Analysis system, analysis method, analysis program, and recording medium

Abstract

Provided is a system with which it is possible to analyze medical data while keeping personal information secret. An analysis device is provided with an acquisition unit that acquires unencrypted medical data and second identification information obtained by encrypting the first identification information of a subject from a user terminal, an analysis unit that analyzes the acquired medical data, and a transmission unit that transmits the analysis result analyzed by the analysis unit and second identification information to the user terminal.

Description

[[Technical Field]]

[0001] The present disclosure relates to an analysis apparatus, an analysis method, an analysis program, and a recording medium. [[Background Art]]

[0002] For example, Patent Document 1 discloses a medical system including a medical apparatus that observes a subject and a network apparatus capable of communicating with the medical apparatus. [[Prior Art Documents]] [[Patent Documents]]

[0003] [[Patent Document 1]] WO2020 / 066076 A1 [[Summary of the Invention]]

[0004] An analysis apparatus according to an aspect of the present disclosure comprises: an acquisition unit that acquires, from a user terminal, medical data of a subject and second identification information obtained by encrypting first identification information of the subject; an analysis unit that analyzes the acquired medical data; and a transmission unit that transmits an analysis result obtained by the analysis unit and the second identification information to the user terminal.

[0005] An analysis method according to an aspect of the present disclosure includes, performed by at least one processor: acquiring, from a user terminal, medical data of a subject and second identification information obtained by encrypting first identification information of the subject; analyzing the acquired medical data; and transmitting an analysis result and the second identification information to the user terminal.

[0006] The analysis apparatus according to each aspect of the present disclosure may be implemented by a computer. In this case, a control program for the analysis system that implements the analysis system on a computer by causing the computer to operate as each unit (software element) included in the analysis system, and a computer-readable non-transitory recording medium having the same recorded therein also fall within the scope of the present disclosure. [Brief explanation of the drawing]

[0007] [Figure 1] This is a block diagram showing the configuration of the analysis system according to Embodiment 1 of this disclosure. [Figure 2] This is a schematic diagram showing a portion of the data flow transmitted and received between the analysis device and the user terminal. [Figure 3] This is a flowchart showing the processing flow of the analysis method executed by the control unit. [Figure 4] This is a block diagram showing the configuration of the analysis system according to Embodiment 2 of this disclosure. [Figure 5] This is a diagram showing an example of a browser image. [Figure 6] This figure shows an example of X-ray image data for which bone density analysis is requested. [Figure 7] This is a schematic diagram showing an example of a browser image of the analysis results transmitted by the transmitting unit. [Figure 8] This figure shows an example of a browser image with derivation data added in addition to the analysis result data. [Figure 9] Figure 8 is a magnified schematic diagram of the image of the region shown. [Figure 10] This figure shows an example of a browser image that displays the current and future risk of fractures in patients. [Modes for carrying out the invention]

[0008] [Embodiment 1] (Configuration of analysis system 70) Hereinafter, one embodiment of the present disclosure will be described in detail with reference to the drawings. Figure 1 is a block diagram showing the configuration of an analysis system 70 according to Embodiment 1 of the present disclosure. The analysis system 70 can be used as a system for remotely analyzing images. The analysis system 70 comprises, for example, an analysis device 1 for analyzing medical data and a user terminal 20. In the analysis system 70, the analysis device 1 and the user terminal 20 are connected so as to be able to communicate via the Internet.

[0009] The analysis system 70 receives image analysis requests from user terminals 20 via an information and communication network such as the Internet. The analysis device 1 is a system that analyzes the medical data to be analyzed transmitted from user terminals 20 and returns the analysis results to user terminals 20 via the Internet. A user can be anyone who requests the analysis system 70 to analyze medical data. Users are, for example, medical professionals such as doctors, medical technologists, and nurses, but are not limited to these.

[0010] This disclosure uses the example of medical data being image data 301. However, medical data is not limited to image data 301; for example, blood data, blood flow data, or gait data may be used as medical data. The analysis system 70 may, for example, analyze blood data to perform a patient's motion analysis.

