WO2022158490A1 - 予測システム、制御方法、および制御プログラム - Google Patents

予測システム、制御方法、および制御プログラム Download PDF

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Publication number
WO2022158490A1
WO2022158490A1 PCT/JP2022/001798 JP2022001798W WO2022158490A1 WO 2022158490 A1 WO2022158490 A1 WO 2022158490A1 JP 2022001798 W JP2022001798 W JP 2022001798W WO 2022158490 A1 WO2022158490 A1 WO 2022158490A1
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Prior art keywords
prediction
information
image
subject
intervention
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English (en)
French (fr)
Japanese (ja)
Inventor
健一 渡辺
政之 京本
昌彦 橋田
信太郎 本多
直哉 和田
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Kyocera Corp
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Kyocera Corp
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Priority to AU2022209591A priority Critical patent/AU2022209591A1/en
Priority to CN202280010250.6A priority patent/CN116724360A/zh
Priority to EP22742612.9A priority patent/EP4283631A4/en
Priority to JP2022576720A priority patent/JP7747668B2/ja
Priority to US18/273,192 priority patent/US20240119587A1/en
Publication of WO2022158490A1 publication Critical patent/WO2022158490A1/ja
Anticipated expiration legal-status Critical
Priority to JP2025155211A priority patent/JP2025181893A/ja
Ceased legal-status Critical Current

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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
    • 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—Two-dimensional [2D] image generation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Definitions

  • the present disclosure relates to a prediction system, control method, and control program for predicting the state of a target part of the human body.
  • Patent Document 1 As described in Patent Document 1, it has been devised to support the diagnosis of osteoporosis using a neural network.
  • a prediction system includes (a) a subject image showing a target part of a subject at a first time point, and (b) the a prediction information acquiring unit that acquires first prediction information about a target part; and generates and outputs a prediction image that predicts the state of the target part at the second point in time from the first prediction information and the subject image. and a predicted image generator.
  • a control method is a control method of a prediction system, comprising: (a) a subject image showing a target part of a subject at a first time point; and (b) the first time point a prediction information acquiring step of acquiring first prediction information about the target part at a second time point after a predetermined period has elapsed since the state of the target part at the second time point from the first prediction information and the subject image and a predicted image generation step of generating and outputting a predicted image predicted by the prediction system, a predicted image generation model capable of generating the predicted image using the subject image and the first prediction information have.
  • the prediction system according to each aspect of the present disclosure may be realized by a computer.
  • the prediction system is operated by the computer by operating the computer as each part (software element) provided in the prediction system.
  • a control program for a prediction system to be realized and a computer-readable recording medium recording it are also included in the scope of the present disclosure.
  • the prediction system When the prediction system is realized by a plurality of computers, the prediction system may be realized by a computer by operating each computer as each part (software element) provided in each of the plurality of computers that make up the prediction system. good.
  • FIG. 1 is a block diagram illustrating a configuration example of a prediction system according to one aspect of the present disclosure
  • FIG. FIG. 11 is a block diagram showing a configuration example of a prediction system according to another aspect of the present disclosure
  • 1 is a block diagram illustrating an example configuration of a prediction system according to one aspect of the present disclosure
  • FIG. It is a figure which shows an example of a structure of the neural network which a prediction image production
  • 4 is a flow chart showing an example of the flow of processing performed by the prediction system according to the first embodiment
  • FIG. 4 is a block diagram showing an example of a configuration of a prediction system according to another aspect of the present disclosure
  • FIG. 9 is a flow chart showing an example of the flow of processing performed by the prediction system according to the second embodiment
  • FIG. 4 is a block diagram showing an example of a configuration of a prediction system according to another aspect of the present disclosure
  • FIG. 4 is a flow chart showing an example of the flow of learning processing of a neural network of a prediction information generation unit
  • 9 is a flow chart showing another example of the flow of processing performed by the prediction system according to the second embodiment
  • FIG. 4 is a block diagram showing an example of a configuration of a prediction system according to another aspect of the present disclosure
  • FIG. 4 is a flow chart showing an example of the flow of learning processing of a neural network of an intervention effect prediction unit; 14 is a flow chart showing an example of the flow of processing performed by the prediction system according to the third embodiment;
  • a prediction system is a system that generates and outputs a prediction image that predicts a state change of a target part of a subject's body.
  • the target part may be any part of the subject's body, for example, the whole body, head, eyes, oral cavity, neck, arms, hands, torso, waist, buttocks, legs. It may be either the part, the foot part, or the like.
  • the predicted image may be an image obtained by predicting a state change of any one of skin, hair, eyeballs, teeth, gums, muscles, fat, bones, cartilage, joints, intervertebral discs, etc. of the target site.
  • the predicted image may be an image that predicts the changes that will occur in the target part of the subject affected by the disease.
  • the predicted image is the shape of the target part of the subject (e.g., waist circumference, chest circumference, height, swelling, atrophy, joint angle and curvature, etc.) and appearance (e.g., posture, wrinkles, spots, redness, turbidity, darkening, yellowing etc.).
  • the subject may have a disease in his/her target area.
  • the predicted image may be an image obtained by predicting a change in symptoms at the target site of the subject due to the influence of the disease.
  • a prediction system that generates and outputs a prediction image that predicts a change in symptoms at a target site of a subject will be described as an example.
  • the predicted image is an image showing the effect of the subject's disease on the target site.
  • an image showing the effect on the target site is any image that shows changes in the shape of the target site affected by the disease, qualitative or quantitative changes that occur in the tissue of the target site due to the disease, etc. may be an image of
  • the disease may comprise at least one of obesity, alopecia, cataracts, periodontal disease, rheumatoid arthritis, Heberden's nodes, hallux valgus, osteoarthritis, spondylosis osteoarthritis, compression fractures and sarcopenia.
  • Diseases may include (i) syndromes such as metabolic syndrome, locomotive syndrome, etc., which exhibit a coherent pathological condition formed by a group of various symptoms, and (ii) physical changes such as aging and tooth alignment.
  • the prediction system generates a predicted image that predicts the symptoms at the target site at the second time point after a predetermined period from the first time point, based on the subject image showing the subject's target site at the first time point.
  • subject image may mean image data representing the subject image.
  • the first point in time may be, for example, the point in time at which the subject's image of the subject's target region is acquired.
  • the first point in time may typically be the point in time at which a subject image of the subject's current target site condition is acquired. That is, the first point in time may substantially mean the present point in time.
  • the predetermined period may be any period that has passed since the first time point, and may be half a year, one year, five years, or ten years. It may be 50 years. That is, the second point in time may be intended to be substantially any point in time in the future.
  • the predetermined period is not limited to one period, and may include multiple periods. That is, the predicted image is an image generated by predicting the symptoms of the target part of the subject at a plurality of time points such as half a year, one year, five years, ten years, and fifty years after the first time point. may contain.
  • a subject image is an image showing the target part of the subject at the first time point.
  • the subject image may be an external image of any one of the subject's whole body, head, upper body, lower body, upper limbs, and lower limbs.
  • the subject image may be a medical image of a target portion of the subject for examination of the subject. Medical images include at least one of X-ray images of the subject, CT (Computed Tomography) images, MRI (Magnetic Resonance Imaging) images, PET (Positron Emission Tomography) images and ultrasound images good.
  • the subject image includes the shape (e.g., waist circumference, chest circumference, height, swelling, atrophy, joint angle and curvature, etc.) and appearance (e.g., posture, wrinkles, blotches, redness, turbidity, etc.) of the subject's target part. , darkening, yellowing, etc.).
  • shape e.g., waist circumference, chest circumference, height, swelling, atrophy, joint angle and curvature, etc.
  • appearance e.g., posture, wrinkles, blotches, redness, turbidity, etc.
  • the prediction system uses the first prediction information about the target part at the second time point in addition to the subject image to generate the prediction image.
  • the first prediction information may be information about the symptoms of the target part of the subject at the second time point.
  • the first prediction information includes information indicating the symptoms of the target site of the subject at a plurality of time points such as half a year, one year, five years, ten years, and fifty years after the first time point.
  • the first prediction information is, for example, information that includes predictions about symptoms that are likely to occur in the target region of the subject, when the symptoms will occur, and the degree of progress of the symptoms in the target region of the subject.
  • the first prediction information may be information about at least one of the shape and appearance of the target part of the subject at the second time point.
  • the shape of the subject's target part e.g., waist circumference, chest circumference, height, etc.
