 포함 길이, 치아 두부와 잇몸 길이 및 부식되어 들어간 잇몸의 길이 중 어느 하나인, AI 골다공증 판독 방법을 제공할 수 있다.The read information, in the fluoroscopic image of the osteoporosis patient, characterized in that at least one of the preset area A and oral length data is used, wherein the oral length data includes the mandible and the total length of the teeth, the tooth head and the gums. It is possible to provide an AI osteoporosis reading method, which is any one of the length and the length of the eroded gum.

상기 데이터베이스는, 각 사용자에 대한 투시 이미지가 저장된 사용자별 데이터부; 및 복수의 골다공증 환자의 투시 이미지가 저장되는 학습 데이터부;를 포함하고, 상기 데이터베이스에 측정부에서 측정된 의심 환자의 투시 이미지가 저장되는 단계는, 상기 사용자별 데이터부에 상기 측정부에서 측정되는 각 사용자에 따른 투시 이미지가 사용자별로 저장되는 단계를 더 포함하는, AI 골다공증 판독 방법을 제공할 수 있다.The database may include: a data unit for each user in which a perspective image for each user is stored; and a learning data unit for storing fluoroscopy images of a plurality of osteoporosis patients, wherein the storing of the fluoroscopic images of the suspected patient measured by the measurement unit in the database includes, in the data unit for each user, the fluoroscopy images measured by the measurement unit It is possible to provide an AI osteoporosis reading method, further comprising the step of storing the fluoroscopy image for each user for each user.

상기 데이터베이스에 측정부에서 측정된 의심 환자의 투시 이미지가 저장되는 단계 이후에, 상기 판단부는, 상기 측정부로부터 수신받은 특정 사용자에 대한 투시 이미지를 상기 사용자별 데이터부에 기 저장된 특정 사용자에 대한 투시 이미지와 비교하여, 골밀도의 상태 변화를 확인하는 단계를 더 포함하는, AI 골다공증 판독 방법을 제공할 수 있다.After the step of storing the fluoroscopy image of the suspected patient measured by the measurement unit in the database, the determination unit, the fluoroscopy image for the specific user received from the measurement unit, is pre-stored in the data unit for each user. Comparing with the image, it is possible to provide an AI osteoporosis reading method further comprising the step of confirming a change in the state of bone density.

본 발명의 실시예에 의하면, 환자의 투시 이미지를 기반을 AI 학습을 통해 의심 환자의 골다공증 여부를 판단할 수 있다.According to an embodiment of the present invention, it is possible to determine whether the suspected patient has osteoporosis through AI learning based on the patient's fluoroscopic image.

또한, 사용자별로 투시 이미지를 시계열에 따라 저장함으로써, 골밀도의 변화를 확인하고, 이를 기반으로 골다공증 여부를 판단할 수 있다.In addition, by storing the fluoroscopic images for each user according to time series, the change in bone density can be checked, and based on this, it is possible to determine whether osteoporosis is present.

도 1은 본 발명의 일 실시예에 따른 AI 골다공증 판독장치를 나타낸 도면이다.1 is a view showing an AI osteoporosis reading apparatus according to an embodiment of the present invention.

도 2 내지 도 6은 본 발명의 여러 실시예에 따른 투시 이미지에서의 판독 범위를 나타내는 도면이다.2 to 6 are diagrams illustrating a reading range in a perspective image according to various embodiments of the present invention.

도 7은 본 발명의 일 실시예에 따른 AI 골다공증 판독 방법을 나타내는 순서도이다.7 is a flowchart illustrating an AI osteoporosis reading method according to an embodiment of the present invention.

도 8는 본 발명의 일 실시예에 따른 사용자 별 정보를 저장하는 순서도이다.8 is a flowchart of storing information for each user according to an embodiment of the present invention.

이하, 도면을 참조하여 본 발명의 구체적인 실시형태를 설명하기로 한다. 그러나 이는 예시에 불과하며 본 발명은 이에 제한되지 않는다.Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. However, this is merely an example, and the present invention is not limited thereto.

본 발명을 설명함에 있어서, 본 발명과 관련된 공지기술에 대한 구체적인 설명이 본 발명의 요지를 불필요하게 흐릴 수 있다고 판단되는 경우에는 그 상세한 설명을 생략하기로 한다. 그리고, 후술되는 용어들은 본 발명에서의 기능을 고려하여 정의된 용어들로써 이는 사용자, 운용자의 의도 또는 관례 등에 따라 달라질 수 있다. 그러므로 그 정의는 본 명세서 전반에 걸친 내용을 토대로 내려져야 할 것이다.In the description of the present invention, if it is determined that the detailed description of the known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. In addition, the terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to intentions or customs of users and operators. Therefore, the definition should be made based on the content throughout this specification.

본 발명의 기술적 사상은 청구범위에 의해 결정되며, 이하의 실시예는 본 발명의 기술적 사상을 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자에게 효율적으로 설명하기 위한 일 수단일 뿐이다.The technical spirit of the present invention is determined by the claims, and the following examples are only one means for efficiently explaining the technical spirit of the present invention to those of ordinary skill in the art to which the present invention belongs.