[0011] There may be multiple user terminals 20; for example, each medical institution may have user terminals 201, 202, and 203. Users access the analysis device 1, for example, via the internet, and request analysis of image data 301 from a screen displayed on a web page. If the same image data 301 is requested for analysis 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 compliant with DICOM (Digital Imaging and Communications in Medicine), for example.

[0012] User terminal 20 may be, for example, a stationary personal computer equipped with communication functions, or a mobile terminal such as a tablet. Since there may be multiple users, multiple user terminals are shown as 201, 202, 203, etc., but unless otherwise specified, the terminal in question will be referred to as user terminal 20, representing all terminals.

[0013] The analysis device 1 comprises: a control unit 16 that integrally controls each part of the analysis device 1; a storage unit 17 that stores various types of data used by the control unit 16; and a communication unit 15 for communicating with other devices.

[0014] The control unit 16 comprises: a communication control unit 11 (transmission unit) that controls the communication unit 15; an acquisition unit 12 that acquires data transmitted from a user terminal 20 via the communication control unit 11; and an analysis unit 13 that analyzes image data 301.

[0015] The control unit 16 includes at least one processor and at least one memory. The processor can be configured using, for example, at least one general-purpose processor such as an MPU (Micro Processing Unit) or a CPU (Central Processing Unit). The memory may comprise a plurality of types of memory such as ROM (Read Only Memory) and RAM (Random Access Memory). As an example, the processor realizes the function as the control unit 16 by developing various control programs recorded in the ROM of the memory into the RAM and executing the programs. Further, the processor may include a processor configured of an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), a PLD (Programmable Logic Device), or the like.

[0016] The storage unit 17 stores a browser image 200, acquired information 172, and analysis result data 173 (analysis result) which will be described later.

[0017] 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. When a user accesses the analysis device 1 via the Internet to request analysis of image data 301, the communication control unit 11 acquires, via the communication unit 15, a browser image 200 serving as an input screen from the storage unit 17 and displays the browser image 200 on a web page. The user inputs acquisition information 172 including image data 301 to be analyzed, patient identification information 320 (first identification information), and attribute information 303 into the browser image 200 and transmits the acquisition information 172 to the analysis device 1.

[0018] The user can transmit the acquisition information 172 to the analysis device 1 by inputting the acquisition information 172 into the browser image 200 and clicking a "transmit" button 204 described later. The input method for the user is not limited to this; for example, input may be performed by drag-and-drop, or input may be performed 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 only the minimum attribute information 303 embedded in the DICOM file may be transmitted. This makes it possible to further ensure anonymity. When the acquisition information 172 is transmitted to the analysis device 1, the image data 301 and the attribute information 303 are not encrypted, but the patient identification information 320 is encrypted and transmitted as described in detail later. The present invention is not limited to this, and although there is no particular need to encrypt the image data 301, the image data 301 may also be encrypted for transmission. A specific example of the browser image 200 will be described later.

[0019] The data type of image data 301 can be any data that can be transmitted over the internet. The content of the image is not particularly limited, but depending on the application of the analysis system 70, it may be, for example, medical X-ray images, micrographs, CT (Computer Tomography) images, MRI (Magnetic Resonance Imaging) images, ultrasound images, PET images, etc. For example, if the analysis system 70 estimates bone density, the type of image data 301 may be X-ray images, CT images, MRI images, etc. of bone. Estimation may include calculations. Also, if the analysis system 70 performs pathological determination of cells, the type of image data 301 may be micrograph data of cells, etc. Bone density is the bone mineral density per unit area (g / cm³). 2 ), bone mineral density per unit volume (g / cm³) 3 Bone density may be expressed by at least one of the following: YAM (%), T-score, and Z-score. YAM (%) is an abbreviation for "Young Adult Mean" and is sometimes called the average percentage for young adults. For example, bone density is the bone mineral density per unit area (g / cm³). 2 The values ​​may be expressed as %) and YAM(%). Bone density may be an index defined in the guidelines or an independent index.

