WO2024090585A1 - 解析装置、解析方法、解析プログラム及び記録媒体 - Google Patents

解析装置、解析方法、解析プログラム及び記録媒体 Download PDF

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Publication number
WO2024090585A1
WO2024090585A1 PCT/JP2023/039051 JP2023039051W WO2024090585A1 WO 2024090585 A1 WO2024090585 A1 WO 2024090585A1 JP 2023039051 W JP2023039051 W JP 2023039051W WO 2024090585 A1 WO2024090585 A1 WO 2024090585A1
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Prior art keywords
analysis
image data
identification information
data
unit
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English (en)
French (fr)
Japanese (ja)
Inventor
健一 渡辺
信太郎 本多
貴朗 白鳥
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Kyocera Corp
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Kyocera Corp
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Priority to EP23882786.9A priority Critical patent/EP4610992A1/en
Priority to JP2024553271A priority patent/JPWO2024090585A1/ja
Publication of WO2024090585A1 publication Critical patent/WO2024090585A1/ja
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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
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • This disclosure relates to an analysis device, an analysis method, an analysis program, and a recording medium.
  • Patent Document 1 discloses a medical system having a medical device for observing a subject and a network device capable of communicating with the medical device.
  • An analysis device includes an acquisition unit that acquires medical data of a subject and second identification information, which is an encrypted version of the subject's first identification information, from a user terminal, an analysis unit that analyzes the acquired medical data, and a transmission unit that transmits the analysis results of the analysis by the analysis unit and the second identification information to the user terminal.
  • An analysis method includes at least one processor acquiring, from a user terminal, medical data of a subject and second identification information obtained by encrypting the first identification information of the subject, analyzing the acquired medical data, and transmitting the analysis results and the second identification information to the user terminal.
  • the analysis device may be realized by a computer.
  • the control program of the analysis system that causes the computer to operate as each part (software element) of the analysis system to realize the analysis system, and the computer-readable non-transitory recording medium on which it is recorded, also fall within the scope of the present disclosure.
  • FIG. 1 is a block diagram showing a configuration of an analysis system according to a first embodiment of the present disclosure.
  • FIG. 2 is a schematic diagram showing a part of the flow of data transmitted and received between an analysis device and a user terminal.
  • 10 is a flowchart showing a process flow of an analysis method executed by a control unit.
  • FIG. 11 is a block diagram showing a configuration of an analysis system according to a second embodiment of the present disclosure.
  • FIG. 13 is a diagram illustrating an example of a browser image.
  • FIG. 1 is a diagram showing an example of X-ray image data for which a bone density analysis is requested.
  • 13 is a schematic diagram showing an example of a browser image of an analysis result transmitted by a transmitting unit;
  • FIG. 13 is a diagram showing an example of a browser image on which derivation basis data is added in addition to analysis result data.
  • FIG. 9 is an enlarged schematic diagram of an image of the area shown in FIG. 8 .
  • FIG. 13 is a diagram showing an example of a browser image displaying a patient's current and future risk of fracture.
  • FIG. 1 is a block diagram showing a configuration of an analysis system 70 according to a first embodiment of the present disclosure.
  • the analysis system 70 can be used in a system for remotely analyzing images.
  • the analysis system 70 includes, for example, an analysis device 1 that analyzes medical data and a user terminal 20.
  • the analysis device 1 and the user terminal 20 are connected to be able to communicate with each other via the Internet.
  • the analysis system 70 accepts image analysis requests from the user terminal 20 via an information and communication network such as the Internet.
  • the analysis device 1 is a system that analyzes medical data to be analyzed that is sent from the user terminal 20, and returns the analysis results to the user terminal 20 via the Internet.
  • a user may be anyone who requests the analysis of medical data from the analysis system 70. Users are, for example, medical personnel such as doctors, medical technicians, and nurses, but are not limited to these.
  • the medical data is image data 301.
  • the medical data is not limited to image data 301, and for example, blood data, blood flow data, walking data, etc. may be used as medical data.
  • the analysis system 70 may analyze, for example, the blood data to perform a movement analysis of the patient.
  • each medical institution may have its own user terminal 201, 202, 203.
  • the user accesses the analysis device 1, for example, via the Internet, and requests analysis of the image data 301 from a screen displayed on a webpage. If a request for analysis of the same image data 301 is made from different user terminals 20, each image data 301 may be linked to the identification information of that terminal, saved, and analyzed.
  • the image data may be in a format that complies with DICOM (Digital Imaging and Communications in Medicine).
  • User terminal 20 may be, for example, a stationary personal computer equipped with a communication function, or a mobile terminal such as a tablet terminal. Since there may be multiple users, multiple user terminals are illustrated as 201, 202, 203, etc., but hereinafter, unless otherwise specified, the target terminal representing all terminals will be referred to as user terminal 20.