도 1은 본 발명의 일 실시예에 따른 AI 골다공증 판독장치를 나타낸 도면이고, 도 2 내지 도 6은 본 발명의 여러 실시예에 따른 투시 이미지에서의 판독 범위를 나타내는 도면이다.1 is a diagram showing an AI osteoporosis reading apparatus according to an embodiment of the present invention, and FIGS. 2 to 6 are diagrams showing a reading range in a fluoroscopic image according to various embodiments of the present invention.

도 1을 참조하면, 본 발명에 따른 AI 골다공증 판독장치(10)는 측정부(102), 제어부(104), 판단부(106), 통신부(108) 및 데이터베이스(110)을 포함할 수 있다.Referring to FIG. 1 , the AI osteoporosis reading apparatus 10 according to the present invention may include a measurement unit 102 , a control unit 104 , a determination unit 106 , a communication unit 108 , and a database 110 .

측정부(102)는 사용자를 촬영하여 골밀도 판독에 필요한 투시 이미지를 생성할 수 있다. 여기서, 사용자는 AI 골다공증 판독장치(10)를 통해 투시 이미지를 촬영하는 사람으로, 골다공증 환자 또는 골다공증 의심환자를 의미할 수 있다. 측정부(102)는 환자의 골밀도 판독에 필요한 투시 이미지를 생성하고, 이를 제어부(104)에 제공할 수 있다. 예를 들어, 투시 이미지는 골밀도 판독을 위한 사진으로, 엑스레이(X-ray) 이미지, 단층촬영(tomography) 이미지, 초음파 이미지, 그리고 자기공명 이미지(MRI) 중 적어도 하나를 포함할 수 있다. 단층촬영은, 예를 들어 CT일 수 있다. 자기공명은, 예를 들어 핵자기공명(NMR, nuclear magnetic resonance)일 수 있다.The measurement unit 102 may generate a fluoroscopic image necessary for reading bone density by photographing the user. Here, the user is a person who takes a fluoroscopic image through the AI osteoporosis reading device 10 , and may mean an osteoporosis patient or a patient suspected of osteoporosis. The measurement unit 102 may generate a fluoroscopic image necessary for reading a patient's bone density, and may provide it to the control unit 104 . For example, the fluoroscopic image is a photograph for reading bone density, and may include at least one of an X-ray image, a tomography image, an ultrasound image, and a magnetic resonance image (MRI). The tomography may be, for example, CT. The magnetic resonance may be, for example, nuclear magnetic resonance (NMR).

측정부(102)는 투시 이미지 촬영 전에 판독 범위를 확인하고, 판독 범위에 대한 투시 이미지를 촬영할 수 있다. 여기서, 판독 범위란 투시 이미지에서 골밀도를 측정하는 특정 영역으로, 예를 들어 손목의 원위부, 요골과 철골의 내부 면적 및 요골과 철골의 외부 면적 중 적어도 하나를 포함할 수 있다. 여기서는 판독 범위가 손목의 원위부, 요골과 철골의 내부 면적 및 요골과 철골의 외부 면적 중 적어도 하나를 포함하는 것으로 설명하나 이에 한정되는 것은 아니며, 판독 범위는 사용자의 설정에 따라 다양한 부위로 설정될 수 있음은 물론이다.The measurement unit 102 may check a reading range before taking a fluoroscopic image, and may take a fluoroscopic image for the reading range. Here, the reading range is a specific area for measuring bone density in the fluoroscopic image, and may include, for example, at least one of a distal part of the wrist, an inner area of a radius and a steel frame, and an outer area of a radius and a steel frame. Here, the reading range is described as including at least one of the distal part of the wrist, the inner area of the radius and the steel frame, and the outer area of the radius and the steel frame, but is not limited thereto, and the reading range can be set to various parts according to the user's setting. of course there is

여기서 도 2를 참조하면, 판독 범위는 손목 원위부의 1.5 cm 내지 2.5 cm 범위, 요골과 철골의 가운데 3 cm 내지 5 cm 범위 중 한 부분에 속할 수 있다.Here, referring to FIG. 2 , the reading range may belong to one of a range of 1.5 cm to 2.5 cm of the distal wrist and a range of 3 cm to 5 cm between the radius and the steel frame.

도 3을 참조하면, 판독 범위는 쇄골의 중위부의 0.5cm 내지 1.5cm 범위, 원위부를 3등분하여 그 중 갈비뼈와 먼 부분에 위치되는 부위의 0.5cm 내지 1.5cm 범위, 상완골두의 하부의 0.5cm 내지 1.5cm 범위 중 한 부분에 속할 수 있다.Referring to FIG. 3 , the reading range is in the range of 0.5 cm to 1.5 cm of the middle part of the clavicle, the range of 0.5 cm to 1.5 cm of the distal part by dividing the distal part into 3 parts, and the range of 0.5 cm to 1.5 cm of the part located far from the ribs, and 0.5 cm of the lower part of the humerus head to 1.5 cm.