[0020] Patient identification information 320 refers to information that indicates whose image data 301 is being analyzed, and may be an ID number, or information that includes age, address, name, and image identification number. The ID number can be any combination of alphanumeric characters, for example. The image identification number may be a number assigned by the imaging device at the time of imaging.

[0021] As described above, patient identification information 320 is encrypted when transmitted to, for example, the analysis device 1, and encrypted patient information 302 (second identification information) is generated. This allows image data 301 to be transmitted and analyzed while concealing personal information.

[0022] The encryption process may be performed by the user terminal 20 that transmits the patient identification information 320, or by the receiving analysis device 1. The encryption method may be a publicly known method and is not limited thereto.

[0023] For example, when the communication control unit 11 receives access from the user terminal 20 via the internet, it may send an encryption application to the user terminal 20. This encryption application encrypts the patient identification information 320 entered into the browser image 200 and decrypts the encrypted patient information 302 that is sent to the user terminal 20 after the image data 301 has been analyzed. With this configuration, the patient identification information 320 can be automatically encrypted and decrypted without the user having to do so themselves. Furthermore, the encrypted second identification information does not need to be displayed on the user terminal 20. The user can use the system without being aware that the identification information is encrypted.

[0024] Attribute information 303 includes, for example, the name and address of the hospital from which the image data 301 was taken, the hospital's ID number, and the hospital's email address.

[0025] The acquisition unit 12 acquires encrypted patient information 302, which is 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 it to the analysis unit 13. The acquisition unit 12 links the acquired image data 301, encrypted patient information 302, and attribute information 303 together and stores them in the storage unit 17.

[0026] The analysis unit 13 analyzes the image data 301 acquired from the acquisition unit 12 using a machine model 131. As shown in Figure 1, the analysis unit 13 includes a machine model 131. The machine model 131 is a trained machine model that has been trained to output predetermined information, which is the analysis result, from the input image data 301. Hereinafter, the analysis content based on the output of the machine model 131 will also be referred to as "analysis result derived by the machine model" or simply "analysis result". The data including the analysis result will be referred to as analysis result data 173. The analysis result data 173 is stored in the storage unit 17 in association with encrypted patient information 302 and attribute information 303. The image data 301 may be analyzed in a format compliant with DICOM (Digital Imaging and Communications in Medicine), or it may be analyzed as the state of pixel values ​​and sequence information of an image extracted from a DICOM-compliant file. Alternatively, it may be converted to another image format before analysis. The other image format may be a general-purpose image format such as JPG, BMP, TIF, or PNG. By doing so, machine models created to handle general-purpose image formats can be used directly.

[0027] The machine model 131 is not limited, but may include, for example, a convolutional neural network model (NNM) for image analysis. Specific examples of the machine model 131 and the analysis result data 173 will be described in detail in Embodiment 2.

[0028] The communication control unit 11 links the analysis result data 173, encrypted patient information 302, and attribute information 303 analyzed by the analysis unit 13 together and transmits them to the user terminal 20 via the communication unit 15.

[0029] When the user terminal 20 receives the analysis result data 173, encrypted patient information 302, and attribute information 303, the encrypted patient information 302 is decrypted and patient identification information 320 is generated. At this time, if the analysis device 1 has sent the encryption application to the user terminal 20, the encryption application will automatically decrypt the encrypted patient information 302, so the user does not need to decrypt it. Here, decryption refers to, for example, restoring the encrypted patient information 302 to the original patient identification information 320.

[0030] In this way, the user terminal 20 can receive the decrypted patient identification information 320, analysis result data 173, and attribute information 303. This allows the user terminal 20 to store the analysis result data 173 linked to the patient identification information 320.

[0031] In Figure 1, the analysis device 1 is depicted as being housed in a single enclosure. However, the analysis device 1 does not need to be housed in a single enclosure. For example, some of the components included in the analysis device 1 may be housed in separate devices. Alternatively, some or all of the components included in the analysis device 1 may be housed in the cloud.