  • the analysis device 1 includes a control unit 16 that controls each part of the analysis device 1, a memory unit 17 that stores various data used by the control unit 16, and a communication unit 15 for communicating with other devices.
  • the control unit 16 includes a communication control unit 11 (transmission unit) that controls the communication unit 15, an acquisition unit 12 that acquires data transmitted from the user terminal 20 via the communication control unit 11, and an analysis unit 13 that analyzes the image data 301.
  • a communication control unit 11 transmission unit
  • an acquisition unit 12 acquisition unit 12 that acquires data transmitted from the user terminal 20 via the communication control unit 11
  • an analysis unit 13 that analyzes the image data 301.
  • the control unit 16 has at least one processor and at least one memory.
  • the processor can be configured using a general-purpose processor such as at least one MPU (Micro Processing Unit) or CPU (Central Processing Unit).
  • the memory may have multiple types of memory such as ROM (Read Only Memory) and RAM (Random Access Memory).
  • the processor realizes the function of the control unit 16 by expanding various control programs recorded in the ROM of the memory into the RAM and executing them.
  • the processor may also include a processor configured as an ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), PLD (Programmable Logic Device), or the like.
  • the storage unit 17 stores a browser image 200, acquired information 172, and analysis result data 173 (analysis results), which will be described later.
  • FIG. 2 is a schematic diagram showing part of the flow of data transmitted and received between the analysis device 1 and the user terminal 20.
  • the communication control unit 11 retrieves a browser image 200, which is an input screen, from the storage unit 17 via the communication unit 15 and displays it on a web page.
  • the user inputs acquired information 172, which includes image data 301 to be analyzed, patient identification information 320 (first identification information), and attribute information 303, into the browser image 200 and sends it to the analysis device 1.
  • the user can input the acquired information 172 in the browser image 200 and send the acquired information 172 to the analysis device 1 by clicking the "Send" button 204 described later.
  • the method of input by the user is not limited to this, and for example, the user may input the information by drag and drop or by specifying a directory.
  • the image data may be uploaded to the server while maintaining the state of the DICOM file, or only the pixel values and array information of the image extracted from the DICOM file, or the minimum attribute information 303 embedded in the DICOM file may be sent. By doing so, anonymity can be more assured.
  • the image data 301 and attribute information 303 are not encrypted, but the patient identification information 320 is encrypted and sent, as described in detail later. Not limited to this, the image data 301 does not need to be encrypted in particular, but the image data 301 may also be encrypted and sent.
  • a specific example of the browser image 200 will be described later.
  • the type of data of the image data 301 may be any data that can be transmitted via the Internet.
  • the content of the image is not particularly limited, and may be, for example, a medical X-ray image, a micrograph, a CT (Computer Tomography) image, an MRI (Magnetic Resonance Imaging) image, an ultrasound image, a PET image, or the like, depending on the application of the analysis system 70.
  • the analysis system 70 estimates bone density or the like
  • the type of the image data 301 is a bone X-ray image, a CT image, an MRI image, or the like.
  • the estimation may include calculation.
  • the type of the image data 301 is data of a micrograph of cells, or the like.
  • the bone density may be represented 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 may be called young adult average percent.
  • the bone mineral density may be a value expressed as bone mineral density per unit area (g/cm 2 ) and YAM (%).
  • the bone mineral density may be an index determined by the guidelines or an original index.
  • Patient identification information 320 is, for example, information indicating whose image data 301 being analyzed belongs to, and may be an ID number, or information including age, address, name, and image identification number.
  • the ID number may be any combination of multiple alphanumeric characters, for example.
  • the image identification number may be a number assigned by the imaging device when the image is captured.
  • the patient identification information 320 is encrypted when it is sent to the analysis device 1, for example, and encrypted patient information 302 (second identification information) is generated. This allows the image data 301 to be transmitted and analyzed while keeping personal information confidential.
  • the encryption process may be performed by the user terminal 20 that transmits the patient identification information 320, or by the analysis device 1 that receives it.
  • the method of encryption may be a known method and is not limited.
  • the communications control unit 11 may transmit an encryption application to the user terminal 20 when accessed by the user terminal 20 via the Internet.
  • This encryption application is an application that encrypts the patient identification information 320 entered in the browser image 200, and decrypts the encrypted patient information 302 transmitted to the user terminal 20 after the image data 301 is analyzed.
  • the patient identification information 320 can be automatically encrypted and decrypted without the user having to encrypt and decrypt the patient identification information 320 themselves.
  • the encrypted second identification information does not have to be displayed on the user terminal 20. The user can use the identification information without being aware that it is encrypted.
  • the attribute information 303 includes, for example, the name and address of the hospital where the image data 301 was captured, the hospital's ID number, the hospital's email address, etc.
  • the acquisition unit 12 acquires encrypted patient information 302, which is the encrypted image data 301, attribute information 303, and patient identification information 320 contained in the acquisition information 172 transmitted from the user terminal 20, and sends the encrypted patient information 302 to the analysis unit 13.