  • it may be information indicating at least one of appearance (eg, posture, wrinkles, blemishes, etc.).
  • the first prediction information may be information related to at least one of the shape and appearance of the target site, which is related to the disease of the target site.
  • the first prediction information may include the following information as information with a high possibility that the target part of the subject will change.
  • the first prediction information is, for example, (i) information related to obesity, such as body weight, body mass index (BMI), abdominal circumference, visceral fat amount, blood pressure, blood sugar level, lipid, uric acid level, liver function value, etc.
  • (ii) as information related to alopecia it may contain information related to the number of hairs, sex hormone values, Norwood classification, Ludwig classification, etc.; (iii) as information related to cataract , visual acuity, field of vision, degree of turbidity, Emery-Little classification, etc., and (iv) information related to periodontal disease, such as degree of pain / swelling, number of remaining teeth, gingivitis (v) information related to rheumatoid arthritis, such as pain level, swelling level, joint angle, joint range of motion, Larsen classification, Stein blocker classification, etc.
  • information related to Hebaden's node may contain information related to the degree of pain, degree of swelling, joint range of motion, etc.
  • information related to hallux valgus may include information on the degree of pain, degree of swelling, joint range of motion, HV angle, M1-M2 angle, etc.
  • information related to osteoarthritis such as the degree of pain and swelling degree, joint angle, joint range of motion, degree of stiffness, thickness of joint cartilage, Kellgren-Laurence (KL) classification, presence or absence of lameness, etc.
  • (ix) degenerative spondylosis In the case, it may include information on the degree of pain, the degree of curvature of the spine, the range of motion of the spine, the KL classification, etc.
  • Information related to sarcopenia may include information related to muscle mass, walking speed, grip strength, and the like.
  • the prediction system is based on the subject image showing the target part of the subject at the first time point and the first prediction information regarding the target part at the second time point after the predetermined period has elapsed from the first time point. Then, a prediction image that predicts the state of the target part at the second time is generated and output.
  • the prediction system can output the state of the target part at the second time point as a visually easy-to-understand predicted image.
  • the predicted image is generated from the target person image, which is the image of the target person, the predicted image is a realistic image that is persuasive to the target person. Therefore, for example, if a doctor in charge of the subject presents it to the subject, the subject can recognize the state of the target site at the second time point, and the subject can easily understand the necessity of intervention. can be done.
  • the predicted image may be an image simulating an exterior image of any one of the subject's whole body, head, upper body, lower body, upper limbs, and lower limbs.
  • the predicted image may be an image simulating a medical image of the subject's target region obtained in the process of examining the subject.
  • the predicted image is the shape (e.g., waist circumference, chest circumference, height, swelling, atrophy, joint angle and curvature, etc.) and appearance (e.g., posture, wrinkles, spots, redness, turbidity, It may be an image showing at least one of blackening, yellowing, etc.).
  • the subject image is an appearance image showing the current appearance of the subject's skin (for example, wrinkles, spots, redness, turbidity, darkening, yellowing, etc.), and the first prediction information is the subject's future skin If the information is about the degree of wrinkles, the degree of blemishes, the degree of redness, the degree of turbidity, the degree of darkening or the degree of yellowing, based on the subject image and the first prediction information, the prediction system predicts the subject's An image showing the future skin appearance can be output as a predicted image.
  • the prediction system may generate the following two images: Output as predicted images: (1) A medical image indicating the current joint angles of the subject and a medical image indicating the future joint angles of the subject based on the first prediction information are output as predicted images. (2) An appearance image showing the current joint appearance of the subject and an image showing the future joint appearance of the subject based on the first prediction information can be output as predicted images.
  • FIG. 1 shows the configuration of a prediction system 100 including a prediction device 1 that acquires a target person image and first prediction information, and generates and outputs a prediction image from the target person image based on the first prediction information.
  • the prediction device 1 of the prediction system 100 can function alone as the prediction system described above.
  • FIG. 1 is a block diagram showing a configuration example of a prediction system 100 in a medical facility 5 into which a prediction device 1 has been introduced.
  • the prediction system 100 includes a prediction device 1 and one or more terminal devices 2 communicatively connected to the prediction device 1.
  • the prediction system 100 may include the prediction device 1 and a device (for example, the terminal device 2) capable of presenting the prediction image output from the prediction device 1.
  • FIG. 1 the prediction device 1 and a device (for example, the terminal device 2) capable of presenting the prediction image output from the prediction device 1.
  • the prediction device 1 is a computer that acquires a target person image and first prediction information, generates a predicted image from the target person image based on the first prediction information, outputs the predicted image, and transmits the predicted image to the terminal device 2.
  • the prediction device 1 may be connected to the LAN of the medical facility 5 as shown in FIG. The configuration of the prediction device 1 will be described later.
  • the terminal device 2 receives the predicted image from the prediction device 1 and presents the predicted image.
  • the terminal device 2 may be a computer or the like used by medical personnel such as doctors belonging to the medical facility 5 .
  • the terminal device 2 may be connected to the LAN of the medical facility 5 as shown in FIG.
  • the terminal device 2 may be, for example, a personal computer, a tablet terminal, a smart phone, or the like.
  • the terminal device 2 has a communication section for transmitting and receiving data with other devices, an input section such as a keyboard and a microphone, a display section capable of displaying a predicted image, and the like.
  • the prediction device 1 and the terminal device 2 are provided separately, but the prediction device 1 and the terminal device 2 may be integrated.
  • the prediction device 1 may have the functions of the terminal device 2 by having a display unit capable of displaying a prediction image.
  • the prediction system 100 may further include a first prediction information management device 3, a subject image management device 4, and an electronic medical record management device 9.
  • the first prediction information management device 3 is a computer that functions as a server for managing first prediction information.
  • the first predictive information management device 3 may be connected to the LAN of the medical facility 5, as shown in FIG. In this case, the prediction device 1 may acquire the first prediction information of the subject from the first prediction information management device 3 .
  • the subject image management device 4 is a computer that functions as a server for managing subject images.
  • the subject image management device 4 may capture an image of a subject who has undergone a medical examination regarding the state of the target site at the medical facility 5 .
  • the subject image may be a medical image captured within the medical facility 5 .
  • the subject image management device 4 may be communicably connected to an imaging device such as an X-ray imaging device in the medical facility 5, for example.
  • the image captured by the image capturing device may be recorded in the subject image management device 4 via, for example, a LAN.
  • the subject image management device 4 may be connected to the LAN of the medical facility 5 as shown in FIG.
  • the prediction device 1 may acquire the target person image from the target person image management device 4 .
  • the electronic medical record management device 9 is a computer that functions as a server for managing electronic medical record information of subjects who have been examined at the medical facility 5 .
  • the electronic medical record management device 9 may be connected to the LAN of the medical facility 5 as shown in FIG.
  • the prediction device 1 may acquire basic information related to the subject from the electronic medical record management device 9 .
  • the basic information is information included in the electronic medical record information, and is at least one of the subject's sex, age, height, weight, and information indicating the state of the subject's target part at the first point in time. may contain
  • a LAN local area network
  • a prediction device 1 is arranged in a medical facility 5, a prediction device 1, a terminal device 2, a first prediction information management device 3, a subject image management device 4, and an electronic medical record management device.
  • the network within the medical facility 5 may employ the Internet, a telephone communication network, an optical fiber communication network, a cable communication network, a satellite communication network, or the like.
  • the LAN within the medical facility 5 may be communicably connected to an external communication network.
  • the terminal device 2 may be a computer or the like used by the patient.
  • the prediction device 1 and at least one of the terminal device 2, the first prediction information management device 3, the subject image management device 4, and the electronic medical record management device 9 are directly connected without via a LAN.
  • the number of terminal devices 2, first prediction information management device 3, subject image management device 4, and electronic medical record management device 9 that can communicate with prediction device 1 may be plural.
  • multiple prediction devices 1 may be introduced.
  • the prediction device 1 may be communicably connected to a LAN installed in each of a plurality of medical facilities 5 via a communication network 6 instead of a computer installed in a predetermined medical facility 5 .
  • FIG. 2 is a block diagram showing a configuration example of a prediction system 100a according to another aspect of the present disclosure.
  • the medical facility 5a includes a terminal device 2a, a subject image management device 4a, and an electronic medical record management device 9a, which are communicably connected.