도 4를 참조하면, 판독 범위는 기 설정된 하악골의 치아 위치에서부터 기 설정된 면적 A를 설정하되, 치아를 제외한 영역으로 설정될 수 있다. Referring to FIG. 4 , the reading range may be set to an area excluding teeth while setting a preset area A from a preset tooth position of the mandible.

도 5 및 도 6을 참조하면, 판독 범위는 흉요추부의 L2, L3 및 L4 중 어느 한 부분일 수 있다. 또는, 판독 범위는 대퇴부 중 대퇴경부 또는 대퇴전자하 중 적어도 하나이거나 대퇴경부 및 대퇴전자하가 이루는 각도 등을 포함할 수 있다.5 and 6 , the reading range may be any one of L2, L3, and L4 of the thoracolumbar region. Alternatively, the reading range may include at least one of the femoral neck and the femoral electron load among the thighs, or an angle formed by the femoral neck and the femoral electron load.

제어부(104)는 AI 골다공증 측정장치(10)를 제어할 수 있다. 구체적으로, 제어부(104)는 측정부(102)가 사용자의 투시 이미지를 촬영하도록 제어할 수 있다. 또한, 제어부(104)는 통신부(106)가 외부 장치와 통신을 통해 외부 정보를 수신하고, 투시 이미지를 전송하도록 제어할 수 있다.The controller 104 may control the AI osteoporosis measuring device 10 . Specifically, the control unit 104 may control the measurement unit 102 to take a fluoroscopic image of the user. Also, the control unit 104 may control the communication unit 106 to receive external information through communication with an external device and to transmit a see-through image.

제어부(104)는 입력부(미도시)를 포함할 수 있다. 입력부는 사용자(user)로부터 입력을 획득할 수 있다. 예를 들어 입력부는 콘솔(console) 입력, 터치 입력 등을 획득할 수 있다. 제어부(110)는 입력부가 외부로부터 입력받은 신호를 기초로 AI 골다공증 판독장치(10)의 작동에 관한 명령을 획득하고, 제어할 수 있다.The control unit 104 may include an input unit (not shown). The input unit may obtain an input from a user. For example, the input unit may obtain a console input, a touch input, and the like. The control unit 110 may obtain and control a command related to the operation of the AI osteoporosis reading apparatus 10 based on a signal input by the input unit from the outside.

판단부(106)는 데이터베이스(110)에 저장된 투시 이미지에서 골밀도를 측정하는 특정 부위인 판독 범위를 바탕으로 AI 학습을 통해 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성할 수 있다. 판단부(106)는 예를 들어, “프로세서(processor)”라 칭할 수 있다. 판단부(106)는, 서버(server), 컴퓨터, PCB, 논리회로, 그리고 랩톱(laptop) 중 적어도 하나를 이용하여 구현될 수 있다. 판단부(106)는, AI 엔진을 포함할 수 있다. AI 엔진은 “인공지능 엔진”이라 칭할 수 있다. AI 엔진은, 머신 러닝(machine learning) 또는/및 딥 러닝(deep learning)을 이용하여 AI 학습을 수행할 수 있다. 여기서, AI 학습이란 데이터베이스(110)에 저장된 투시 이미지에서 판독 범위를 바탕으로 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성하는 것을 의미할 수 있다. 여기서, 골밀도 판독 정보란, 골다공증 환자의 골밀도에 대한 수치 또는 이미지에 대한 정보를 의미할 수 있다.The determination unit 106 may generate bone density reading information on the bone density of the osteoporosis patient through AI learning based on the reading range, which is a specific region for measuring bone density in the fluoroscopic image stored in the database 110 . The determination unit 106 may be referred to as, for example, a “processor”. The determination unit 106 may be implemented using at least one of a server, a computer, a PCB, a logic circuit, and a laptop. The determination unit 106 may include an AI engine. The AI engine may be referred to as an “artificial intelligence engine”. The AI engine may perform AI learning using machine learning and/or deep learning. Here, AI learning may mean generating bone density reading information for the osteoporosis patient's bone density based on the reading range in the fluoroscopic image stored in the database 110 . Here, the bone density reading information may refer to information about a numerical value or an image about the bone density of an osteoporosis patient.