  • the acquisition unit 12 stores the acquired image data 301, encrypted patient information 302, and attribute information 303 in the storage unit 17 while linking them to each other.
  • the analysis unit 13 analyzes the image data 301 acquired from the acquisition unit 12 using the machine model 131.
  • the analysis unit 13 includes the machine model 131.
  • the machine model 131 is a trained machine model that has learned to output predetermined information, which is an analysis result, from the input image data 301.
  • the analysis content based on the output of the machine model 131 will be referred to as the "analysis result derived by the machine model” or simply as the “analysis result”.
  • data including the analysis result will be referred to as the analysis result data 173.
  • the analysis result data 173 is linked to the encrypted patient information 302 and the attribute information 303 and stored in the storage unit 17.
  • the image data 301 may be analyzed in a format compliant with DICOM (Digital Imaging and Communications in Medicine), or may be analyzed as the pixel values and array information of an image extracted from a file compliant with DICOM. Alternatively, it may be converted into another image format before being analyzed.
  • the other image format may be a general-purpose image format such as JPG, BMP, TIF, or PNG. In this way, a machine model created for images in a general-purpose format can be used as is.
  • the machine model 131 is not limited to any particular one, but may include, for example, a convolutional neural network model (NNM; Neural Network Model) for image analysis. Specific examples of the machine model 131 and the analysis result data 173 will be described in detail in embodiment 2.
  • NVM convolutional neural network model
  • the communication control unit 11 links the analysis result data 173 analyzed by the analysis unit 13, the encrypted patient information 302, and the attribute information 303 together and transmits them to the user terminal 20 via the communication unit 15.
  • the encrypted patient information 302 is decrypted and patient identification information 320 is generated.
  • the analysis device 1 has sent an encryption application to the user terminal 20, the encryption application automatically decrypts the encrypted patient information 302, so there is no need for the user to decrypt it.
  • decryption means, for example, restoring the encrypted encrypted patient information 302 to the original patient identification information 320.
  • the user terminal 20 can receive the decrypted patient identification information 320, the analysis result data 173, and the attribute information 303. This allows the user terminal 20 to store the analysis result data 173 in association with the patient identification information 320.
  • the analysis device 1 is shown as being arranged together in a single housing. However, the analysis device 1 does not have to be arranged together in a single housing. For example, some of the components included in the analysis device 1 may be arranged as separate devices. In addition, some or all of the components included in the analysis device 1 may be arranged on the cloud.
  • the analysis system 70 having the above configuration can analyze image data 301 requested for analysis by a user while keeping personal information confidential. Therefore, even when analysis requests are received from an unspecified number of users, it is possible to analyze each user's image data 301 while protecting personal information.
  • the image data 301 may be, for example, medical image data of the subject photographed with an endoscope. More specifically, the image data 301 may include medical image data of the subject's nasal cavity, esophagus, stomach, duodenum, rectum, large intestine, small intestine, anus, and colon photographed with an endoscope. By inputting the medical image data of these areas into the machine model 131, an analysis result indicating, for example, an area of interest including at least one of inflammation, polyps, and cancer may be output.
  • the machine model 131 may be, for example, a model trained based on a first learning image including an image with an area of interest and first teacher data indicating the presence of the area of interest, and a second learning image including an image without an area of interest and second teacher data indicating the absence of the area of interest.
  • the first teacher data may include information indicating the degree of inflammation (degree of inflammation) or malignancy (degree of malignancy) of the area of interest.
  • the analysis result may be, for example, a display surrounding the area of interest, a display indicating the area of interest, or a display in which a color is superimposed on the area of interest.
  • derivation basis information showing the basis on which the analysis results were derived may also be displayed.
  • the image data 301 may be, for example, image data of the subject's eyes, skin, etc., captured by a digital camera.
  • an analysis result indicating the sign of interest may be output.
  • the sign of interest may include, for example, a sign indicating a disease including at least one of glaucoma, cataracts, age-related macular degeneration, conjunctivitis, styes, retinopathy, and blepharitis in the case of the eyes.
  • the sign of interest may include, for example, a sign including skin cancer, urticaria, atopic dermatitis, and herpes in the case of the skin.
  • the analysis result may be a display that surrounds these signs of interest, a display that indicates the signs of interest, a display that superimposes a color on the signs of interest, or a display that displays the name of the disease.
  • the machine model 131 for example, a model that has been trained based on a first learning image having an image including these parts of interest and first teacher data indicating the presence of the sign of interest, and a second learning image including an image without the sign of interest and second teacher data indicating the absence of the sign of interest can be used.
  • derivation basis information showing the basis on which the analysis results were derived may also be displayed.
  • FIG. 3 is a flowchart showing the flow of processing of analysis method S1 executed by control unit 16. As shown in the figure, analysis method S1 includes steps S11 to S14.