[0032] The analysis system 70 having the above configuration can analyze image data 301 requested by users while concealing personal information. Therefore, even when receiving analysis requests from an unspecified number of users, it is possible to analyze each user's image data 301 while protecting personal information.

[0033] Image data 301 may, for example, be medical image data obtained by endoscopy of a subject. More specifically, image data 301 may include medical image data obtained by endoscopy of a part of the subject that includes at least one of the following: nasal cavity, esophagus, stomach, duodenum, rectum, large intestine, small intestine, anus, and colon. By inputting medical image data of these parts into the machine model 131, an analysis result may be output that clearly indicates a site of interest that includes at least one of inflammation, polyps, and cancer. In such a case, the machine model 131 may be a model trained on, for example, a first training image containing an image of the site of interest and first training data indicating the presence of the site of interest, and a second training image containing an image without the site of interest and second training data indicating the absence of the site of interest. The first training data may include information indicating the degree of inflammation (degree of inflammation) or malignancy (degree of malignancy) of the site of interest. The analysis result may, for example, be a display that surrounds the site of interest, a display that points to the site of interest, or a display that superimposes color on the site of interest. The system may also display derivation information that shows the basis for how the analysis results were derived, along with the analysis results themselves.

[0034] Alternatively, the image data 301 may be, for example, image data of the subject's eyes, skin, etc., taken with a digital camera. By inputting the image data 301 of these areas into the machine model 131, for example, an analysis result that clearly indicates the signs of interest may be output. If the signs of interest are of the eyes, for example, signs indicating diseases including at least one of glaucoma, cataracts, age-related macular degeneration, conjunctivitis, stye, retinopathy, and blepharitis may be included. Alternatively, if the signs of interest are of the skin, for example, signs including skin cancer, urticaria, atopic dermatitis, and herpes may be included. The analysis result may be a display that surrounds these signs of interest, a display that points to the signs of interest, a display that superimposes color on the signs of interest, or a display that shows the name of the disease. As the machine model 131, for example, a model trained on a first training image having images including these areas of interest and first training data indicating the presence of the signs of interest, and a second training image including images without signs of interest and second training data indicating the absence of signs of interest can be used. The system may also display derivation information that shows the basis for how the analysis results were derived, along with the analysis results themselves.

[0035] Next, the flow of the analysis method S1 according to this embodiment will be described with reference to the drawings. Figure 3 is a flowchart showing the processing flow of the analysis method S1 executed by the control unit 16. As shown in the figure, the analysis method S1 includes steps S11 to S14.

[0036] When a user accesses the analysis device 1, the communication control unit 11 displays a browser image 200 on a web page via the communication unit 15, 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).

[0037] The acquisition unit 12 acquires encrypted patient information 302, which is encrypted image data 301, attribute information 303, and patient identification information 320 contained in the acquisition information 172 that is input into the browser image 200 and transmitted from the user terminal 20 (S11).

[0038] The analysis unit 13 analyzes the image data 301 sent from the acquisition unit 12 using the machine model 131 (S13).

[0039] The communication control unit 11 transmits the analysis result data 173, encrypted patient information 302, and attribute information 303 analyzed by the analysis unit 13 to the user terminal 20 via the communication unit 15 (S14).

[0040] According to the analysis method S1 described above, image data 301 requested by users can be analyzed while concealing personal information. Therefore, even when receiving analysis requests from a large number of unspecified users, analysis can be performed while protecting personal information.

[0041] [Embodiment 2] Other embodiments of this disclosure are described below. For convenience of explanation, components having the same function as those described in Embodiment 1 above are denoted by the same reference numerals, and their descriptions are not repeated. In this embodiment, the example given is when the analysis system 70A analyzes X-ray images of bone and outputs bone density, relative comparison of bone densities, fracture probability (%), etc.

[0042] (Configuration of analysis system 70A) Figure 4 is a block diagram showing the configuration of the analysis system 70A according to Embodiment 2 of this disclosure. As shown in the figure, 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. Of these, the communication control unit 11, the acquisition unit 12, and the analysis unit 13 have the same functions as those described in Embodiment 1, so their description is omitted here.