판단부(106)는 사용자 병력 정보, 골밀도 판독 정보 및 의심 환자 투시 이미지를 이용하여 의심 환자의 골다공증 여부를 판단할 수 있다. 여기서, 사용자 병력 정보란, 개인의 골다공증 및 골절 위험 인자, 질병력, 약물력, 개인 유전자 정보 등을 의미할 수 있다. 구체적으로, 판단부(106)는 AI 학습을 통해 생성한 골밀도 판독 정보와 측정부(102)에서 측정된 의심 환자의 투시 이미지를 비교 분석하되, 의심 환자의 사용자 병력 정보를 통해 사용자 별로 서로 다른 기준점을 확립하고, 이를 이용해 의심 환자의 골다공증 여부를 판단할 수 있다.The determination unit 106 may determine whether the suspected patient has osteoporosis using the user medical history information, the bone density reading information, and the fluoroscopic image of the suspected patient. Here, the user medical history information may mean an individual's osteoporosis and fracture risk factors, disease history, drug history, personal genetic information, and the like. Specifically, the determination unit 106 compares and analyzes the bone density reading information generated through AI learning and the fluoroscopic image of the suspected patient measured by the measurement unit 102, but different reference points for each user through the user history information of the suspected patient established, and using this, it is possible to determine whether the suspected patient has osteoporosis.

판단부(106)는 각 사용자 별 골밀도 상태 변화를 AI 학습하여 해당 사용자의 골밀도 상태 변화를 예측할 수 있다. 구체적으로 판단부(106)는 측정부(102)로부터 수신받은 특정 사용자에 대한 투시 이미지를 데이터베이스부(110)에 기 저장된 특정 사용자에 대한 투시 이미지와 비교하여 특정 사용자(즉, 측정부(102)로부터 수신 받은 투시 이미지의 특정 사용자)의 골밀도의 상태 변화를 시계열적으로 확인할 수 있다. 또한, 판단부(106)는 데이터베이스(110)에 저장된 사용자 별 투시 이미지의 변화를 AI 학습하고, 상기에서 확인한 특정 사용자의 골밀도 상태 변화를 AI 학습 결과와 비교하여, 해당 사용자의 골밀도 상태 변화를 예측할 수 있다.The determination unit 106 may predict a change in the bone density state of the user by AI learning the change in the state of bone density for each user. Specifically, the determination unit 106 compares the perspective image of the specific user received from the measurement unit 102 with the perspective image of the specific user pre-stored in the database unit 110 for the specific user (ie, the measurement unit 102). A change in the state of bone density of a specific user of the fluoroscopy image received from In addition, the determination unit 106 AI learns the change in the fluoroscopic image for each user stored in the database 110, and compares the change in the bone density state of the specific user identified above with the AI learning result to predict the change in the bone density state of the user. can

통신부(108)는 외부 기관(미도시)과 통신할 수 있다. 외부 기관은 외부 기관의 외부 기기를 의미하는 것으로, 외부 기관의 서버(server) 또는 시스템(system) 또는 단말기(terminal)를 의미할 수 있다. 예를 들어 통신부(108)는 외부 기관과 제1 신호(S1)를 송수신할 수 있다. 제1 신호(S1)는 데이터를 포함할 수 있다. 예를 들어 제1 신호(S1)는 골다공증 판독에 필요한 투시 이미지를 포함하는 것일 수 있다.The communication unit 108 may communicate with an external organization (not shown). The external organization refers to an external device of an external organization, and may mean a server, system, or terminal of the external organization. For example, the communication unit 108 may transmit and receive the first signal S1 to and from an external organization. The first signal S1 may include data. For example, the first signal S1 may include a fluoroscopic image necessary for reading osteoporosis.

데이터베이스(110)는 AI 골다공증 판독장치(10)에 필요한 데이터가 저장될 수 있다. 구체적으로, 데이터베이스(110)는 복수의 골다공증 환자의 투시 이미지가 저장될 수 있다. 여기서, 복수의 골다공증 환자의 투시 이미지는 측정부(102)를 통해 촬영된 투시 이미지는 물론이고, 통신부(108)를 통해 외부에서 수신한 투시 이미지를 포함할 수 있다.The database 110 may store data necessary for the AI osteoporosis reading device 10 . Specifically, the database 110 may store fluoroscopic images of a plurality of osteoporosis patients. Here, the fluoroscopic images of the plurality of osteoporosis patients may include a fluoroscopic image taken through the measurement unit 102 as well as a fluoroscopic image received from the outside through the communication unit 108 .

데이터베이스(110)는 사용자별 데이터부 및 학습 데이터부를 포함할 수 있다. 여기서, 사용자별 데이터부는 각 사용자에 대한 투시 이미지를 시계열적으로 저장하는 것으로, 측정부(102)에서 측정되는 각 사용자에 따른 투시 이미지가 사용자 별로 지속적 업데이트 될 수 있다. 또한, 학습 데이터부는 복수의 골다공증 환자의 투시 이미지를 저장할 수 있다.The database 110 may include a data unit for each user and a learning data unit. Here, the data unit for each user stores the perspective images for each user in time series, and the perspective images for each user measured by the measurement unit 102 may be continuously updated for each user. Also, the learning data unit may store fluoroscopic images of a plurality of osteoporosis patients.

도 7은 본 발명의 일 실시예에 따른 AI 골다공증 판독 방법을 나타내는 순서도이다.7 is a flowchart illustrating an AI osteoporosis reading method according to an embodiment of the present invention.

