WO2024143476A1 - 情報処理システム、情報処理システムの制御方法、制御プログラム、記録媒体 - Google Patents

情報処理システム、情報処理システムの制御方法、制御プログラム、記録媒体 Download PDF

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WO2024143476A1
WO2024143476A1 PCT/JP2023/046960 JP2023046960W WO2024143476A1 WO 2024143476 A1 WO2024143476 A1 WO 2024143476A1 JP 2023046960 W JP2023046960 W JP 2023046960W WO 2024143476 A1 WO2024143476 A1 WO 2024143476A1
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information
bone
subject
information processing
surgical treatment
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PCT/JP2023/046960
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English (en)
French (fr)
Japanese (ja)
Inventor
健一 渡辺
政之 京本
研太郎 亀井
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Kyocera Corp
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Kyocera Corp
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Priority to JP2024567936A priority Critical patent/JP7736949B2/ja
Priority to EP23912254.2A priority patent/EP4643800A1/en
Publication of WO2024143476A1 publication Critical patent/WO2024143476A1/ja
Anticipated expiration legal-status Critical
Priority to JP2025142597A priority patent/JP2025172095A/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G06N3/0455—Auto-encoder networks; Encoder-decoder networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/108—Computer aided selection or customisation of medical implants or cutting guides
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/30—Surgical robots