  • the medical facility 5b includes a terminal device 2b, a subject image management device 4b, and an electronic medical record management device 9b, which are communicably connected.
  • terminal device 2 terminal device 2
  • medical facility 5" medical facility 5"
  • FIG. 2 shows an example in which the LANs of the medical facility 5a and the medical facility 5b are connected to the communication network 6.
  • the prediction device 1 is not limited to the configuration shown in FIG.
  • the prediction device 1 and the first prediction information management device 3 may be installed in the medical facility 5a or the medical facility 5b.
  • the prediction system 100a adopting such a configuration may have a first prediction information management device 3a installed in the medical facility 5a and a first prediction information management device 3b installed in the medical facility 5b.
  • the prediction device 1 can acquire the first prediction information and the subject image of the subject Pa from the first prediction information management device 3a and the subject image management device 4a of the medical facility 5a, respectively.
  • the prediction device 1 can transmit a prediction image that predicts the state of the target part of the subject Pa to the terminal device 2a installed in the medical facility 5a.
  • the prediction device 1 can acquire the first prediction information and the subject image of the subject Pb from the first prediction information management device 3b and the subject image management device 4b of the medical facility 5b, respectively.
  • the prediction device 1 can transmit a prediction image that predicts the state of the target part of the subject Pb to the terminal device 2b installed in the medical facility 5b.
  • the first prediction information of each subject and the subject image include identification information unique to each medical facility 5 given to each medical facility 5 that examines each subject, and It suffices if the identification information unique to each given subject is included.
  • the identification information unique to each medical facility 5 may be, for example, a facility ID. Further, the identification information unique to each subject may be, for example, a patient ID. Based on these pieces of identification information, the prediction device 1 can correctly transmit a predicted image that predicts the state of the target part of the subject to the terminal device 2 of each medical facility 5 where the subject has been examined. .
  • FIG. 3 is a block diagram illustrating an example configuration of a prediction system 100, 100a according to one aspect of the present disclosure.
  • members having the same functions as the members already explained are denoted by the same reference numerals, and the explanation thereof will not be repeated.
  • the prediction systems 100 and 100a shown in FIG. 3 include a prediction device 1, one or more terminal devices 2 communicably connected to the prediction device 1, a first prediction information management device 3, and a subject image management device 4, It has
  • the prediction device 1 includes a control unit 7 that controls each unit of the prediction device 1 and a storage unit 8 that stores various data used by the control unit 7 .
  • the control unit 7 includes a prediction information acquisition unit 71 , a prediction image generation unit 72 and an output control unit 73 .
  • the storage unit 8 stores a control program 81 that is a program for performing various controls of the prediction device 1 .
  • the prediction information acquisition unit 71 acquires the target person image from the target person image management device 4 and acquires the first prediction information from the first prediction information management device 3 .
  • the target person image and the first prediction information are input data input to the prediction image generator 72 .
  • the subject image and the first prediction information will be explained using several diseases as examples.
  • the subject image may be an image showing the current subject's whole body or abdomen
  • the first prediction information is the subject's weight, BMI, waist circumference, and visceral fat amount.
  • blood pressure blood sugar level, lipid, uric acid level or liver function value.
  • the subject image is an image showing the current subject's whole body or head
  • the first prediction information is the subject's hair count, sex hormone value, Norwood classification, or Ludwig It may be information about classification.
  • the subject image is an image showing the subject's current head (face) or eyes
  • the first prediction information is the subject's visual acuity, visual field, and eye lens. It may be information about the degree of turbidity or the Emery-Little classification.
  • the subject image is an image showing the subject's current head (face) or oral cavity
  • the first prediction information is the pain of the subject's teeth or gums. It may be information about extent, extent of tooth or gum swelling, number of remaining teeth, gingivitis index or periodontal pocket depth. If the disease is periodontal disease, the subject image may be an image showing an open mouth or an image showing a closed mouth.
  • the subject image is an image showing the subject's current whole body, upper limbs, or lower limbs
  • the first prediction information is the degree of pain in the subject's whole body, upper limbs, or lower limbs.
  • degree of swelling, joint angle, joint range of motion Larsen classification or Stein blocker classification.
  • the subject image is an image showing the subject's current hand
  • the first predictive information is the degree of pain, degree of swelling, or degree of joint of the subject's hand.
  • Information about the movable range may be used.
  • the subject image is an image showing the current foot of the subject
  • the first prediction information is the degree of pain, the degree of swelling, and the degree of joint mobility of the subject's foot. It may be information about range, HV angle or M1-M2 angle.
  • the subject image is an image showing the subject's current whole body, upper limbs, or lower limbs
  • the first predictive information is the pain of the subject's whole body, upper limbs, or lower limbs. degree of swelling, joint angle, joint range of motion or KL classification.
  • the subject image is an image showing the subject's current whole body, neck, chest, or waist
  • the first prediction information is the degree of curvature of the subject's spine
  • the first prediction information is the degree of curvature of the subject's spine
  • the first prediction information is the degree of curvature of the subject's spine, the range of motion of the spine, or K It may be information about the -L classification.
  • the subject image may be an image showing the subject's current whole body, upper limbs, or lower limbs
  • the first prediction information may be information about the subject's muscle mass.
  • the subject image may be a medical image taken during diagnosis of each disease.
  • the disease is knee osteoarthritis
  • the subject image is an X-ray image showing the current subject's knee joint
  • the first prediction information is the subject's tibia and femur two years later. It may be information about an angle formed by and.
  • the predicted image generation unit 72 Based on the first prediction information, the predicted image generation unit 72 generates and outputs a predicted image that predicts the state of the target part at the second point in time from the subject image.
  • the predicted image generation unit 72 may generate an image simulating at least part of the subject image used to generate the predicted image.
  • the predicted image generated by the predicted image generation unit 72 may be an image showing the effect of a disease occurring in a target region on the target region.
  • the generated predicted images may include images associated with parts of the subject that have not changed from the first time point at the second time point. That is, the predicted image may include an image associated with a part that changed from the first time point to the second time point and an image associated with a part that did not change from the first time point to the second time point.
  • the predicted image generation unit 72 may have any known image editing function and video editing function.
  • the predicted image generator 72 converts the subject image into an editable file format, and then modifies the subject image based on the first prediction information to generate the predicted image.
  • the first prediction information is information about the angle formed by the subject's tibia and femur two years later
  • the predicted image generation unit 72 converts the subject image into a predetermined file format.
  • the predicted image generator 72 may generate a predicted image by changing the angle formed by the tibia and the femur appearing in the subject image after converting the file format based on the first prediction information.
  • the predicted image generation unit 72 may have a predicted image generation model that can generate a predicted image using the subject image and the first prediction information.
  • the predictive image generation model may be a neural network trained using a plurality of image data showing target parts as training data.
  • a convolutional neural network (CNN), a generative adversarial network (GAN), an autoencoder, or the like may be applied as a predictive image generation model.
  • the predicted image generation unit 72 inputs the subject image and the first prediction information to the predicted image generation model and outputs the predicted image.
  • the predicted image generation unit 72 outputs the predicted image output from the predicted image generation model (that is, generated by the predicted image generation unit 72).
  • a predicted image generation model is a calculation model used when the predicted image generation unit 72 performs calculations based on input data.
  • a predicted image generation model is generated by executing machine learning, which will be described later, on the neural network of the predicted image generation unit 72 .
  • the output control unit 73 transmits the predicted image output from the predicted image generation unit 72 to the terminal device 2 .
  • the output control unit 73 may transmit at least one of the target person image and the first prediction information used to generate the predicted image to the terminal device 2 together with the predicted image.
  • the prediction device 1 may be configured to include a display unit (not shown).
  • the output control section 73 may cause the display section to display the predicted image.
  • the output control unit 73 may cause the display unit to display at least one of the target person image and the first prediction information used to generate the predicted image together with the predicted image.
  • the prediction system 100, 100a By providing the predicted image generation unit 72 having the predicted image generation model, the prediction system 100, 100a generates and outputs a realistic predicted image in which the state of the target part at the second point in time is reflected in the image of the subject. be able to. As a result, the prediction systems 100 and 100a can allow the subject to clearly recognize the state of the target part at the second time point.
  • a trained prediction image generation model may be installed in the prediction device 1 in advance.
  • the prediction device 1 may further include a first learning section 74 that performs learning processing for the predicted image generation section 72 .
  • the first learning section 74 controls learning processing for the neural network of the predicted image generating section 72 .