도 7을 참조하면, AI 골다공증 판독장치(10)는 골다공증 환자의 투시 이미지를 저장한다(S302). 구체적으로, AI 골다공증 판독장치(10)는 골다공증 환자의 투시 이미지를 저장할 수 있다. AI 골다공증 판독장치(10)는 골다공증 환자의 투시 이미지를 외부장치로부터 수신하여 저장할 수 있고, 측정부(102)로 촬영하여 저장할 수도 있다.Referring to FIG. 7 , the AI osteoporosis reading device 10 stores the fluoroscopic image of the osteoporosis patient ( S302 ). Specifically, the AI osteoporosis reading device 10 may store a fluoroscopic image of an osteoporosis patient. The AI osteoporosis reading device 10 may receive and store a fluoroscopic image of an osteoporosis patient from an external device, or may be captured and stored by the measurement unit 102 .

다음으로, AI 골다공증 판독장치(10)는 AI 학습을 통해 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성할 수 있다(S304). 구체적으로, AI 골다공증 판독장치(10)는 데이터베이스(110)에 저장된 투시 이미지에서 골밀도를 측정하는 특정 부위인 판독 범위를 바탕으로 AI 학습을 함으로써, 골밀도 판독 정보를 생성할 수 있다. 여기서, 판독 범위는 투시 이미지의 손목 부분을 포함할 수 있다.Next, the AI osteoporosis reading device 10 may generate bone density reading information on the bone density of the osteoporosis patient through AI learning (S304). Specifically, the AI osteoporosis reading device 10 may generate bone density reading information by performing AI learning based on the reading range, which is a specific region for measuring bone density, in the fluoroscopic image stored in the database 110 . Here, the reading range may include a wrist portion of the fluoroscopic image.

다음으로, AI 골다공증 판독장치(10)는 의심 환자의 투시 이미지를 촬영 및 저장할 수 있다(S306). 구체적으로, AI 골다공증 판독장치(10)는 측정부(102)를 통해 의심 환자의 투시 이미지를 촬영하고, 이를 데이터베이스(110)에 저장할 수 있다.Next, the AI osteoporosis reading device 10 may take and store the fluoroscopic image of the suspected patient (S306). Specifically, the AI osteoporosis reading device 10 may take a fluoroscopic image of the suspected patient through the measurement unit 102 , and store it in the database 110 .

이때, AI 골다공증 판독장치(10)는 측정부(102)를 통해 촬영된 의심 환자의 투시 이미지를 해당 사용자의 기 저장된 투시 이미지 저장 위치에 저장할 수 있다. 구체적으로, AI 골다공증 판독장치(10)는 데이터베이스(110)에 측정부(102)에서 측정되는 각 사용자에 따른 투시 이미지가 사용자별로 저장되도록 할 수 있다.In this case, the AI osteoporosis reading device 10 may store the fluoroscopy image of the suspected patient captured through the measurement unit 102 in a pre-stored fluoroscopy image storage location of the user. Specifically, the AI osteoporosis reading apparatus 10 may store the fluoroscopic images for each user measured by the measurement unit 102 in the database 110 for each user.

다음으로, AI 골다공증 판독장치(10)는 의심 환자의 골밀도 상태 변화를 확인할 수 있다. 구체적으로, AI 골다공증 판독장치(10)는 측정부(102)로부터 수신받은 투시 이미지의 특정 사용자의 이전 투시 이미지가 데이터베이스(110)에 기 저장되어 있는 경우, 측정부(102)로부터 수신받은 투시 이미지와 기 저장된 특정 사용자의 투시 이미지 비교를 통해 골밀도의 상태 변화를 확인할 수 있다.Next, the AI osteoporosis reading device 10 may check the bone density state change of the suspected patient. Specifically, the AI osteoporosis reading device 10 is a fluoroscopic image received from the measurement unit 102 when the previous fluoroscopy image of a specific user of the fluoroscopic image received from the measurement unit 102 is pre-stored in the database 110 . A change in the state of bone density can be confirmed by comparing the fluoroscopic image of a specific user with the pre-stored fluoroscopy image.

다음으로, AI 골다공증 판독장치(10)는 의심 환자의 투시 이미지에서 판독 범위를 확인할 수 있다(S308). 구체적으로, AI 골다공증 판독장치(10)는 의심 환자의 투시 이미지에서 골밀도 판독 정보에 대응되는 위치를 확인할 수 있다.Next, the AI osteoporosis reading device 10 may confirm the reading range in the fluoroscopic image of the suspected patient (S308). Specifically, the AI osteoporosis reading device 10 may identify a position corresponding to the bone density reading information in the fluoroscopic image of the suspected patient.

다음으로, AI 골다공증 판독장치는(10)는 의심 환자의 골다공증 여부를 판단할 수 있다. 구체적으로, AI 골다공증 판독장치(10)는 의심 환자의 투시 이미지에서 확인한 판독 범위와 AI 학습으로 생성된 골밀도 판독 정보를 비교함으로써, 의심 환자의 골다공증 여부를 판단할 수 있다.Next, the AI osteoporosis reading device 10 may determine whether the suspected patient has osteoporosis. Specifically, the AI osteoporosis reading device 10 may determine whether the suspected patient has osteoporosis by comparing the reading range confirmed in the fluoroscopic image of the suspected patient with the bone density reading information generated by AI learning.