Definitions

  • the present disclosure relates to an information processing system that assists in planning surgical treatment appropriate for the affected bone of a subject, and a control method thereof.
  • Patent Document 1 discloses a technology that allows doctors who are about to perform surgery on a patient's joints to suggest surgical procedures with greater precision than ever before.
  • an information processing system includes an acquisition unit that acquires input information including a medical image showing the affected bone of a subject; an identification unit that has a first learning model trained using first teacher data including bone information related to at least one of bone density and bone quality of the bone of each of a plurality of learning subjects before a surgical treatment including an implant placement procedure is applied, shape characteristics, and treatment information related to the surgical treatment applied to the bone; and an output unit that outputs at least one of the following: instrument information indicating a surgical instrument to be used in a surgical treatment suitable for the affected bone of the subject identified by the identification unit based on the input information; prediction information predicting the state of the affected bone of the subject after the surgical treatment has been applied; parameter information indicating parameters that may be selected when a surgical treatment is applied to the affected bone of the subject; and implant information related to an implant that may be embedded in the affected bone of the subject.
  • a control method of an information processing system includes an acquisition step of acquiring input information including a medical image showing the affected bone of a subject, and an output step of outputting, using a first learning model trained using first teacher data including bone information regarding at least one of bone density and bone quality of the bone before a surgical treatment including an implant placement procedure is applied to each of a plurality of learning subjects, at least one of instrument information indicating a surgical instrument to be used in a surgical treatment suitable for the affected bone of the subject, which is identified based on the input information, prediction information predicting the state of the affected bone of the subject after the surgical treatment has been applied, parameter information indicating parameters that may be selected when a surgical treatment is applied to the affected bone of the subject, and implant information regarding an implant that may be embedded in the affected bone of the subject.
  • the information processing system according to each aspect of the present disclosure may be realized by a computer.
  • the control program for the information processing system that causes the computer to operate as each unit (software element) of the information processing system to realize the information processing system, and the computer-readable recording medium on which it is recorded also fall within the scope of the present disclosure.
  • FIG. 1 is a diagram illustrating a configuration example of an information processing system according to an embodiment of the present disclosure.
  • FIG. 13 is a diagram illustrating a configuration example of an information processing system according to another embodiment of the present disclosure.
  • 1 is a block diagram illustrating an example of a configuration of an information processing device according to an embodiment of the present disclosure.
  • a figure showing an example of the configuration of a first learning model executed by the identification unit. 13 is a flowchart showing an example of the flow of a learning process by a learning unit.
  • 11 is a flowchart illustrating an example of a flow of processing performed by an information processing device.
  • 1 is a block diagram illustrating an example of a configuration of an information processing device according to an embodiment of the present disclosure.
  • FIG. 1 is a diagram illustrating a configuration example of an information processing system according to an embodiment of the present disclosure.
  • 1 is a block diagram showing an example of a configuration of an information processing device according to an embodiment of the present disclosure.
  • FIG. 2 is a diagram illustrating an example of a configuration of a learning model executed by an estimation unit.
  • 13 is a flowchart showing an example of the flow of a learning process by a learning unit.
  • 11 is a flowchart illustrating an example of a flow of processing performed by an information processing device.
  • FIG. 1 is a diagram illustrating a configuration example of an information processing system according to an embodiment of the present disclosure.
  • One aspect of the present disclosure can assist in planning surgical treatment appropriate for the affected bone of a subject.
  • the subject to which the application of a surgical treatment is considered is a human (i.e., a "subject") will be described as an example, but the subject is not limited to humans.
  • the "subject" of the present disclosure may be a mammal other than a human, such as an equine, feline, canine, bovine, or porcine animal.
  • the present disclosure also includes, among the embodiments below, embodiments in which the "subject” is replaced with "animal” if the embodiment is applicable to these animals.
  • the information processing device 1 outputs at least any one of instrument information, prediction information, parameter information, and implant information identified using a trained first learning model 34 based on input information including medical images showing the affected bone of the subject.
  • the instrument information is information indicating a surgical instrument to be used in a surgical treatment method suitable for the affected bone of the subject.
  • the prediction information is information predicting the state of the affected bone of the subject after a surgical treatment method suitable for the affected bone of the subject has been applied.
  • the parameter information is information indicating parameters that may be selected when applying a surgical treatment method to the affected bone of the subject.
  • the implant information is information regarding an implant that may be embedded in the affected bone of the subject.
  • the implant information may include inventory information of implants that may be embedded in the affected bone of the subject.
  • the trained first learning model 34 is trained using first training data including bone information on at least one of the bone density and bone quality of the bone of each of a plurality of training subjects before the application of a surgical treatment including implant placement, shape characteristics, and treatment information on the surgical treatment applied to the bone.
  • the training subjects include, for example, patients with a specific disease, but may also include subjects unrelated to the disease, and will be referred to simply as "patients" in the following description.
  • the medical image may be, for example, at least one of an X-ray image, a CT (Computed Tomography) image, a Magnetic Resonance Imaging (MRI) image, and an ultrasound image, but is not limited to these.
  • the medical image may be, for example, an inspection device image obtained from an inspection device (e.g., an X-ray inspection device), or an inspection device image with noise reduction.
  • the area shown in the medical image may be any of the head, neck, chest, lower back, hip joint, knee joint, ankle joint, foot, toe, shoulder joint, elbow joint, wrist joint, hand, and fingers.
  • the medical image may be, for example, a dental image.
  • Bone density may be measured, for example, by measuring actual bone density from at least one of the hand, lumbar spine, proximal femur, tibia, heel, and arm (e.g., radius). Bone density may be measured, for example, by single energy X-ray absorptiometry, dual energy X-ray absorptiometry (DXA), ultrasound, or quantitative computed tomography (CT).