  • FIG. 4 is a diagram showing an example of the configuration of a neural network included in the predicted image generation unit 72. As shown in FIG.
  • the predictive image generation model applying the adversarial generation network has two networks: a generator network (hereinafter referred to as generator 721) and a discriminator network (hereinafter referred to as discriminator 722). have.
  • the generator 721 can generate an image that looks like a real image as a predicted image from the first predicted information and the subject image.
  • the discriminator 722 can discriminate between the image data (fake image) from the generator 721 and the real image from the first training data set 82, which will be described later.
  • the first learning unit 74 acquires the subject image and the first prediction information from the storage unit 8 and inputs them to the generator 721 .
  • a generator 721 generates a predicted image candidate (fake image) from the subject image and the first prediction information.
  • the generator 721 may refer to real images included in the first training data set 82 to generate predicted image candidates.
  • the first learning data set 82 is data used for machine learning to generate a predictive image generation model.
  • the first training data set 82 may contain any real image that the generator 721 aims to reproduce as faithfully as possible.
  • the first training data set 82 may contain real medical images captured in the past.
  • the medical image includes, for example, at least one of X-ray image data, CT image data, MRI image data, PET image data, and ultrasound image data obtained by imaging target regions of each of a plurality of patients. You can
  • the first learning data set 82 may include first learning data and first teacher data.
  • the first learning data is, for example, data of the same type as the subject image and data of the same type as the first prediction information.
  • Data of the same type as the subject's image is image data of the same target part as the target part shown in the subject's image from the same angle, and image data of the same type, such as medical images or external images.
  • Data of the same kind as the first predictive information means that, when the first predictive information is information related to the shape and appearance of the target part related to the disease, it is related to the shape and appearance of the same target part related to the same disease. Information to be intended.
  • the first training data is data of the same type as the predicted image, and is data of the same person whose time has passed since the first learning data.
  • the first teacher data is data related to "data of the same kind as the first prediction information", which is the first learning data.
  • Data of the same type as the predicted image means image data of the same target region as the target region shown in the predicted image from the same angle, and image data of the same type, such as medical images or external images. do.
  • the first learning unit 74 inputs the predicted image candidate generated by the generator 721 and the real image included in the first training data set 82 to the classifier 722 .
  • the discriminator 722 takes as inputs the real images from the first training data set 82 and the predicted image candidates generated by the generator 721 and outputs for each image the probability that it is the real image. .
  • the first learning unit 74 calculates a classification error that indicates how accurate the probability output by the discriminator 722 is.
  • the first learning unit 74 iteratively improves the discriminator 722 and the generator 721 using the error backpropagation method.
  • the weights and biases of classifier 722 are updated to minimize classification error (ie, to maximize classification performance).
  • the generator 721 weights and biases are updated to maximize the classification error (ie, maximize the probability that the classifier 722 mistakes a predicted image candidate for the real image).
  • the first learning unit 74 updates the weight and bias of the classifier 722 and the weight and bias of the generator 721 until the probability output by the classifier 722 satisfies a predetermined criterion.
  • the predicted image generator 72 can generate a predicted image that is indistinguishable from the real thing.
  • FIG. 5 is a flowchart showing an example of the flow of processing performed by the prediction systems 100 and 100a according to this embodiment.
  • step S1 the prediction information acquisition unit 71 acquires a subject image and first prediction information (input data) (prediction information acquisition step).
  • the predicted image generation unit 72 generates a predicted image and outputs the predicted image in step S2 (predicted image generation step).
  • the prediction systems 100 and 100a include the prediction device 1 that acquires the first prediction information from the first prediction information management device 3, but are not limited to this.
  • the configuration may be such that the prediction device 1A generates the first prediction information. Configurations of prediction systems 100 and 100a including such a prediction device 1A will be described with reference to FIG.
  • FIG. 6 is a block diagram illustrating an example configuration of a prediction system 100, 100a according to another aspect of the present disclosure.
  • the prediction device 1A includes a control unit 7A that controls each unit of the prediction device 1A in an integrated manner, and a storage unit 8 that stores various data used by the control unit 7A.
  • the control unit 7A further includes a prediction information generation unit 75 in addition to the prediction information acquisition unit 71, the prediction image generation unit 72, and the output control unit 73. FIG.
  • FIG. 6 shows an example in which the prediction device 1A includes the first learning unit 74, it is not limited to this.
  • a learned predictive image generation model may be pre-installed in the prediction device 1A.
  • the prediction information generation unit 75 generates first prediction information about the target part at the second time point after a predetermined period from the first time point from the subject image showing the target part of the subject at the first time point, The first prediction information is output to the prediction information acquisition unit 71 .
  • the prediction information generation unit 75 may have a prediction information generation model that can estimate the first prediction information from the subject image.
  • the predictive information generation model is a model capable of estimating the first predictive information from the subject's subject image and the subject's basic information.
  • the predictive information generation model may be a neural network trained using patient information regarding a patient having a disease of the target site as training data.
  • a convolutional neural network CNN: convolutional neural network
  • RNN recurrent neural network
  • LSTM Long Short-Term Memory
  • the patient information includes, for example, state information indicating the state of the target part of each patient acquired at a plurality of times in the past, and indicates the state information for each patient and the time when the state information was acquired.
  • Information is information with which information is associated.
  • the prediction information generation unit 75 inputs data related to the subject image to the prediction information generation model and outputs the first prediction information.
  • the prediction information generation unit 75 outputs the first prediction information output from the prediction information generation model (that is, generated by the prediction information generation unit 75).
  • a prediction information generation model is a calculation model used when the prediction information generation unit 75 performs calculations based on input data.
  • a prediction information generation model is generated by executing machine learning, which will be described later, on the neural network of the prediction information generation unit 75 .
  • FIG. 7 is a diagram showing an example of the configuration of a neural network included in the prediction information generation unit.
  • the prediction information generation unit 75 includes an input layer 751 and an output layer 752.
  • the prediction information generation unit 75 performs calculations based on the prediction information generation model on input data input to the input layer 751 and outputs prediction information from the output layer 752 .
  • the prediction information generation unit 75 in FIG. 7 includes a neural network up to an input layer 751 and an output layer 752.
  • the neural network may be any neural network suitable for handling time-series information. For example, LSTM or the like may be used.
  • the neural network may be a neural network suitable for combined handling of time-series information and location information. For example, a ConvLSTM network that combines CNN and LSTM may be used.
  • the input layer 751 is capable of extracting time-varying feature amounts of input data.
  • the output layer 752 can calculate a new feature amount based on the feature amount extracted by the input layer 751, the temporal change of the input data, and the initial value.
  • Input layer 751 and output layer 752 have multiple LSTM layers. Each of input layer 751 and output layer 752 may have three or more LSTM layers.
  • the input data input to the input layer 751 may be, for example, a parameter indicating a feature amount extracted from the subject's image showing the subject's target part at the first time point.
  • the prediction information generating section 75 can output the first prediction information regarding the target part at the second point in time when the predetermined period has elapsed from the first point in time.
  • the prediction information generation unit 75 outputs, as the first prediction information, for example, a prediction result of the degree of onset or progression of the disease related to the target site of the subject at the second time point after a predetermined period has elapsed from the first time point. Specifically, the prediction information generation unit 75 generates, as the first prediction information, for example, the degree of symptoms of each disease at the second time point of the subject, the classification of each disease, and the target site requiring invasive treatment. Output information such as the time.
  • the first prediction information shown here is an example, and is not limited to these.
  • the prediction information generation unit 75 may output information indicating the subject's QOL as the third prediction information based on the above-described first prediction information. Specifically, the prediction information generation unit 75 generates, as the third prediction information, information about the pain occurring in the target site of the subject, information about the subject's catastrophic thinking, information about the exercise ability of the subject, At least one of information indicating the subject's degree of life satisfaction and information such as the degree of stiffness of the target site of the subject is output.
  • the information indicating the subject's QOL is information including at least one of the following. ⁇ Information about the subject's catastrophic thinking ⁇ Information about the subject's exercise ability ⁇ Information indicating the subject's life satisfaction level.
  • the information indicating the subject's QOL includes the subject's (1) physical function, (2) daily role function (body), (3) body pain, (4) overall health, (5) vitality, ( It may include information on 6) social functioning, (7) daily role functioning (mental), and (8) mental health.