[0043] 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, linking them together.

[0044] The generation unit 18 generates information regarding the basis for the derivation of the analysis results analyzed by the analysis 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. For example, the derivation basis data 311 may be information indicating which part of the image was mainly 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 storage unit 17. Specific examples of the functions of the generation unit 18 will be described later.

[0045] (Judgment part 19) The determination unit 19 determines whether the encrypted patient information 302 acquired by the acquisition unit 12 is actually encrypted patient identification information 320. If it is determined that the encrypted patient information 302 is not actually encrypted, 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 encrypted patient identification information 320, the acquired image data 301 is deleted along with the encrypted patient information 302 and 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 or not something is encrypted may be made, for example, from the file extension of the encrypted data.

[0046] If it is determined that the encrypted patient information 302 is not actually encrypted, the 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 encrypted and sent again.

[0047] This disclosure describes a configuration in which the analysis device 1A includes a determination unit 19, but is not limited to this configuration. For example, in the analysis system 70A, the determination unit 19 may be provided in at least one of the analysis device 1A or the hospital server that mediates the transmission of encrypted patient information 302 from the user terminal 20 to the analysis device 1A. In one example, both the analysis device 1A and the hospital server may be configured to include a determination unit 19. Furthermore, the user terminal 20, which is the source of the encrypted patient information 302, may also be configured to include a determination unit 19.

[0048] (Browser image 200) Figure 5 shows the user accessing the analysis device 1 via the internet. A This figure shows an example of a browser image 200 that is displayed on a web page when accessed, for inputting image data 301, etc.

[0049] As shown in the diagram, the browser image 200 displays the text "<Bone Density Analysis>" at the top, indicating the subject of the image analysis. In the upper left of the browser image 200, the text "<Reception>" is displayed, indicating that this is a screen for accepting input. Below that, the text "Please place the image to be analyzed here" prompts the user to input the image data to be analyzed, and a frame 205 indicates the area (evidence area) where the image data can be pasted (drag and drop). Further below, the text "Please enter attribute information here" prompts the user to input patient identification information 320 and attribute information 303, and an input box 206 is displayed. In the lower right of the browser image 200, a "Submit" button 204 prompts the user to submit, and in the upper right, a "Back" button 207 is displayed to return to the initial screen of the website.

[0050] Figure 6 shows an example of image data 301, specifically X-ray image data 300 for which bone density analysis is requested. 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. In a DXA device that measures bone density using the DXA method, when measuring lumbar spine bone density, X-rays are irradiated from the front of the subject's lumbar spine. Similarly, when measuring proximal femur bone density with a DXA device, X-rays are irradiated from the front of the subject's proximal femur. Here, "front of the lumbar spine" and "front of the proximal femur" refer to the direction that correctly faces the imaging site, such as the lumbar spine and proximal femur, and may be the ventral side or the posterior side of the subject's body. In the MD (micro densitometry) method, X-rays are irradiated to the hand. Ultrasonography is a method of measuring bone density by applying ultrasound waves to bones such as the lumbar spine, femur, heel, or tibia. Furthermore, the image data does not have to be X-ray images; any image containing bone information is acceptable. For example, bone density can be estimated from MRI (magnetic resonance imaging) images, CT (computed tomography) images, PET images, and ultrasound images.

[0051] This disclosure uses the example of a case where the subject of estimation regarding bone condition is a human (i.e., "subject"), but the subject is not limited to humans. The subject of estimation regarding bone condition may be a mammal other than a human, such as a horse, feline, canine, bovine, or pig. Furthermore, this disclosure includes embodiments in which "subject" is replaced with "animal," as long as the embodiments are applicable to these animals as well.