도 8는 본 발명의 일 실시예에 따른 사용자 별 정보를 저장하는 순서도이다. 도 8를 참조하면, AI 골다공증 판독장치(10)는 사용자 기초 정보를 입력받을 수 있다(S402). 여기서, 사용자 기초 정보란 AI 골다공증 판독장치(10)를 사용하는 사용자의 기본 정보로, 이름, 나이, 성별, 혈액형 등 사용자의 기초 정보를 의미할 수 있다.8 is a flowchart of storing information for each user according to an embodiment of the present invention. Referring to FIG. 8 , the AI osteoporosis reading device 10 may receive basic user information ( S402 ). Here, the user basic information is basic information of a user who uses the AI osteoporosis reading device 10, and may mean basic information of the user, such as name, age, gender, blood type, and the like.

다음으로, AI 골다공증 판독장치(10)는 입력받은 사용자 정보가 기 저장된 정보인지 판단할 수 있다(S404). 구체적으로, AI 골다공증 판독장치(10)는 측정부(102)로부터 사용자의 투시 이미지와 함께 사용자 기초 정보를 저장하되, 해당 사용자 기초 정보가 기 저장된 정보인지 확인할 수 있다.Next, the AI osteoporosis reading apparatus 10 may determine whether the received user information is pre-stored information (S404). Specifically, the AI osteoporosis reading device 10 stores the user's basic information together with the fluoroscopic image of the user from the measurement unit 102 , and may check whether the corresponding user basic information is pre-stored information.

사용자 기초 정보가 기 저장되어 있는 경우, AI 골다공증 판독장치(10)는 측정부(102)에서 측정한 사용자의 투시 이미지를 해당 사용자의 기 저장된 투시 이미지와 함께 저장할 수 있다(S406). AI 골다공증 판독장치(10)는 사용자별로 투시 이미지를 저장함으로써, 해당 환자의 골밀도 변화를 시계열적으로 확인할 수 있다.If the user basic information is pre-stored, the AI osteoporosis reading apparatus 10 may store the user's fluoroscopy image measured by the measurement unit 102 together with the user's pre-stored fluoroscopic image (S406). The AI osteoporosis reading device 10 stores fluoroscopy images for each user, so that changes in bone density of the patient can be checked in time series.

만약, 사용자 기초 정보가 기장되어 있지 않는 경우, AI 골다공증 판독장치(10)는 해당 사용자의 추가 정보를 입력받고, 데이터베이스에 저장할 수 있다(S408). 구체적으로, AI 골다공증 판독장치(10)는 해당 사용자의 골다공증 관련 추가 정보(예를 들어, 측정부(102)를 통해 측정한 투시 이미지 이외에, 이전에 다른 곳에서 측정한 투시 이미지, 골밀도 수치 등)를 입력받고, 이를 데이터베이스(110)에 저장할 수 있다.If the user basic information is not recorded, the AI osteoporosis reading device 10 may receive additional information of the user and store it in the database (S408). Specifically, the AI osteoporosis reading device 10 provides the user's osteoporosis-related additional information (for example, in addition to the fluoroscopic image measured through the measurement unit 102, a fluoroscopic image previously measured elsewhere, bone density values, etc.) may be input and stored in the database 110 .

이상에서 본 발명의 대표적인 실시예들을 상세하게 설명하였으나, 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자는 상술한 실시예에 대하여 본 발명의 범주에서 벗어나지 않는 한도 내에서 다양한 변형이 가능함을 이해할 것이다. 그러므로 본 발명의 권리범위는 설명된 실시예에 국한되어 정해져서는 안 되며, 후술하는 특허 청구 범위뿐만 아니라 이 특허청구범위와 균등한 것들에 의해 정해져야 한다.Although representative embodiments of the present invention have been described in detail above, those of ordinary skill in the art to which the present invention pertains will understand that various modifications are possible without departing from the scope of the present invention with respect to the above-described embodiments. . Therefore, the scope of the present invention should not be limited to the described embodiments, and should be defined by the claims and equivalents as well as the claims to be described later.