  • SF-36 36-Item. Short-Form Health Survey
  • VAS Visual analog scale
  • NEI VFQ-25 The 25-item National Eye Institute Visual Function Questionnaire
  • GOHAI General Oral Health Assessment Index
  • WOMAC Western Ontario and McMaster Universities Osteoarthritis Index
  • RDQ Raland-Morris Disability Questionnaire
  • the prediction information generation unit 75 should generate at least part of the first prediction information used by the prediction image generation unit 72 to generate the prediction image. In that case, the remaining first prediction information may be obtained from the first prediction information management device 3 by the prediction information acquisition unit 71 .
  • FIG. 8 is a flow chart showing an example of the flow of processing performed by the prediction systems 100 and 100a of the second embodiment.
  • step S11 the prediction information generation unit 75 acquires the subject image (input data) (image acquisition step).
  • the prediction information generation unit 75 generates first prediction information in step S12 and outputs the first prediction information to the prediction information acquisition unit 71 (first prediction step).
  • the prediction information acquisition unit 71 obtains (a) the first prediction information acquired from the prediction information generation unit 75, and (b) whether the first prediction information was acquired from the subject image management device 4 before acquisition of the first prediction information. , or a target person image (input data) acquired simultaneously with or after acquisition of the first prediction information is input to the prediction image generation unit 72 (not shown).
  • the predicted image generation unit 72 generates a predicted image and outputs the predicted image in step S13 (predicted image generation step).
  • the prediction information generation unit 75 generates the first prediction information based on the subject image.
  • the prediction information generation unit 75B generates the first prediction information based on the basic information in addition to the subject image.
  • Configurations of prediction systems 100 and 100a including such a prediction device 1B will be described with reference to FIG.
  • FIG. 9 is a block diagram illustrating a variation of the configuration of prediction systems 100, 100a according to another aspect of the present disclosure.
  • the prediction device 1B further has a prediction information generation section 75B in the control section 7B.
  • the prediction device 1B includes a control unit 7B that controls each unit of the prediction device 1B, and a storage unit 8B that stores various data used by the control unit 7B.
  • the control unit 7B further includes a prediction information generation unit 75B and a basic information acquisition unit 76 in addition to the prediction information acquisition unit 71, the prediction image generation unit 72, and the output control unit 73.
  • the prediction device 1B obtains information about a symptom that can occur in the target site at the second time point or is closer to the symptom that has occurred, that is, more accurate first prediction information, from the image of the subject captured at the first time point. can be generated based on As a result, the prediction device 1B can generate an image showing a symptom that can occur in the target region at the second time point or that is closer to the symptom that has occurred, that is, a prediction image showing more accurate prediction information.
  • the basic information acquisition unit 76 acquires basic information, which is information related to the subject, from the electronic medical record management device 9 .
  • the electronic medical record management device 9 is a computer that functions as a server for managing electronic medical record information of subjects who have been examined at the medical facility 5 or medical facilities other than the medical facility 5 .
  • the electronic medical record information may include the subject's basic information and interview information.
  • the basic information is input data input to the prediction information generation unit 75B in addition to the subject image.
  • the basic information is information including at least one of the subject's sex, age, height, weight, and information indicating the state of the target part of the subject at the first time point.
  • the basic information includes at least one of the subject's BMI, race, occupational history, exercise history, disease history related to the target site, information regarding the shape and appearance of the target site, biomarker information, and genetic information. It may contain further.
  • the basic information may also include, for example, information such as the degree of disease symptoms related to the subject's target site.
  • the basic information may include, for example, information included in the subject's electronic medical record information.
  • the basic information may be interview information obtained from the subject by interview conducted at the medical facility 5 or the like, and may include, for example, information related to the subject's QOL at the first time point.
  • the prediction device 1B acquires the subject's basic information from the electronic medical record management device 9 in addition to the subject's image, and stores the target region of the subject Pa in the terminal device 2a installed in the medical facility 5a. It is possible to transmit a predicted image that predicts the state of The prediction device 1B acquires the subject's basic information from the electronic medical record management device 9 in addition to the subject's image, and predicts the state of the target part of the subject Pb to the terminal device 2b installed in the medical facility 5b. Images can be sent.
  • a learned prediction information generation model may be pre-installed in the prediction device 1B.
  • the prediction device 1B may further include a second learning section 77 that performs learning processing for the prediction information generating section 75B.
  • the second learning section 77 controls learning processing for the neural network of the prediction information generating section 75B.
  • a second learning data set 83 which will be described later, is used for this learning.
  • a specific example of learning performed by the second learning unit 77 will be described later.
  • FIG. 10 is a flow chart showing an example of the flow of learning processing of the neural network of the prediction information generator 75B.
  • the second learning unit 77 acquires the second learning data included in the second learning data set 83 from the storage unit 8B (step S21).
  • the second learning data includes patient images of multiple patients.
  • the second learning unit 77 determines a certain patient (step S22).
  • the second learning unit 77 inputs the patient image of a certain patient at time point A, which is included in the second learning data, to the input layer 751 (step S23).
  • the input layer 751 may extract parameters representing feature amounts from the input patient image.
  • the second learning unit 77 acquires output data related to the symptom of the target part of the patient from the output layer 752 (step S24).
  • This output data contains the same content as the second teacher data.
  • the second learning unit 77 acquires the second teacher data included in the second learning data set 83. Then, the second learning unit 77 compares the obtained output data with the state information indicating the state of the target part of the patient at time point B, which is included in the second teacher data, and calculates the error (step S25). .
  • the second learning unit 77 adjusts the prediction information generation model so that the error becomes small (step S26).
  • any known method can be applied to adjust the predictive information generation model.
  • an error backpropagation method may be employed as a method of adjusting the prediction information generation model.
  • the adjusted prediction information generation model becomes a new prediction information generation model, and the prediction information generation unit 75B uses the new prediction information generation model in subsequent calculations.
  • the parameters used in the prediction information generation section 75B can be adjusted.
  • the parameters include, for example, parameters used in the input layer 751 and the output layer 752. Specifically, the parameters include weighting factors used in the input layer 751 and the LSTM layers of the output layer 752 . The parameters may also include filter coefficients.
  • Second learning unit 77 determines if the error is not within a predetermined range and if patient images of all patients included in second learning data set 83 have not been input (NO in step S27). , the patient is changed (step S28), and the process returns to step S23 to repeat the learning process. Second learning unit 77 determines if the error is within a predetermined range and if patient images of all patients included in second learning data set 83 have been input (YES in step S27). , terminate the learning process.
  • the second learning data set 83 is data used for machine learning to generate a prediction information generation model.
  • the second training data set 83 may include patient information regarding patients with disease of the target site.
  • the patient information includes state information indicating the state of the target part of each patient acquired at a plurality of times in the past, and the state information for each patient and the time point at which the state information was acquired. may be information associated with information indicating
  • the second learning data set 83 includes second learning data used as input data and second teacher data for calculating the error between the first prediction information output by the prediction information generating section 75B.
  • the second learning data may include, for example, image data showing target regions of each of a plurality of patients.
  • the image data used as the second learning data may be image data obtained by imaging any one of the whole body, upper body, lower body, upper limbs, and lower limbs of each of a plurality of patients.
  • the image data used as the second learning data may be medical image data showing target regions of a plurality of patients.
  • the medical image data includes, for example, at least one of X-ray image data, CT image data, MRI image data, PET image data, and ultrasound image data obtained by imaging target regions of a plurality of subjects. You can
  • the second learning data is data of the same kind as the subject image.
  • the second teacher data is data of the same kind as the first prediction information.
  • the second teacher data may include state information indicating the state of the target region of each patient at the time the patient's image was captured, and symptom information regarding the target region.
  • the state information may include information regarding the progression of symptoms of the target site.
  • the symptom information may include information regarding the onset time of the disease of the target site.
  • the second learning data set 83 may be data in which the second learning data and the second teacher data are integrated. That is, the second learning data set 83 includes patient images obtained from each of a plurality of patients at a plurality of times in the past, and state information indicating the state of the target region at the time when the patient images were captured. It may be associated time-series data.
  • the second learning data set 83 may include parameters indicating feature amounts extracted from the following information at a certain point in time and one year after a certain point in time.
  • the second learning data set 83 may include body weight, BMI, waist circumference, visceral fat amount, blood pressure, blood sugar level, lipid, uric acid level, or liver function value.
  • the second learning data set 83 may include the number of hairs, sex hormone values, Norwood classification, Ludwig classification, and the like.