[0052] In this embodiment, when a user accesses the analysis device 1A via the Internet, the communication control unit 11 may display a browser image 200 on a web page and send an encryption application via the communication unit 15. When the user enters the 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, along with the image data 301 and attribute information 303, is sent to the analysis device 1A. Therefore, the user does not need to perform the encryption process for the patient identification information 320. The encryption process may be performed after the user clicks the "Send" button 204, rather than when the user clicks it.

[0053] The encryption method can be any known method and is not limited to it. For example, it may be an encryption method that combines a public key and a private key. The encryption may be such that even the operator of the analysis system 70A cannot decrypt it. This reduces the risk that the combination of image data 301 and patient identification information 320 may be leaked to the operator of the analysis system 70A, even if the user requests image analysis from the operator.

[0054] The acquisition unit 12 acquires the X-ray image data 300, encrypted patient information 302, and attribute information 303 transmitted from the user terminal 20, and transmits them to the analysis unit 13.

[0055] The analysis unit 13 inputs the X-ray image data 300 transmitted from the acquisition unit 12 to the machine model 131, processes the output data from the machine model 131 as needed, and generates analysis result data 173. The generated analysis result data 173 is linked to at least encrypted patient information 302 and attribute information 303 and stored in the storage unit 17.

[0056] The communication control unit 11 acquires analysis result data 173 linked to 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.

[0057] When the user terminal 20 receives the analysis result data 173, the encrypted patient information 302 is automatically decrypted and 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.

[0058] As a result, the user terminal 20 can display the analysis result data 173 on its screen along with patient identification information 320, which includes the patient's name or identification number.

[0059] Figure 7 shows an example of a browser image 400 displayed on the screen of a user terminal 20 that has received the analysis result data 173. At the top of the browser image 400, the words "<Bone Density Analysis>" are displayed to indicate the content of the image analysis. At the top left of the browser image 400, the words "<Analysis Results>" are displayed to indicate that this is a screen displaying the analysis results. Below that, patient information may be displayed. Below that, it may say, "Your bone density is □ / cm 2 The text "is" is displayed. Box 401 displays the estimated bone density value. Further below, the text "It is □% compared to young people" may be displayed. Box 402 displays the ratio to the Young Adult Mean (YAM) bone density. Further below, the text "Judgment" and the text "Bone loss" in box 403 may be displayed. For example, if the ratio to YAM is below 80%, it is judged as "bone loss," and if it is below 70%, it is judged as a possibility of "osteoporosis." Browser image 400 may also have an "Exit" button 404 and a "Back" button 405. The "Back" button 405 may, for example, return to the previous screen, return to the home screen, or return to a specific screen.

[0060] (Generation part 18) Next, the details of the generation unit 18 will be described. In this embodiment, the generation unit 18 generates the derivation basis data 311 of the analysis results analyzed by the analysis unit 13. The communication control unit 11 may transmit the derivation basis data 311 generated by the generation unit 18 to the user terminal 20.

[0061] 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 judge the reliability of the output by looking only at the output from the machine model 131. By sending the analysis result data 173, including the derivation basis data 311, to the user terminal 20, the user can be helped to improve their confidence in the analysis results.

[0062] For example, the derived basis data 311 may be processed image data 310 (basis image data) to which new information has been added to the image data 301 input to the machine model 131. For example, the new information may be information indicating the region that was the main basis for deriving the analysis result within the image data 301. In other words, the processed image may be an image to which information indicating the main basis for the result has been added to the input image data. Information indicating a region is information that shows the extent of the region, such as coloring or a frame. In image analysis, a certain region of the image is often the main basis for estimation, so such information indicating a region allows the user to confirm the region that was the basis for the estimation.

[0063] The processed image data 310 does not need to contain all the information of the user-input image data 301. For example, the processed image data 310 may be a cropped version of the input image data 301, or it may be an image with a lower resolution than the input image data 301. This reduces the amount of data transmitted and received.

[0064] Figure 8 shows an example of a browser image 500 that transmits the derivation basis data 311 in addition to the analysis result data 173 to the user terminal 20. The browser image 500 has the derivation basis data 311 (processed image data 502 including region 503) added to the analysis result data 173 shown in Figure 7.