  • the communication control unit 11 displays on a web page via the communication unit 15 a browser image 200, which is an input screen for inputting acquired information 172 including image data 301 to be analyzed, patient identification information 320, and attribute information 303 (S11).
  • the acquisition unit 12 acquires encrypted patient information 302, which is the encrypted image data 301, attribute information 303, and patient identification information 320 contained in the acquisition information 172 input to the browser image 200 and transmitted from the user terminal 20 (S11).
  • the analysis unit 13 analyzes the image data 301 sent from the acquisition unit 12 using the machine model 131 (S13).
  • the communication control unit 11 transmits the analysis result data 173 analyzed by the analysis unit 13, the encrypted patient information 302, and the attribute information 303 to the user terminal 20 via the communication unit 15 (S14).
  • (Configuration of analysis system 70A) 4 is a block diagram showing a configuration of an analysis system 70A according to the second embodiment of the present disclosure.
  • the control unit 16 of the analysis device 1A includes a communication control unit 11, an acquisition unit 12, an analysis unit 13, a generation unit 18, and a determination unit 19.
  • the communication control unit 11, the acquisition unit 12, and the analysis unit 13 have the same functions as those of the respective units described in the first embodiment, and therefore will not be described here.
  • the storage unit 17 stores the image data 301 (medical data), encrypted patient information 302, and attribute information 303 acquired by the acquisition unit 12 in association with each other.
  • the generating unit 18 generates information on the basis for deriving the analysis result analyzed by the analyzing unit 13 (hereinafter referred to as "derivation basis data 311").
  • the derivation basis data 311 means at least one of the reasons why the machine model 131 derived such an analysis result.
  • the derivation basis data 311 may be information that mainly indicates which part of the image was used to derive the analysis result.
  • the derivation basis data 311 is linked to at least the encrypted patient information 302 and stored in the memory unit 17. Specific examples of the functions of the generating unit 18 will be described later.
  • the determination unit 19 determines whether the encrypted patient information 302 acquired by the acquisition unit 12 is the patient identification information 320 that has actually been encrypted. If it is determined that the encrypted patient information 302 is not the encrypted patient information, the acquisition unit 12 may delete the acquired encrypted patient information 302. By providing such a determination unit 19, if the encrypted patient information 302 transmitted from the user terminal 20 is not the encrypted patient identification information 320, the acquired image data 301 is deleted together with the encrypted patient information 302 and the attribute information 303 without being analyzed. Therefore, the acquired information 172 including the image data 301 is not stored inside the analysis device 1A. The determination of whether the information is encrypted may be made, for example, from the extension of the encrypted data.
  • control unit 16 may send a message to the user terminal 20 via the communication control unit 11 requesting that the patient identification information 320 be re-encrypted and sent.
  • the analysis device 1A includes a determination unit 19
  • the determination unit 19 may be included in at least one of the analysis device 1A and a hospital-side server that mediates the transmission of encrypted patient information 302 from the user terminal 20 to the analysis device 1A.
  • both the analysis device 1A and the hospital-side server may be configured to include the determination unit 19.
  • the user terminal 20, which is the source of the encrypted patient information 302 may be configured to include the determination unit 19.
  • FIG. 5 is a diagram showing an example of a browser image 200 displayed on a web page for inputting image data 301 and the like when a user accesses the analysis device 1 via the Internet.
  • the words ⁇ Bone Density Analysis> indicating the subject of image analysis, are displayed at the top of the browser image 200.
  • the words ⁇ Reception> indicating that this is an input-accepting screen, are displayed in the upper left corner of the browser image 200.
  • the words "Place the image to be analyzed here” to prompt the user to input image data to be analyzed, and a frame 205 indicating the area (basis area) into which the image data is pasted (drag-and-dropped).
  • the words “Enter attribute information here” to prompt the user to enter patient identification information 320 and attribute information 303, etc.
  • a box 206 for input A "Send” button 204 is displayed in the lower right corner of the browser image 200 to prompt the user to send the data
  • a "Back” button 207 is displayed in the upper right corner to return to the initial screen of the website.
  • X-ray image data 300 may be a lumbar X-ray image, a chest X-ray image, or an X-ray image taken with a DXA (Dual energy X-ray Absorptiometry) device.
  • DXA Direct energy X-ray Absorptiometry
  • a DXA device that measures bone density using the DXA method, when the bone density of the lumbar vertebrae is measured, X-rays are irradiated from the front of the lumbar vertebrae of the subject.
  • X-rays are irradiated from the front of the proximal femur of the subject.
  • the front of the lumbar vertebrae and “the front of the proximal femur” refer to the direction that correctly faces the imaging site such as the lumbar vertebrae and the proximal femur, and may be the ventral side of the subject's body or the back side of the subject.
  • MD micro densitometry
  • the ultrasound method measures bone density by applying ultrasound to bones such as the lumbar vertebrae, femur, heel, or tibia.