  • the second training data set 83 may include visual acuity, visual field, recommended degree of opacity of the eye, Emery-Little classification, or the like.
  • the second learning data set 83 includes the degree of tooth or gingival pain, the degree of tooth or gingival swelling, the number of remaining teeth, the gingivitis index, the periodontal pocket depth, and the like. May contain.
  • the second learning data set 83 includes the degree of pain, degree of swelling, joint angle, joint range of motion, Larsen classification, Stein blocker classification, etc. of the subject's whole body, upper limbs or lower limbs. may contain
  • the second learning data set 83 may include the degree of pain, the degree of swelling, the range of motion of the joint, and the like of the subject's hand.
  • the second learning data set 83 may include the degree of pain, the degree of swelling, the joint range of motion, the HV angle or the M1-M2 angle, etc. of the subject's foot.
  • the second learning data set 83 includes the degree of pain, degree of swelling, joint angle, range of joint motion, degree of stiffness, joint cartilage thickness, KL classification or presence or absence of lameness.
  • the second learning data set 83 may include the degree of pain, the degree of curvature of the spine, the range of motion of the spine, or the KL classification.
  • the second learning data set 83 may include the degree of pain or the range of motion of the spine.
  • the disease is sarcopenia, muscle mass, walking speed or grip strength may be included.
  • the second learning data set 83 may include parameters indicating attributes of the subject.
  • the subject's attributes are, for example, each subject's sex, age, height, and weight.
  • the second learning unit 77 uses the target person image at a certain time point as the second learning data, and uses the target person image and the target person image after a predetermined period from a certain time point as the second learning data.
  • Information about the symptoms of the target part and information about the subject at the time when the person image was captured may be used as the second teacher data.
  • the second learning data set 83 may include time-series data containing information about the QOL of each of a plurality of subjects. For example, information such as SF-36 and VAS may be included.
  • the prediction information generation unit 75B having a prediction information generation model generated by machine learning using such a second learning data set 83 obtains information related to the QOL of the subject at the second time point from the subject image of the subject. It is also possible to output information to
  • the input data used during learning by the prediction information generation unit 75B is (a) a subject image showing the subject's target part at a certain point A, which is included in the second learning data.
  • the prediction information generator 75B Based on the above-described input data, the prediction information generator 75B outputs first prediction information regarding the target site at time B after a predetermined period (for example, three years) has passed from time A as output data.
  • the prediction information generation unit 75B outputs data such as the angle around the target site of the subject at time B, the degree of increase and decrease of the target site, the degree of wrinkles and blemishes of the target site, the target site information indicating the timing and degree of onset of pain, and the timing at which invasive treatment is required for the target site.
  • the output data shown here is an example, and is not limited to these.
  • the second learning unit 77 when performing the learning of the prediction information generation unit 75B, obtains an image of the target person at a certain point A in which the target part of the target person is shown.
  • the general subject's symptom information and attribute information may be input to the prediction information generator 75B as second learning data.
  • FIG. 11 is a flow chart showing an example of the flow of processing performed by the prediction systems 100 and 100a according to this embodiment.
  • step S31 the prediction information generation unit 75B acquires a subject image (input data) and basic information (input data) (image and information acquisition step).
  • the prediction information generation unit 75B generates the first prediction information in step S32, and sends the first prediction information to the prediction information acquisition unit 71. output (first prediction step).
  • the prediction information acquisition unit 71 acquires (a) the first prediction information acquired from the prediction information generation unit 75B and (b) from the subject image management device 4 before acquiring the first prediction information. , or a target person image (input data) acquired simultaneously with or after acquisition of the first prediction information is input to the prediction image generation unit 72 (not shown).
  • the predicted image generation unit 72 generates a predicted image and outputs the predicted image in step S33 (predicted image generation step).
  • the prediction system 100, 100a has a function of outputting a prediction image that predicts the state of the target part at the second point in time when the target part is intervened, and the method of intervention for the subject and the effect of the intervention.
  • may be A prediction device 1C having such a function will be described with reference to FIG.
  • FIG. 12 is a block diagram showing an example configuration of a prediction system 100, 100a according to another aspect of the present disclosure.
  • intervention methods may include lifestyle guidance, diet therapy, drug therapy, exercise therapy, surgical therapy (liposuction, gastrectomy, gastric banding, etc.) and the like.
  • intervention methods may include lifestyle guidance, dietary therapy, drug therapy, surgical therapy (hair transplantation), wearing a wig, and the like.
  • intervention methods may include drug therapy, exercise therapy, surgical therapy (cataract extraction, intraocular lens implantation, etc.), and the like.
  • methods of intervention may include oral care instructions, pharmacotherapy, orthodontic therapy, surgical therapy (periodonoplasty, implant therapy, etc.), use of dentures, and the like.
  • methods of intervention may include drug therapy, surgical therapy (osteotomy, joint replacement).
  • methods of intervention may include drug therapy and the like.
  • intervention methods may include shoe instruction, exercise therapy, brace therapy, drug therapy, surgical therapy (osteotomy, fusion, joint replacement, etc.), and the like.
  • intervention methods include exercise therapy, brace therapy, drug therapy, rehabilitation, and surgical therapy (intra-articular injection, arthroscopic surgery, osteotomy, fusion surgery, joint replacement, etc.). ) and so on.
  • methods of intervention may include exercise therapy, brace therapy, drug therapy, surgical therapy (such as spinal instrumentation surgery), and the like.
  • methods of intervention may include brace therapy, drug therapy, surgical therapy (such as spinal instrumentation surgery), and the like.
  • intervention methods may include lifestyle guidance, diet therapy, drug therapy, exercise therapy, and the like.
  • the prediction device 1C includes a control unit 7C that controls each unit of the prediction device 1 and a storage unit 8C that stores various data used by the control unit 7C.
  • the control unit 7C includes a prediction information acquisition unit 71, a prediction image generation unit 72C, an output control unit 73C, a first learning unit 74, a prediction information generation unit 75B, a basic information acquisition unit 76, and a second learning unit 77.
  • An effect prediction unit 78 and a third learning unit 79 are provided.
  • FIG. 12 shows the prediction device 1C including the first learning unit 74, the second learning unit 77, and the third learning unit 79, it is not limited to this.
  • the prediction device 1C may include any (or all) of the first learning unit 74, the second learning unit 77, and the third learning unit 79, or may not include any (or all). good.
  • the prediction device 1C does not have to include the first learning unit 74.
  • a trained prediction image generation model may be installed in the prediction device 1C in advance.
  • the prediction device 1 ⁇ /b>C does not have to include the second learning section 77 .
  • a trained prediction information generation model may be installed in the prediction device 1C in advance.
  • the prediction device 1 ⁇ /b>C does not have to include the third learning section 79 .
  • a learned intervention effect prediction model (described later) may be installed in the prediction device 1C in advance.
  • a control program 81 which is a program for performing various controls of the prediction device 1C
  • a first learning data set 82 and a second learning data set 83
  • third teacher data 84 and Intervention information 85 may be stored.
  • FIG. 12 shows a prediction device 1C in which a control program 81, a first learning data set 82, a second learning data set 83, a third teacher data 84, and intervention information 85 are stored in a storage unit 8C. Yes, but not limited to. Any one (or all) of control program 81, first learning data set 82, second learning data set 83, third teacher data 84, and intervention information 85 is stored in storage unit 8C of prediction device 1C. may be stored, or any (or all) may not be stored.
  • ⁇ Predicted image generator 72C> The predicted image generation unit 72C inputs the subject image, the first prediction information, and the second prediction information to the predicted image generation model, and outputs the predicted image.
  • the predicted image generation unit 72C outputs the predicted image output from the predicted image generation model (that is, generated by the predicted image generation unit 72C).
  • the output controller 73C transmits the predicted image output from the predicted image generator 72C to the terminal device 2 . As shown in FIG. 12, the output control unit 73C outputs at least one of the target person image, the first prediction information, and the second prediction information used to generate the prediction image together with the prediction image to the terminal device 2. may be sent to
  • the intervention effect prediction unit 78 outputs second prediction information indicating the method of intervention for the subject and the effect of the intervention from the first prediction information regarding the target site at the second time point after the predetermined period has elapsed from the first time point. do.
  • the intervention effect prediction unit 78 may have an intervention effect prediction model capable of estimating the second prediction information from the first prediction information.
  • the intervention effect prediction model is a calculation model used when the intervention effect prediction unit 78 performs calculations based on input data.