Claims (12)

복수의 골다공증 환자의 투시 이미지가 저장되는 데이터베이스;a database in which fluoroscopic images of a plurality of osteoporosis patients are stored; 의심 환자의 투시 이미지를 측정하는 측정부; 및Measuring unit for measuring the fluoroscopic image of the suspected patient; and 상기 데이터베이스에 저장된 투시 이미지에서 골밀도를 측정하는 특정 부위인 판독 범위를 바탕으로 AI 학습을 통해 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성하고, 상기 골밀도 판독 정보와 상기 측정부로부터 수신 받은 의심 환자의 투시 이미지의 골밀도를 비교하여 의심 환자의 골다공증 여부를 판단하는 판단부;를 포함하되,Based on the reading range, which is a specific region for measuring bone density in the fluoroscopic image stored in the database, through AI learning, the bone density reading information for the osteoporosis patient is generated, and the bone density reading information and the suspicious patient received from the measurement unit. A determination unit that compares the bone density of the fluoroscopic image to determine whether the suspected patient has osteoporosis; 상기 판독 범위는 투시 이미지의 손목 부분을 포함하는 것을 특징으로 하는, AI 골다공증 판독장치.The reading range AI osteoporosis reading device, characterized in that it includes the wrist portion of the fluoroscopic image. 청구항 1에 있어서,The method according to claim 1, 상기 판독 범위는,The reading range is a) 손목의 원위부, 요골과 철골의 내부 면적 및 요골과 철골의 외부 면적을 포함하는 범위,a) a range including the distal portion of the wrist, the inner area of the radius and steel frame, and the outer area of the radius and steel frame; b) 쇄골의 중위부, 원위부 및 상완골두의 하부 중 적어도 하나를 포함하는 범위,b) a range comprising at least one of the medial portion, the distal portion of the clavicle and the lower portion of the humeral head; c) 하악 피질골 두께 및 하악 수질골 밀도 중 적어도 하나, 및c) at least one of mandibular cortical bone thickness and mandibular medullary bone density, and d) 척추뼈 L2, L3, L4, 대퇴경부 및 대퇴전자하 중 적어도 하나를 포함하는 범위d) a range including at least one of vertebrae L2, L3, L4, femoral neck and trochanteric load 중 어느 하나인 것을 특징으로 하는, AI 골다공증 판독장치.Any one, characterized in that the AI osteoporosis reading device. 청구항 2에 있어서,3. The method according to claim 2, 상기 판독 범위는,The reading range is 기 설정된 하악골의 치아 위치에서부터 기 설정된 면적 A를 설정하되, 치아를 제외한 영역으로 설정되는, AI 골다공증 판독장치.AI osteoporosis reading device, which sets a preset area A from a preset tooth position of the mandible, but is set to an area excluding the teeth. 청구항 3에 있어서,4. The method according to claim 3, 상기 판독 정보는,The read information is 상기 골다공증 환자의 상기 투시 이미지에서, 상기 기 설정된 면적 A 및 구강 길이 데이터 중 적어도 하나를 이용하는 것을 특징으로 하되,In the fluoroscopic image of the osteoporosis patient, it is characterized in that at least one of the preset area A and oral length data is used, 상기 구강 길이 데이터는, 하악 및 치아 전체 포함 길이, 치아 두부와 잇몸 길이 및 부식되어 들어간 잇몸의 길이 중 어느 하나인, AI 골다공증 판독장치.The oral length data is any one of the length including the mandible and the entire tooth, the length of the tooth head and the gum, and the length of the eroded gum, AI osteoporosis reading device. 청구항 1에 있어서,The method according to claim 1, 상기 데이터베이스는,The database is 각 사용자에 대한 투시 이미지가 저장된 사용자별 데이터부; 및a data unit for each user in which a perspective image for each user is stored; and 복수의 골다공증 환자의 투시 이미지가 저장되는 학습 데이터부;를 포함하고,Includes; learning data unit in which a plurality of osteoporosis patients fluoroscopic images are stored; 상기 사용자별 데이터부에는,In the user-specific data unit, 상기 측정부에서 측정되는 각 사용자에 따른 투시 이미지가 사용자별로 저장되는, AI 골다공증 판독장치.A fluoroscopic image according to each user measured by the measurement unit is stored for each user, AI osteoporosis reading device. 청구항 5에 있어서,6. The method of claim 5, 상기 판단부는,The judging unit, 상기 측정부로부터 수신받은 특정 사용자에 대한 투시 이미지를 상기 사용자별 데이터부에 기 저장된 특정 사용자에 대한 투시 이미지와 비교하여, 골밀도의 상태 변화를 확인할 수 있는, AI 골다공증 판독장치.AI osteoporosis reading device capable of confirming the state change of bone density by comparing the fluoroscopic image of the specific user received from the measurement unit with the fluoroscopic image of the specific user pre-stored in the data unit for each user. 