  • the image data does not have to be X-ray images, as long as it is an image that contains bone information.
  • it can be estimated from MRI (magnetic resonance imaging) images, CT (computed tomography) images, PET images, and ultrasound images.
  • the subject for which an estimation is made regarding a bone condition is a human (i.e., a "subject") is given as an example for explanation, but the subject is not limited to humans.
  • the subject for which an estimation is made regarding a bone condition may also be, for example, a mammal other than a human, such as an equine, feline, canine, bovine, or porcine animal.
  • this disclosure also includes embodiments in which "subject” is replaced with "animal” if the embodiment is applicable to these animals.
  • the communication control unit 11 may display a browser image 200 on a web page and transmit an encrypted application via the communication unit 15.
  • the user inputs image data 301, patient identification information 320, and attribute information 303 to be analyzed and clicks the "Send" button 204, the patient identification information 320 is encrypted, and the encrypted patient information 302 is transmitted to the analysis device 1A together with the image data 301 and attribute information 303. Therefore, the user does not need to perform encryption processing of the patient identification information 320.
  • the encryption processing may be performed after the user clicks the "Send" button 204, rather than when the user clicks it.
  • the encryption method may be a known method, and is not limited to this. For example, it may be an encryption method that combines a public key and a private key.
  • the encryption may be a method that cannot be decrypted even by the operator of analysis system 70A. This reduces the risk that the combination of image data 301 and patient identification information 320 will be leaked to the operator, even if a user requests image analysis from the operator of analysis system 70A.
  • the acquisition unit 12 acquires the X-ray image data 300, encrypted patient information 302, and attribute information 303 sent from the user terminal 20, and transmits them to the analysis unit 13.
  • the analysis unit 13 inputs the X-ray image data 300 sent from the acquisition unit 12 to the machine model 131, processes the output data from the machine model 131 as necessary, and generates analysis result data 173.
  • the generated analysis result data 173 is linked to at least the encrypted patient information 302 and attribute information 303, and stored in the memory unit 17.
  • the communication control unit 11 acquires the analysis result data 173 linked to the encrypted patient information 302 and attribute information 303 from the storage unit 17, and transmits it to the user terminal 20 via the communication unit 15.
  • the encrypted patient information 302 is automatically decrypted and the patient identification information 320 is generated.
  • the encrypted patient information 302 may be decrypted when the analysis result data 173 is transmitted, or the user may decrypt it themselves.
  • FIG. 7 is a diagram showing an example of a browser image 400 displayed on the screen of the user terminal 20 that has received the analysis result data 173.
  • the words ⁇ Bone Density Analysis> which indicate the contents of the image analysis, are displayed at the top.
  • the words ⁇ Analysis Result> which indicates that the screen displays the analysis result, are displayed. Below that, patient information may be displayed. Below that, the words "Your bone density is ⁇ / cm2 " are displayed. In a box 401, an estimated bone density value is displayed. Further below that, the words "Compared to young people, it is ⁇ %" may be displayed.
  • the ratio to the bone density average of young adults (YAM, Young Adult Mean) is displayed. Further below that, the words “Judgment” and words such as “Bone mass reduction” may be displayed in a box 403. For example, if the ratio to YAM is below 80%, it is determined to be “bone mass reduction", and if it is below 70%, it is determined to be possible "osteoporosis”.
  • the browser image 400 may have an "Exit” button 404 and a "Back” button 405.
  • the "Back” button 405 may be, for example, a button for returning to a previous screen, a button for returning to a home screen, or a button for returning to a specified screen.
  • the generating unit 18 generates derivation basis data 311 of the analysis result analyzed by the analyzing unit 13.
  • the communication control unit 11 may transmit the derivation basis data 311 generated by the generating unit 18 to the user terminal 20.
  • the analysis result data 173 is based on the output from the machine model 131, but the output from the machine model 131 does not include the analysis process. Therefore, it is generally not possible to determine the reliability of the output from the machine model 131 by looking at only the output. Therefore, by transmitting the analysis result data 173 including the derivation basis data 311 to the user terminal 20, the user can improve their confidence in the analysis result.
  • the derivation basis data 311 may be processed image data 310 (basis image data) in which new information has been added to the image data 301 input to the machine model 131.
  • the new information may be information indicating an area in the image data 301 that served as the main basis for deriving the analysis result.
  • the processed image may be an image in which information indicating the area that served as the main basis has been added to the input image data.
  • Information indicating an area is information that indicates the range of the area, such as coloring or framing. In image analysis, a certain area of an image is often the main basis for estimation, so such information indicating the area allows the user to confirm the area that served as the basis for the estimation.
  • the edited image data 310 does not need to include all of the information in the image data 301 input by the user.
  • the edited image data 310 may be image data that has been cropped from the input image data 301, or may be an image with a lower resolution than the input image data 301. In this way, the amount of data to be sent and received can be reduced.