  • Other configurations of the intervention effect prediction model are not particularly limited as long as it is a computation model that can estimate the second prediction information from the first prediction information.
  • the intervention effect prediction model may be a neural network, for example, a trained neural network with an input layer and an output layer. More specifically, the intervention effect prediction model may be a neural network trained using effect information as teacher data.
  • the effect information includes state information indicating the state of the target part of each patient acquired at a plurality of past points in time, and the state information for each patient and the intervention indicating the intervention applied to each patient Information 85 is associated information.
  • the efficacy information may include time-series data regarding each patient's condition information obtained at multiple points in the past from each of the multiple patients to whom the intervention has been applied in the past.
  • the intervention effect prediction unit 78 performs calculations based on the intervention effect prediction model in response to input of the first prediction information as input data to the input layer. , the second prediction information is output from the output layer as output data.
  • the second prediction information is, for example, information indicating the type of intervention and information indicating the effect of the intervention.
  • the effect of the intervention is information representing the symptoms of the target area of the subject at the second time point when the intervention is applied.
  • the effect of the intervention is the degree to which the application of the intervention improves symptoms or slows down the progression of the disease for the target site of the subject at the second time point compared to not applying the intervention.
  • Information indicating the degree may be used.
  • the second prediction information may include information indicating when intervention should be applied (intervention time).
  • the intervention effect prediction unit 78 may be configured to extract a feature amount from the first prediction information and use it as input data.
  • a known algorithm such as the following can be applied to extract the feature quantity.
  • CNN Convolutional neural network
  • RNN Auto encoder
  • LSTM Long Short-Term Memory
  • the configuration of the intervention effect prediction unit 78 will be further described below using FIG. 7, taking as an example a case where the intervention effect prediction unit 78 is a neural network as an intervention effect prediction model.
  • the configuration shown in FIG. 7 is an example, and the configuration of the intervention effect prediction unit 78 is not limited to this.
  • the intervention effect prediction unit 78 includes an input layer 781 and an output layer 782.
  • the intervention effect prediction unit 78 acquires the first prediction information from the prediction information generation unit 75B and uses it as input data to be input to the input layer 781 .
  • the intervention effect prediction unit 78 may further acquire a subject image and use it as input data.
  • the intervention effect prediction unit 78 may acquire basic information from the basic information acquisition unit 76 and use the basic information as input data to be input to the input layer 781 .
  • the intervention effect prediction unit 78 performs operations based on the intervention effect prediction model on the input data input to the input layer 781 and outputs a predicted image from the output layer 782 .
  • the intervention effect prediction unit 78 includes a neural network up to an input layer 781 and an output layer 782.
  • the neural network may be any neural network suitable for handling time-series information.
  • LSTM or the like may be used.
  • the neural network may be a neural network suitable for combined handling of time-series information and location information.
  • a ConvLSTM network that combines CNN and LSTM may be used.
  • the input layer 781 is capable of extracting time-varying feature amounts of input data.
  • the output layer 782 can calculate a new feature amount based on the feature amount extracted by the input layer 781, the temporal change of the input data, and the initial value.
  • the input layer 781 and the output layer 782 have multiple LSTM layers. Each of input layer 781 and output layer 782 may have three or more LSTM layers.
  • An intervention effect prediction model is generated by executing machine learning, which will be described later, on the neural network of the intervention effect prediction unit 78.
  • the input data to be input to the input layer 781 may be, for example, a parameter indicating the feature amount extracted from the first prediction information regarding the target part at the second point in time when a predetermined period has elapsed from the first point in time.
  • the input data may be information indicating an intervention method included in intervention information 85, which will be described later.
  • the intervention effect prediction unit 78 uses the intervention information 85 as input data, the intervention effect prediction unit 78 selects at least one of the intervention methods included in the intervention information 85 and predicts the effect of the intervention. Second prediction information can be output.
  • the output layer 782 In response to input of the above-described input data to the input layer 781, the output layer 782 outputs the second prediction information indicating the method of intervention for the subject and the effect of the intervention.
  • the second prediction information is, for example, information representing the extent to which the symptoms of the disease related to the target site of the subject when the intervention at the second time is applied or the extent to which the progression of the symptoms is suppressed good. More specifically, the intervention effect prediction unit 78 may output information shown below as the second prediction information. - How close the angle around the target site is to a normal angle. • How close to normal size the target area grows and shrinks. - What percentage of wrinkles and blemishes in the target area are alleviated? ⁇ How long the state of the target part is maintained. • How much walking ability (including the ability to climb stairs) improves.
  • the second prediction information is the same type of data as the first prediction information.
  • the second predictive information may be information regarding the subject's weight, BMI, waist circumference, visceral fat content, blood pressure, blood sugar level, lipids, uric acid level, or liver function value.
  • the second predictive information may be information regarding the subject's hair count, sex hormone level, Norwood classification, or Ludwig classification.
  • the second predictive information may be information about the subject's visual acuity, visual field, degree of opacity of the crystal of the eye, or Emery-Little classification.
  • the second predictive information is information on the degree of tooth or gum pain, degree of tooth or gum swelling, number of remaining teeth, gingivitis index, or periodontal pocket depth of the subject.
  • the second predictive information is information on the degree of pain, degree of swelling, joint angle, joint range of motion, Larsen classification or Stein blocker classification of the subject's whole body, upper limbs or lower limbs.
  • the second predictive information may be information regarding the degree of pain, the degree of swelling, or the joint range of motion of the subject's hand.
  • the second predictive information may be information regarding the degree of pain, degree of swelling, joint range of motion, HV angle or M1-M2 angle of the subject's foot.
  • the second predictive information is information about the degree of pain, degree of swelling, joint angle, joint range of motion, or KL classification of the subject's whole body, upper limbs, or lower limbs.
  • the second predictive information may be information about the degree of curvature of the spine of the subject, the range of motion of the spine, or the KL classification.
  • the second predictive information may be information about the subject's degree of curvature of the spine, range of motion of the spine, or KL classification.
  • the second predictive information may be information about the subject's muscle mass.
  • the intervention information 85 is information about interventions whose effects are estimated by the intervention effect prediction unit 78 .
  • Intervention information 85 whose efficacy is estimated includes, for example, non-invasive treatments such as weight control, hyperthermia therapy, ultrasound therapy, wearing braces, or taking supplements. Also, as the intervention information 85, the effect of invasive treatment such as surgical treatment may be estimated.
  • FIG. 13 is a diagram showing an example of the configuration of a neural network included in the predicted image generation section 72C.
  • the predictive image generation model applying the adversarial generation network has two networks, a generator 721C and a classifier 722C, as shown in FIG.
  • the first learning unit 74 acquires the subject image and the first prediction information from the storage unit 8C and inputs them to the generator 721C.
  • the first learning unit 74 also inputs the second prediction information generated by the intervention effect prediction unit 78 to the generator 721C.
  • the generator 721C generates predicted image candidates (false images) from the subject image, the first prediction information, and the second prediction information.
  • the generator 721C may refer to real images included in the first training data set 82 to generate predicted image candidates.
  • the first learning unit 74 inputs the predicted image candidate generated by the generator 721C and the real image included in the first training data set 82 to the classifier 722C.
  • the classifier 722C takes as inputs the authentic images from the first training data set 82 and the predicted image candidates generated by the generator 721C and outputs for each image the probability that it is the authentic image. .
  • the first learning unit 74 calculates a classification error that indicates how accurate the probability output by the discriminator 722C is.
  • the first learning unit 74 iteratively improves the discriminator 722C and the generator 721C using the error back propagation method.
  • the first learning unit 74 updates the weight and bias of the classifier 722C and the weight and bias of the generator 721C until the probability output by the classifier 722C satisfies a predetermined criterion.
  • the predicted image generation unit 72C can generate a predicted image that is indistinguishable from the real thing.
  • the third learning section 79 controls learning processing for the neural network of the intervention effect prediction section 78 .
  • Third teacher data 84 is used for this learning.
  • the third teacher data 84 is data used for machine learning to generate an intervention effect prediction model.
  • the third teacher data 84 includes third learning input data used as input data and third teacher data for calculating the error between the first prediction information output by the intervention effect prediction unit 78. .
  • the third learning input data for example, for each of a plurality of patients to whom the intervention was applied, information indicating the time when the intervention was applied, a patient image showing the target site of each patient, and each patient's It may also include symptom information regarding the onset or progression of symptoms at the target site at the time the patient image was captured.