데이터베이스에 복수의 골다공증 환자의 투시 이미지가 저장되는 단계;Storing a plurality of fluoroscopic images of osteoporosis patients in a database; 판단부가, 상기 데이터베이스에 저장된 투시 이미지에서 골밀도를 측정하는 특정 부위인 판독 범위를 바탕으로 AI 학습을 통해 골다공증 환자의 골밀도에 대한 골밀도 판독 정보를 생성하는 단계;generating, by a determination unit, bone density reading information on bone density of an osteoporosis patient through AI learning based on a reading range that is a specific region for measuring bone density in the fluoroscopic image stored in the database; 측정부가, 의심 환자의 투시 이미지를 측정하는 단계;measuring, by the measuring unit, a fluoroscopic image of the suspected patient; 상기 데이터베이스에 상기 측정부에서 측정된 의심 환자의 상기 투시 이미지가 저장되는 단계; 및storing the fluoroscopy image of the suspected patient measured by the measurement unit in the database; and 상기 판단부가, 상기 골밀도 판독 정보와 상기 측정부로부터 수신 받은 의심 환자의 투시 이미지의 골밀도를 비교하여 의심 환자의 골다공증 여부를 판단하는 단계를 포함하되,Comprising the step of determining, by the determination unit, the bone density reading information and the bone density of the fluoroscopic image of the suspected patient received from the measurement unit to determine whether the suspected patient has osteoporosis, 상기 판독 범위는 투시 이미지의 손목 부분을 포함하는 것을 특징으로 하는, AI 골다공증 판독 방법.AI osteoporosis reading method, characterized in that the reading range includes the wrist portion of the fluoroscopic image. 청구항 7에 있어서,8. The method of claim 7, 상기 판독 범위는,The reading range is a) 손목의 원위부, 요골과 철골의 내부 면적 및 요골과 철골의 외부 면적을 포함하는 범위,a) a range including the distal portion of the wrist, the inner area of the radius and steel frame, and the outer area of the radius and steel frame; b) 쇄골의 중위부, 원위부 및 상완골두의 하부 중 적어도 하나를 포함하는 범위,b) a range comprising at least one of the medial portion, the distal portion of the clavicle and the lower portion of the humeral head; c) 하악 피질골 두께 및 하악 수질골 밀도 중 적어도 하나, 및c) at least one of mandibular cortical bone thickness and mandibular medullary bone density, and d) 척추뼈 L2, L3, L4, 대퇴경부 및 대퇴전자하 중 적어도 하나를 포함하는 범위d) a range including at least one of vertebrae L2, L3, L4, femoral neck and trochanteric load 중 어느 하나인 것을 특징으로 하는,AI 골다공증 판독 방법.Any one, characterized in that, AI osteoporosis reading method. 청구항 8에 있어서,9. The method of claim 8, 상기 판독 범위는,The reading range is 기 설정된 하악골의 치아 위치에서부터 기 설정된 면적 A를 설정하되, 치아를 제외한 영역으로 설정되는, AI 골다공증 판독 방법.AI osteoporosis reading method, in which a preset area A is set from a preset tooth position of the mandible, but is set to an area excluding the teeth. 청구항 9에 있어서,10. The method of claim 9, 상기 판독 정보는,The read information is 상기 골다공증 환자의 상기 투시 이미지에서, 상기 기 설정된 면적 A 및 구강 길이 데이터 중 적어도 하나를 이용하는 것을 특징으로 하되,In the fluoroscopic image of the osteoporosis patient, it is characterized in that at least one of the preset area A and oral length data is used, 상기 구강 길이 데이터는, 하악 및 치아 전체 포함 길이, 치아 두부와 잇몸 길이 및 부식되어 들어간 잇몸의 길이 중 어느 하나인, AI 골다공증 판독 방법.The oral length data is any one of the length including the mandible and the entire tooth, the length of the tooth head and the gum, and the length of the eroded gum, AI osteoporosis reading method. 청구항 8에 있어서,9. The method of claim 8, 상기 데이터베이스는,The database is 각 사용자에 대한 투시 이미지가 저장된 사용자별 데이터부; 및a data unit for each user in which a perspective image for each user is stored; and 복수의 골다공증 환자의 투시 이미지가 저장되는 학습 데이터부;를 포함하고,Includes; learning data unit in which a plurality of osteoporosis patients fluoroscopic images are stored; 상기 데이터베이스에 측정부에서 측정된 의심 환자의 투시 이미지가 저장되는 단계는,The step of storing the fluoroscopic image of the suspected patient measured by the measurement unit in the database, 상기 사용자별 데이터부에 상기 측정부에서 측정되는 각 사용자에 따른 투시 이미지가 사용자별로 저장되는 단계를 더 포함하는, AI 골다공증 판독 방법.Further comprising the step of storing the fluoroscopic image for each user measured by the measurement unit in the user-specific data unit for each user, AI osteoporosis reading method. 청구항 11에 있어서,12. The method of claim 11, 상기 데이터베이스에 측정부에서 측정된 의심 환자의 투시 이미지가 저장되는 단계 이후에,After the step of storing the fluoroscopic image of the suspected patient measured by the measurement unit in the database, 상기 판단부는, 상기 측정부로부터 수신받은 특정 사용자에 대한 투시 이미지를 상기 사용자별 데이터부에 기 저장된 특정 사용자에 대한 투시 이미지와 비교하여, 골밀도의 상태 변화를 확인하는 단계를 더 포함하는, AI 골다공증 판독 방법.The determination unit, AI osteoporosis, further comprising the step of confirming the state change of bone density by comparing the fluoroscopic image for the specific user received from the measurement unit with the fluoroscopic image for the specific user pre-stored in the data unit for each user How to read.
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