  • FIG. 8 is a diagram showing an example of a browser image 500 that transmits derived basis data 311 to the user terminal 20 in addition to the analysis result data 173.
  • derived basis data 311 processed image data 502 including area 503 is added to the analysis result data 173 shown in FIG. 7.
  • browser image 500 displays processed image data 502, which is input image data (X-ray image data 300 in FIG. 6) with a rectangular area 503 added and processed, together with analysis result 501.
  • analysis result 501 derived by machine model 131 that analyzes image data
  • processed image data 502 including derivation basis data 311 are displayed on one screen.
  • Browser image 500 may also display a "Back" button 506, an "Exit” button 507, and a "Future Prediction” button 505. The role of "Future Prediction” button 505 will be described later.
  • FIG. 9 is an enlarged schematic diagram of the image of area 503 in FIG. 8.
  • Area 503 includes four lumbar vertebrae, indicated as L1 to L4. This indicates that lumbar vertebrae L1 to L4 are the area on which the analysis results are based.
  • the bone density of lumbar vertebrae L1 to L4 is related to the average bone density of the entire body.
  • the framed area 503 indicates that the analysis results of the mechanical model 131 are derived based on the bone density of these lumbar vertebrae L1 to L4.
  • the region on which the analysis result is based may include a segmented region. Segmentation is the division of an image into several regions. Segmentation is performed to reduce the amount of analysis processing of the mechanical model 131. That is, the mechanical model 131 may analyze only the segmented region.
  • the segmented region may be set to any range. The segmented region may be, for example, rectangular, square, or circular. When the segmented region is a square, the amount of analysis processing of the mechanical model 131 can be reduced.
  • a range including the lumbar vertebrae L1 to L4 is segmented.
  • the size of the segmentation region may change depending on the size of the lumbar vertebrae L1 to L4 in the image.
  • the segmentation region may be set, for example, slightly larger than the lumbar vertebrae L1 to L4 in the arrangement direction of the lumbar vertebrae L1 to L4, or may be set so as to overlap with the sides of the lumbar vertebrae L1 to L4.
  • the segmentation region may always have a predetermined dimension, or the dimension may be set according to the medical image.
  • the segmentation region may be determined by identifying the positions of the lumbar vertebrae L1 to L4, setting the lengths of the lumbar vertebrae L1 to L4 in the arrangement direction, and then setting the lengths of the lumbar vertebrae L1 to L4 in the vertical direction. Segmentation may be performed by the machine model 131.
  • the machine model 131 may learn images with annotations of the analysis region in order to perform segmentation.
  • the generating unit 18 may also generate a heat map of the area on which the analysis result is based.
  • the outer edge of the heat map indicates the segmentation area.
  • a heat map is a method of expressing the magnitude of bone density with an arbitrary color intensity.
  • the generating unit 18 may generate a heat map that indicates the degree of attention.
  • the generating unit 18 may generate a heat map that indicates the numerical value of bone density.
  • the generating unit 18 may generate a heat map that indicates the possibility (probability) of a fracture.
  • the image used for the heat map may be a still image or a video. By showing it as a video, for example, by fading various heat maps in order, it becomes easier to visually recognize the relationship between each heat map.
  • the analysis result is a bone density heat map that includes areas other than the segmentation area, a part of the segmentation area may be surrounded by a frame.
  • the generating unit 18 may obtain information on the region on which the analysis result is based from the analyzing unit 13. Specifically, the generating unit 18 may obtain the region on which the analysis result is based from the analyzing unit 13, and generate information indicating the region (such as a frame surrounding the region 503). The generating unit 18 may obtain information indicating the degree of attention data, bone density data, or the possibility of fracture within the region from the analyzing unit 13, and generate a heat map. The generating unit 18 may generate any one or more of a heat map indicating the degree of attention data, a heat map indicating bone density data, and a heat map indicating the possibility of fracture.
  • the generating unit 18 may cause the color of the heat map indicating the degree of attention data to be different from the color of the heat map indicating the bone density data, and the color of the heat map indicating the possibility of fracture.
  • the machine model 131 analyzes the X-ray image data 300 using NNM.
  • NNM the image is first divided into small regions, each of which is quantified, and then the multiple regions are pooled to combine them into larger regions, which are then quantified again, repeating this process. Therefore, the machine model 131 may extract regions that have numerical values that affect the processing results (for example, relatively large numerical values) as regions that serve as the basis.
  • processed image data 502 is described in which an area 503 that is the basis of the analysis result is superimposed on the X-ray image data 300 to be analyzed.
  • the processed image data is not limited to this.
  • the analysis device 1 may transmit position information (such as coordinates) of the area that is the basis of the analysis result in the image to be analyzed to the user terminal 20, and cause the user terminal 20 to display the processed image data with the area displayed on the image to be analyzed.
  • a screen like the one shown in Figure 8 can be used when a doctor explains the analysis results to a patient.