  • DXA dual energy X-ray absorptiometry
  • CT quantitative computed tomography
  • the bone density of the bone may be, for example, the bone density of the patient estimated by inputting an X-ray image of the patient's bone into a trained estimation model machine-learned using teacher data including an X-ray image of the bone and the measured bone density of the bone.
  • the bone density of the bone may be, for example, the future bone density of the patient predicted by inputting an X-ray image of the patient's bone into a trained prediction model machine-learned using teacher data including an X-ray image of the patient's bone and the measured bone density at a time when a predetermined period (e.g., one year, three years, etc.) has elapsed since the X-ray image was taken.
  • the bone density of the bone may be information about the density of the bone.
  • the bone density of the bone may be, for example, information in accordance with the definition of the osteoporosis guidelines or may be a unique index.
  • the bone density of the bone is information related to the teacher data.
  • the bone density of the bone may be expressed by at least one of bone mineral density per unit area (g/cm 2 ), bone mineral density per unit volume (g/cm 3 ), YAM, T-score, and Z-score.
  • YAM is an abbreviation for “Young Adult Mean” and is sometimes called the young adult average percentage.
  • the bone quality may be based on at least one of the following: statistical properties of bone, geometric properties of bone, mechanical properties of bone, and chemical properties of bone.
  • the bone quality may be based on at least one of the following: bone metabolism marker, sex, race, menopausal status, age, cortical bone status, cancellous bone status, cancellous bone trabecular status, disease information, bone evaluation information, drug information, and the presence or absence of fracture. More specifically, the bone quality may be based on at least one of the following: bone formation marker, bone resorption marker, bone quality marker (e.g., vitamin K value), cortical bone thickness, trabecular density, trabecular direction, and cancellous bone structure index (trabecular bone score), but is not limited thereto.
  • the disease information may include at least one of osteoporosis, rheumatism, bone necrosis (e.g., femoral head necrosis, etc.), systemic sclerosis, kidney disease, and osteopetrosis.
  • the bone assessment information may include information assessed by a Fracture Risk Assessment Tool (FRAX (registered trademark)).
  • the drug information may include at least one of the trade name, generic name, dosage, administration period, and administration method (e.g., oral, intravenous injection, intramuscular injection, subcutaneous injection, etc.) for drugs including at least one of drugs that inhibit bone resorption, drugs that promote bone formation, and other drugs (e.g., calcium preparations, vitamin preparations, female hormone preparations, etc.).
  • the bone quality may also include, for example, the type of medullary cavity shape.
  • the Dorr classification may be used for the medullary cavity shape.
  • the medullary cavity shape may be classified as follows using at least one of the thickness of the cortical bone and the shape of the medullary cavity: - Type A: The cortical bone is thick and the medullary cavity is narrow and thin. - Type B: This type is between Type A and Type C, and the medullary cavity is neither narrow nor wide. - Type C: The cortical bone is thin and the medullary cavity is wide.
  • the geometric features include, for example, at least the relative positional relationship, dimensions, shape, and general shape of the bones.
  • the geometric features may include, for example, the shape of the pelvis, the dimensions of the pelvis, the shape of the acetabulum of the pelvis, the dimensions of the acetabulum of the pelvis, the position of the femoral head, the dimensions of the femoral head, and the positional relationship of the femur and the pelvis (such as the femoral head and the acetabulum of the pelvis).
  • the geometric features may include, for example, the position of the femoral head, the femoral curvature angle, the dimensions of the distal part of the femur, the shape of the femur, the angle of the femoral articular surface, the amount of posterior offset of the femur, the dimensions of the tibia (e.g., the proximal part in contact with the tibia), the shape of the tibia (e.g., the proximal part in contact with the tibia), the dimensions of the patella, the shape of the patella, and the positional relationship of the femur and the tibia (such as the angle between the femur and the tibia).
  • the tibia e.g., the proximal part in contact with the tibia
  • the shape of the tibia e.g., the proximal part in contact with the tibia
  • the patella the shape of the patella
  • the relative positional relationship of bones among the shape characteristics may be, for example, at least two distances between the alveolar bone crest, the floor of the maxillary sinus, the floor of the nasal cavity, the upper edge of the mandibular canal, and the mental foramen.
  • the surgical treatment may be at least one of artificial joint replacement surgery and dental implant surgery, and may be performed by a surgical robot.
  • the information processing device 1 and the control device for the surgical robot may be communicatively connected, and various information output from the information processing device 1 may be transmitted to the control device for the surgical robot.
  • the surgical robot can be appropriately configured by referring to the instrument information, prediction information, parameter information, and implant information output from the information processing device 1.
  • the settings of the surgical robot can include at least one of the type and size of the instruments used by the surgical robot.
  • various parameters of the instruments used by the surgical robot during surgery can be set. Examples of the various parameters include the angle at which the bone (e.g., the femoral neck in the case of total hip replacement surgery, and the femoral condyle, distal femur, or proximal tibia in the case of total knee replacement surgery) is resected, the amount of resection, the insertion angle of the instrument into the human body (including the medullary cavity, for example), the insertion depth, the number of rotations of the instrument (including torque), the removal angle of the instrument, and the bone cutting time.
  • These various parameters can be set in combination with each other, or multiple parameters can be set.
  • Fig. 1 is a diagram showing an example of the configuration of the information processing system 100a in a medical facility 8 in which an information processing device 1 has been introduced.
  • a LAN local area network
  • the information processing device 1 and terminal device 7 are connected to the LAN, but this is not a limitation.
  • the network in the medical facility 8 may be the Internet, a telephone communication line network, an optical fiber communication network, a cable communication network, or a satellite communication network.
  • the information processing system 100a is compatible with a hospital information system (HIS: Hospital Information System), a radiology information system (RIS: Radiology Information System), a picture archiving and communication system (PACS: Picture Archiving and Communication System), etc. in the medical institution 8.
  • communication in the information processing system 100a complies with international standards such as DICOM (Digital Imaging and Communications in Medicine).
  • a server device (not shown) that is communicatively connected to the terminal device 7, the medical image management device 5, the inventory management device 6, etc. may be configured to have various functions of the information processing device 1.
  • the server device functions as the information processing device 1 in the information processing system 100a.