[0065] Specifically, the browser image 500 displays the analysis result 501 along with the processed image data 502, which is the input image data (X-ray image data 300 in Figure 6) with a rectangular region 503 added. As shown in Figure 8, in this embodiment, the analysis result 501 derived by the machine model 131 that analyzes the image data, and the processed image data 502 including the derivation basis data 311 are displayed on a single screen. The browser image 500 may also display a "Back" button 506, an "Exit" button 507, and a "Future Prediction" button 505. The role of the "Future Prediction" button 505 will be described later.

[0066] Figure 9 is a magnified schematic diagram of the image of region 503 in Figure 8. Region 503 includes the four lumbar vertebrae shown from L1 to L4. This indicates that lumbar vertebrae L1-L4 are the region on which the analysis results were based. In fact, it is known that the bone density of lumbar vertebrae L1-L4 is related to the average bone density of the whole body. That is, the enclosed region 503 indicates that the analysis results of the mechanical model 131 were derived based on the bone density of these lumbar vertebrae L1-L4.

[0067] The region on which the analysis results are based may include a segmented region. Segmentation is the process of dividing an image into several regions. Segmentation is performed to reduce the processing load of the machine model 131. That is, the machine model 131 may analyze only the segmented region. The segmented region can be set to any range. The segmented region may be, for example, a rectangle, a square, or a circle. If the segmented region is a square, the processing load of the machine model 131 can be reduced. When analyzing an X-ray image of the lumbar spine, for example, the range including lumbar vertebrae L1 to L4 is segmented. Therefore, the size of the segmented region may vary depending on the size of lumbar vertebrae L1 to L4 in the image. The segmented region may be set to be slightly larger than lumbar vertebrae L1 to L4 in the direction of the arrangement of lumbar vertebrae L1 to L4, for example, or it may be set to overlap with the edges of lumbar vertebrae L1 to L4. The segmentation region may always have predetermined dimensions, or its dimensions may be set according to the medical image. For example, the segmentation region may identify the positions of lumbar vertebrae L1-L4, set the length of the lumbar vertebrae L1-L4 in the alignment direction, and then set the length of the lumbar vertebrae L1-L4 in the vertical direction. Segmentation may be performed by a machine model 131. The machine model 131 may have learned images with annotations of the analysis region in order to perform segmentation.

[0068] Furthermore, the generation unit 18 may generate a heatmap of the region that formed the basis of the analysis results. In this case, for example, the outer edge of the heatmap would point to the segmentation region. A heatmap is a method of representing the magnitude of bone density with an arbitrary color intensity. For example, the generation unit 18 may generate a heatmap that shows the degree of focus. It may also generate a heatmap that shows the numerical value of bone density. Furthermore, the generation unit 18 may generate a heatmap that shows the possibility (probability) of fracture. The images used for the heatmap may be still images or videos. By showing it as a video, for example, by fading various heatmaps in sequence, it becomes easy to visually recognize the relationships between each heatmap. Also, if the analysis result is a heatmap of bone density that includes areas other than the segmentation region, a portion of the segmentation region may be enclosed in a frame.

[0069] The generation unit 18 may obtain information about the region that formed the basis of the analysis results from the analysis unit 13. Specifically, the generation unit 18 may obtain the region that formed the basis of the analysis results from the analysis unit 13 and generate information indicating that region (such as a border surrounding region 503). The generation unit 18 may obtain data indicating the degree of focus, bone density data, or information indicating the possibility of fracture within the region from the analysis unit 13 and generate a heat map. The generation unit 18 may generate any multiple of the following: a heat map showing the degree of focus data, a heat map showing bone density data, and a heat map showing the possibility of fracture. When displaying the heat map showing the degree of focus data, the heat map showing bone density data, and the heat map showing the possibility of fracture overlaid, the generation unit 18 may make the colors of the heat map showing the degree of focus data, the heat map showing bone density data, and the heat map showing the possibility of fracture different.