  • the third training data is a patient image showing the target region of the patient at a time after the patient image used for the third learning input data was captured (for example, one year later), and It may also include symptom information regarding the onset or progression of symptoms at the target site of the patient.
  • the third training data may include symptom information regarding the target region of each patient at the time when the patient's image was captured.
  • the symptom information may include information regarding the onset time of the patient's disease or the progression of symptoms.
  • the third teacher data 84 includes state information indicating the state of the target part of each patient acquired at a plurality of past points, and indicates the state information for each patient and the intervention applied to each patient. It may be information associated with intervention information, that is, effect information.
  • the third teacher data 84 includes patient images obtained at a plurality of time points from each of a plurality of patients to whom intervention has been applied in the past; may be time-series data associated with .
  • FIG. 14 is a flow chart showing an example of the flow of learning processing of the neural network of the intervention effect prediction unit 78. As shown in FIG.
  • the third learning unit 79 acquires the third learning input data included in the third teacher data 84 from the storage unit 8C (step S41).
  • the third learning input data includes, for example, (a) information indicating the time when the intervention was applied to each of a plurality of patients to whom the intervention was applied, and (b) the patient showing the target site of each patient Image pixel data and (c) symptom information regarding the onset or progression of symptoms at the site of interest at the time the patient image was taken for each patient are included.
  • the third learning unit 79 determines a certain patient (step S42).
  • the third learning unit 79 acquires (a) information indicating the time when the intervention was applied for a certain patient to whom the intervention was applied, (b) the target of the certain patient, and The pixel data of the patient image showing the region and (c) the symptom information regarding the onset or progression of the symptom of the target region at the time the patient image of a certain patient was captured are input to the input layer 781 (step S43).
  • the third learning unit 79 acquires output data, which is information indicating at least one of the method of intervention for a certain patient and the effect of the intervention, from the output layer 782 (step S44).
  • This output data contains the same content as the third teacher data.
  • the third learning unit 79 acquires third teacher data included in the third teacher data 84 . Then, the third learning unit 79 compares the acquired output data with the information indicating the intervention method for a certain patient and the effect of the intervention, which is included in the third teacher data, and calculates the error (step S45).
  • the third learning unit 79 adjusts the intervention effect prediction model so that the error becomes smaller (step S46).
  • any known method can be applied to adjust the intervention effect prediction model.
  • error backpropagation may be employed as a method of adjusting the intervention effect prediction model.
  • the intervention effect prediction model after adjustment becomes a new intervention effect prediction model, and the intervention effect prediction unit 78 uses the new intervention effect prediction model in subsequent calculations.
  • the parameters used in the intervention effect prediction unit 78 may be adjusted.
  • the parameters include, for example, parameters used in the input layer 781 and the output layer 782.
  • the parameters include weighting factors used in the input layer 781 and the LSTM layers of the output layer 782 .
  • the parameters may also include filter coefficients.
  • Third learning unit 79 if the error is not within a predetermined range and if patient images of all patients included in third teacher data 84 have not been input (NO in step S47), the patient is changed (step S48), and the process returns to step S43 to repeat the learning process. Third learning unit 79 learns when the error is within a predetermined range and when patient images of all patients included in third teacher data 84 have been input (YES in step S47). End the process.
  • the third teacher data 84 is data used for machine learning to generate an intervention effect prediction model.
  • the third teacher data 84 includes state information indicating the state of the target part of each patient acquired at a plurality of past points, and indicates the state information for each patient and the intervention applied to each patient. Information associated with intervention information, ie effect information, may be included.
  • the third teacher data 84 includes third learning input data used as input data and third teacher data for calculating the error between the first prediction information output by the intervention effect prediction unit 78. .
  • the third learning input data is, for example, (a) information about a patient to whom the intervention was applied, indicating the time when the intervention was applied, and (b) pixel data of a patient image showing the target region of the patient. and (c) symptom information regarding the onset or progression of symptoms at the site of interest at the time the patient image of a patient was taken.
  • the image data used as the third learning input data may be medical image data showing target regions of a plurality of patients.
  • the medical image data includes, for example, at least one of X-ray image data, CT image data, MRI image data, PET image data, and ultrasound image data obtained by imaging target regions of a plurality of subjects. You can stay.
  • the third training data is a patient image showing the target region of the patient at a time after the patient image used for the third learning input data was captured (for example, one year later), and State information indicating the state of the target site of the patient and symptom information regarding the target site may be included.
  • the third training data is a patient image obtained at a plurality of times from each of a plurality of patients to whom intervention has been applied in the past, and information about joint symptoms at the time when the patient image was captured. It may be associated time-series data.
  • the input data used when the intervention effect prediction unit 78 learns is (a) a subject image showing the subject's target part at a certain point A, which is included in the third learning input data.
  • the intervention effect prediction unit 78 outputs, as output data, first prediction information regarding the target site at time B after a predetermined period (for example, three years) has elapsed from time A based on the above-described input data.
  • the intervention effect prediction unit 78 outputs, for example, the angle around the target site at time B of the subject, the degree of increase and shrinkage of the target site, the degree of wrinkles and blemishes of the target site, the target site information indicating the timing and degree of onset of pain, and the timing at which invasive treatment is required for the target site.
  • the output data shown here is an example, and is not limited to these.
  • the input data used when the intervention effect prediction unit 78 learns is included in the information indicating the degree of onset or progress of the disease regarding the target site of the patient at a certain point B included in the third teacher data 84 and the intervention information. information indicating how interventions should be implemented.
  • the intervention effect prediction unit 78 outputs, as output data, information indicating the method of intervention for the subject and the effect of the intervention.
  • the intervention effect prediction unit 78 outputs as output data, for example, the extent to which the symptoms of the disease related to the target site of the patient are improved or the progression of the symptoms when the intervention at time point B is applied. Outputs information indicating the degree of suppression. More specifically, the intervention effect prediction unit 78 may output the aforementioned second prediction information as the output data.
  • FIG. 15 is a flow chart showing an example of the flow of processing performed by the prediction systems 100 and 100a according to this embodiment.
  • the prediction information acquisition unit 71 acquires a subject image.
  • the basic information acquisition unit 76 acquires basic information (step S51: acquisition step).
  • the prediction information generation unit 75B generates first prediction information in response to the input of the subject image and the basic information, and transmits the first prediction information to the prediction information acquisition unit 71 and the intervention effect prediction unit. 78 (step S52: first information prediction step).
  • the intervention effect prediction unit 78 refers to the intervention information 85 and selects at least one of the intervention methods included in the intervention information 85 (step S53: intervention method selection step).
  • the intervention effect prediction unit 78 generates second prediction information about the selected intervention method in response to the input of the first prediction information, and transmits the second prediction information to the prediction image generation unit 72C and the output control unit 72C. Output to the section 73C (step S54: intervention effect prediction step).
  • the predicted image generation unit 72C generates a predicted image in response to input of the subject image, the first predicted information, and the second predicted information, and outputs the predicted image to the terminal device 2 (step S55: prediction image generation step).
  • the prediction systems 100 and 100a can output as a visually easy-to-understand prediction image that the state of the target part differs depending on the presence or absence of the effect of the intervention at the second time point.
  • the predicted image is generated from the target person image, which is the image of the target person, the predicted image is a realistic image that is persuasive to the target person. Therefore, for example, if a doctor in charge of the subject presents it to the subject, the subject can effectively understand the need for intervention, and the subject's motivation for intervention can be enhanced.
  • control blocks of the prediction devices 1, 1A, 1B, 1C may be realized by logic circuits (hardware) formed in integrated circuits (IC chips) or the like. , may be implemented by software.
  • the prediction devices 1, 1A, 1B, and 1C are equipped with computers that execute program instructions, which are software that implements each function.
  • This computer includes, for example, one or more processors, and a computer-readable recording medium storing the program.
  • the processor reads the program from the recording medium and executes it, thereby achieving the object of the present disclosure.
  • a CPU Central Processing Unit
  • the recording medium a "non-temporary tangible medium" such as a ROM (Read Only Memory), a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used.
  • a RAM Random Access Memory
  • the program may be supplied to the computer via any transmission medium (communication network, broadcast wave, etc.) capable of transmitting the program.
  • One aspect of the present disclosure may also be embodied in the form of a data signal embedded in a carrier wave, with the program embodied by electronic transmission.

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