  • the analysis result 501 and the processed image data 502 including the derivation basis data 311 are displayed on one screen.
  • “one screen” does not have to be displayed on the screen at the same time.
  • the screen may be displayed by scrolling up and down or left and right.
  • the range displayed by scrolling up and down or left and right is referred to as "one screen”.
  • FIG. 10 is a schematic diagram showing an example of a browser image 700 that displays the patient's current possibility of fracture, estimated from image data analyzed by the analysis unit 13, and the possibility of the patient having a fracture three years from now.
  • the future time is not limited to three years from now, and may be any time (e.g., X years from now). This is displayed by clicking the "Future prediction" button 505 at the bottom right of the browser image 500 shown in FIG. 8.
  • the words ⁇ Bone Density Analysis> indicating the subject of image analysis, are displayed at the top of browser image 700.
  • the words ⁇ Future Prediction> are displayed in the upper left corner of browser image 700, indicating that this is a screen for displaying future predictions. Below that, patient information may be displayed. Below that, the words "Your femur fracture probability is ⁇ %" are displayed. Box 701 displays the currently estimated fracture probability. Further below that, the words "Your femur fracture probability in 3 years is ⁇ %" may be displayed. Box 702 displays the predicted fracture probability in 3 years' time. Information regarding the basis for the estimation or prediction may also be displayed in this image.
  • This type of screen can be used by doctors when explaining to patients the current and future possibility of fractures. If information on the basis of estimation or prediction is also displayed, it can be more persuasive to patients.
  • the machine model 131 is a model that estimates the state of bones, and the input image data is an image that includes bones, and outputs an estimation result regarding the state of the bone as an analysis result.
  • the machine model 131 is a trained model that has been trained to output an estimation result or a calculation result regarding the state of bones, such as bone density, a relative comparison of bone density, the presence or absence of a fracture, and the possibility of a fracture, from an X-ray image of a bone.
  • the bone density may be a calculated bone density of a bone part included in the image data, or may be an average bone density of the whole body estimated from the image data.
  • a known method can be used to calculate bone density from an image.
  • the relative comparison of bone density is the ratio of the estimated bone density to the YAM.
  • the presence or absence of a fracture is information indicating whether or not there is a fracture in the input image data.
  • the possibility of a fracture is the possibility that a bone in a specific part (for example, the femoral neck) will be fractured.
  • the estimation result regarding the state of the bone may be an estimation result at the time when the image is taken, or may be a prediction at a time when a predetermined period has passed since that time.
  • the mechanical model 131 may output as an estimation result at least one of the following: bone density estimated at the time the image data was captured, bone density predicted when a specified period of time has passed since the image data was captured, fracture site and possibility estimated at the time the image data was captured, and fracture site and possibility predicted when a specified period of time has passed since the image data was captured.
  • the learning of the machine model 131 may be performed using an X-ray image of a bone with a specified bone density as training data.
  • Learning to estimate the possibility of fracture may be performed using an X-ray image of a bone and data on whether the patient subsequently fractured within a specified period as training data. Relative comparison of bone density does not need to be learned, and is obtained by dividing the estimated bone density by YAM.
  • learning of future prediction may be performed using an X-ray image of a bone with a specified bone density and data on how much bone density the patient had after a specified period or whether the patient subsequently fractured as training data.
  • the mechanical model 131 has been described by taking as an example a mechanical model that has been trained to estimate bone conditions such as bone density.
  • the input image data is an X-ray image of the bone
  • the output is an estimation result regarding the bone condition.
  • the mechanical model 131 is not limited to such a model.
  • the mechanical model 131 may be a cellular pathology analysis model.
  • the input image data may be a microscopic image of a cell, and the output may be the presence or absence of a pathological mutation in the cell.
  • the image data may be an X-ray image, a CT image, a mammography image, or the like, and the output may be the presence or absence of cancer.
  • the above-described configuration of the analysis system 70A according to the second embodiment makes it possible to provide the user with the derivation basis data 311 together with the analysis result data 173. Therefore, in addition to the effects of the analysis system 70 according to the first embodiment, it is possible to achieve the effect of improving the user's confidence in the analysis results. Furthermore, it is possible to achieve the effect of making it easier for the patient to understand the analysis results when explaining them to the patient.
  • Analysis method S2 includes steps S21 to S24. Steps S21 to S23 are the same as steps S11 to S13 described in the analysis method S1 described in the first embodiment.
  • step S24 the communication control unit 11 transmits the analysis result data 173 analyzed by the analysis unit 13, the derivation basis data 311, and the encrypted patient information 302 to the user terminal 20 (not shown).
  • the derivation basis data 311 can be provided to the user together with the analysis result data 173. Therefore, in addition to the effect of the analysis method S1 according to the first embodiment, the effect of improving the user's confidence in the analysis result can be obtained. Furthermore, the effect of making it easier for the patient to understand the analysis result when explaining it to the patient can be obtained.