  • Information regarding whether or not an implant has been antibacterial treated includes, for example, information indicating the suitability of the antibacterial implant for the patient, information indicating the probability of infection occurring at the affected area, information indicating the probability of reducing infection at the affected area, a prediction of the degree of inflammation at the affected area, or information indicating the probability of side effects occurring when an antibacterial treated implant is applied.
  • the first teacher data 32 may include information indicating the concentration of blood markers related to the patient's resistance to bacterial infection and the results of a culture test in which a specific bacterium is cultured using the patient's blood.
  • step S6 If the error is not within the predetermined range and explanatory variables for all patients included in the first teacher data 32 have not been input (NO in step S6), the learning unit 25 returns to step S2 and repeats the learning process. If the error is within the predetermined range and explanatory variables for all patients included in the first teacher data 32 have been input (YES in step S6), the learning unit 25 ends the learning process.
  • the estimation unit 27c may output at least one of a first estimation result assuming that the subject has reached menopause and a second estimation result assuming that the subject has not yet reached menopause. This makes it possible to output an estimation result with higher accuracy that takes into account the presence or absence of menopause.
  • the estimation unit 27c may simultaneously output a first estimation result assuming that the subject has reached menopause and a second estimation result assuming that the subject has not yet reached menopause.
  • the information processing device 1c may not be a computer installed in a specific medical facility 8, but may be communicably connected to a LAN installed in each of the multiple medical facilities 8 via a communication network 9.
  • Fig. 13 is a diagram showing an example of the configuration of an information processing system 100d according to still another aspect of the fourth embodiment.
  • the program may be recorded on one or more computer-readable recording media, not on a temporary basis.
  • the recording media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.
  • An information processing system comprises an acquisition unit that acquires input information including medical images depicting the affected bone of a subject; an identification unit having a first learning model trained using first teacher data including bone information regarding at least one of bone density and bone quality of the bone of each of a plurality of patients before a surgical treatment method including an implant insertion procedure is applied, shape characteristics, and treatment information regarding the surgical treatment method applied to the bone; and an output unit that outputs at least one of instrument information indicating a surgical instrument to be used in a surgical treatment method suitable for the affected bone of the subject identified by the identification unit based on the input information, prediction information predicting the condition of the affected bone of the subject after the surgical treatment method is applied, parameter information indicating parameters that may be selected when applying a surgical treatment method to the affected bone of the subject, and implant information regarding an implant that may be embedded in the affected bone of the subject.
  • the implant information may include inventory information of implants that can be embedded in the affected bone of the subject.
  • the first teacher data may include, as the treatment information, the content and results of a surgical treatment method applied to the affected bone of each of the multiple patients.
  • the first teacher data may include information indicating a surgical treatment applied to the affected bone of each of the multiple patients, medical images showing the affected bone of each of the multiple patients before the surgical treatment was applied, and information regarding an implant embedded in the affected bone of each of the multiple patients.
  • the first teacher data may include information regarding surgical instruments used in a surgical treatment method applied to the affected bone of each of the multiple patients.
  • the output unit may further output inventory information of surgical instruments to be used in a surgical treatment method suitable for the affected bone of the subject, identified by the identification unit based on the input information.
  • the first teacher data may further include at least one of event information regarding an event that occurred during an implant placement procedure in each of the bones of the multiple patients, and treatment information performed during the implant placement procedure.
  • the output unit may output the prediction information including a first predicted image generated from the medical image included in the input information, the first predicted image assuming a state of the affected bone of the subject after a surgical treatment is applied to the affected bone.
  • the first predicted image may be an image generated from the medical images included in the input information using a second learning model trained using second training data including medical images of the affected bone of a patient to which a surgical treatment suitable for the affected bone of the subject has been applied, the medical images being captured before and after the application of the surgical treatment.
  • the information processing system may output the parameter information including surgical parameters estimated from the results of image analysis of the first predicted image in aspect 8 or 9 above, the surgical parameters being selectable by a medical professional who applies to the subject a surgical treatment method suitable for the affected bone of the subject.
  • the information processing system may further include a generating unit that, when an operation instructing a change to the estimated surgical parameters is received in the above-mentioned aspect 10, generates a second predicted image by performing processing on the first predicted image corresponding to the change.
  • the information processing system may further include an estimation unit that outputs an estimation result regarding the strength of the bone in the affected area of the subject from the first predicted image in any of aspects 8 to 11 above.
  • the first predicted image may be an image converted from a two-dimensional image to a three-dimensional image.
  • the control method of the information processing system includes an acquisition step of acquiring input information including a medical image showing the affected bone of the subject, and an output step of outputting, using a first learning model trained using first teacher data including bone information regarding at least one of bone density and bone quality of the bone before a surgical treatment including an implant placement procedure is applied to each of a plurality of patients, at least one of instrument information indicating a surgical instrument to be used in a surgical treatment suitable for the affected bone of the subject, which is identified based on the input information, prediction information predicting the state of the affected bone of the subject after the surgical treatment has been applied, parameter information indicating parameters that may be selected when a surgical treatment is applied to the affected bone of the subject, and implant information regarding an implant that may be embedded in the affected bone of the subject.
  • the control program of the information processing device is a control program for causing a computer to function as the information processing system described in any one of aspects 1 to 15 above, and is a control program for causing a computer to function as the acquisition unit, the identification unit, and the output unit.

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PCT/JP2023/046960 2022-12-28 2023-12-27 情報処理システム、情報処理システムの制御方法、制御プログラム、記録媒体 Ceased WO2024143476A1 (ja)

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JP2024567936A JP7736949B2 (ja) 2022-12-28 2023-12-27 情報処理システム、情報処理システムの制御方法、制御プログラム、記録媒体
EP23912254.2A EP4643800A1 (en) 2022-12-28 2023-12-27 Information processing system, method for controlling information processing system, control program, and recording medium
JP2025142597A JP2025172095A (ja) 2022-12-28 2025-08-28 情報処理システム、情報処理システムの制御方法、制御プログラム、記録媒体

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JP2022-212518 2022-12-28
JP2022212518 2022-12-28

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JP2018501879A (ja) * 2014-12-24 2018-01-25 バイオアルファ コーポレイション 人工骨組織の製造システム及びその製造方法
JP2021115188A (ja) 2020-01-24 2021-08-10 コニカミノルタ株式会社 術式提案装置及びプログラム
JP2022500148A (ja) * 2018-09-12 2022-01-04 オルソグリッド システムズ，エスアーエス 人工知能の術中外科的ガイダンスシステムと使用方法

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JP2022013898A (ja) * 2020-06-30 2022-01-18 貴志 山本 情報処理装置及び学習済モデル
WO2022181154A1 (ja) * 2021-02-26 2022-09-01 京セラ株式会社 判定システム、判定システムの制御方法、および制御プログラム

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JP2018501879A (ja) * 2014-12-24 2018-01-25 バイオアルファ コーポレイション 人工骨組織の製造システム及びその製造方法
JP2022500148A (ja) * 2018-09-12 2022-01-04 オルソグリッド システムズ，エスアーエス 人工知能の術中外科的ガイダンスシステムと使用方法
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