  • the functions of analysis systems 70, 70A are programs for causing a computer to function as the system, and can be realized by programs for causing a computer to function as each part of the system.
  • the system includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program.
  • the program is executed by the control device and storage device, thereby realizing each of the functions described in each of the above embodiments.
  • 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.
  • each of the above-mentioned parts can be realized by a logic circuit.
  • the scope of this disclosure also includes an integrated circuit in which a logic circuit that functions as each of the above-mentioned parts is formed.
  • the analysis device comprises an acquisition unit that acquires medical data of a subject and second identification information, which is encrypted first identification information of the subject, from a user terminal, an analysis unit that analyzes the acquired medical data, and a transmission unit that transmits the analysis results analyzed by the analysis unit and the second identification information to the user terminal.
  • the analysis device may transmit the analysis result and the second identification information to the user terminal that decodes the second identification information.
  • An analysis device is based on the first or second aspect, wherein the user terminal is provided with an application that performs decryption, and the application has a function of performing the encryption.
  • the analysis device may be configured in such a way that, in the above-mentioned aspect 3, the application body that performs the decryption is transmitted to the user terminal.
  • the second identification information may not be displayed on the user terminal.
  • An analysis device in any one of aspects 1 to 5 above, further includes a determination unit that determines whether the acquired second identification information is the first identification information encrypted using a predetermined method, and the acquisition unit may delete the acquired second identification information if it is determined that the second identification information is not encrypted using a predetermined method.
  • the medical data may be image data.
  • An analysis device is any one of aspects 1 to 7 above, wherein the medical data is image data of the bones of the subject, and the analysis unit may output an estimated result regarding the condition of the bones of the subject as the analysis result.
  • An analysis device in any one of aspects 1 to 8 above, may further include a generation unit that generates basis information regarding the basis for deriving the analysis result, and the transmission unit may include the basis information in the analysis result and transmit it to the user terminal.
  • the medical data is image data
  • the basis information may be basis image data obtained by adding information to the medical data indicating the basis area that was the basis for deriving the analysis result.
  • An analysis device may be such that, in the tenth aspect, the basis image data is image data showing the basis region on the image data.
  • the analysis unit may output as an estimation result at least one of the bone density of the subject's bone at the time the image data was captured, the bone density of the subject's bone at a predetermined time after the medical image data was captured, the site where a fracture is estimated at the time the medical image data was captured and the possibility of the fracture occurring, and the site where a fracture is estimated to occur within a predetermined time after the medical image data was captured and the possibility of the fracture occurring.
  • An analysis method includes at least one processor acquiring medical data of a subject and second identification information of the subject, which is encrypted from a user terminal, analyzing the acquired medical data, and transmitting analysis results and the second identification information to the user terminal.
  • the analysis program of aspect 14 of the present disclosure is an analysis program for causing a computer to function as the analysis device described in any one of aspects 1 to 12 above, and is a computer program for causing a computer to function as the acquisition unit, the analysis unit, and the transmission unit.
  • a recording medium according to aspect 15 of the present disclosure is a computer-readable non-transitory recording medium having the analysis program according to aspect 14 recorded therein.

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Citations (5)

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JP2004290240A (ja) * 2003-03-25 2004-10-21 New Industry Research Organization 遺伝子検査システム
JP2005518832A (ja) * 2002-02-27 2005-06-30 イメージング セラピューティクス，インコーポレーテッド Ｘ線画像の定量解析の方法と装置
JP2019057822A (ja) * 2017-09-21 2019-04-11 三菱スペース・ソフトウエア株式会社 医療データ検索システム、医療データ検索方法および医療データ検索プログラム
WO2020027228A1 (ja) * 2018-07-31 2020-02-06 株式会社Ｌｉｌｙ ＭｅｄＴｅｃｈ 診断支援システム及び診断支援方法
WO2020066076A1 (ja) 2018-09-28 2020-04-02 オリンパス株式会社 医療システム、ネットワーク装置、医療装置、及び検査情報処理方法

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Publication number Priority date Publication date Assignee Title
JP2005518832A (ja) * 2002-02-27 2005-06-30 イメージング セラピューティクス，インコーポレーテッド Ｘ線画像の定量解析の方法と装置
JP2004290240A (ja) * 2003-03-25 2004-10-21 New Industry Research Organization 遺伝子検査システム
JP2019057822A (ja) * 2017-09-21 2019-04-11 三菱スペース・ソフトウエア株式会社 医療データ検索システム、医療データ検索方法および医療データ検索プログラム
WO2020027228A1 (ja) * 2018-07-31 2020-02-06 株式会社Ｌｉｌｙ ＭｅｄＴｅｃｈ 診断支援システム及び診断支援方法
WO2020066076A1 (ja) 2018-09-28 2020-04-02 オリンパス株式会社 医療システム、ネットワーク装置、医療装置、及び検査情報処理方法

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