[0070] The machine model 131 analyzes X-ray image data 300, for example, using NNM. In NNM, the image is divided into small regions, each is quantified, and multiple regions are pooled to form larger regions, which are then quantified again. This process is repeated. Therefore, the machine model 131 may extract regions that have numerical values ​​that influence the processing results (for example, relatively large numerical values) as the basis regions.

[0071] In the example shown in Figure 8, a processed image data 502 was described in which the region 503 that formed the basis of the analysis result was superimposed on the X-ray image data 300 that was to be analyzed. However, the processed image data is not limited to this. For example, the analysis device 1 may transmit location information (such as coordinates) of the region that formed the basis of the analysis result in the image to be analyzed to the user terminal 20, and the user terminal 20 may display the processed image data with the region superimposed on the image to be analyzed.

[0072] A screen like the one shown in Figure 8 can be used when a doctor explains the analysis results to a patient. In other words, it can not only explain the analysis results to the patient, but also explain which areas of the image data were used as the basis for these results. This not only improves the doctor's confidence in the analysis results, but also makes it easier for the patient to accept the results.

[0073] In the example shown in Figure 8, the analysis results 501 and the processed image data 502, which includes the derivation basis data 311, are displayed on a single screen. However, "a single screen" does not necessarily mean that the two are displayed simultaneously on the screen. For example, the two may be displayed by scrolling the screen up and down or left and right. In other words, the range displayed by scrolling the screen up and down or left and right is referred to as "a single screen."

[0074] Figure 10 is a schematic diagram showing an example of a browser image 700 that displays the current probability of a patient's fracture and the probability of that patient suffering a fracture in three years, as estimated from the image data analyzed by the analysis unit 13. The future timeframe is not limited to three years from now; it can be any time (for example, X years from now). This can be displayed by clicking the "Future Prediction" button 505 in the lower right corner of the browser image 500 shown in Figure 8.

[0075] Browser image 700 displays the text "<Bone Density Analysis>" at the top, indicating the subject of the image analysis. In the upper left of browser image 700, the text "<Future Prediction>" is displayed, indicating that this is a screen showing future predictions. Below that, patient information may be displayed. Below that, the text "Your femoral fracture probability is □%" is displayed. Box 701 displays the currently estimated fracture probability. Further below that, the text "Your femoral fracture probability in 3 years is □%" may be displayed. Box 702 displays the predicted fracture probability in 3 years. In addition, information regarding the basis for estimation or prediction may be displayed in conjunction with this image.

[0076] Such screens can be used by doctors to explain the current and future risk of fractures to patients. Displaying information on the underlying rationale or predictive basis can increase the persuasiveness of the explanation to the patient.

[0077] (Machine Model 131) Next, the machine model 131 will be described. The machine model 131 is a model that makes estimations regarding the state of bone. The input image data is an image containing bone, and the analysis result outputs an estimation result regarding the state of the bone. For example, the machine model 131 is a trained model that has been trained to output estimation results or calculation results regarding the state of bone, such as bone density, relative comparison of bone densities, presence or absence of fracture, and probability of fracture, from X-ray images of bone. The bone density may be the calculated bone density of the bone site included in the image data, or it may be the average bone density of the whole body estimated from the image data. A known method can be used to calculate the bone density from the image. The relative comparison of bone densities is the ratio of the estimated bone density to YAM. The presence or absence of fracture is information indicating whether or not there is a fracture in the input image data. The probability of fracture is the possibility of a fracture in a specific site (e.g., the femoral neck). The estimation result regarding the state of bone may be an estimation result at the time the image was taken, or it may be a prediction at a predetermined period of time after that time.

[0078] The machine model 131 may output at least one of the following as estimation results: bone density estimated at the time image data is acquired, bone density predicted after a predetermined period has elapsed from the time image data is acquired, fracture site and its probability estimated at the time image data is acquired, and fracture site and its probability predicted after a predetermined period has elapsed from the time image data is acquired.

[0079] (Learning method for machine model 131) Next, the learning method for the machine model 131 will be described. For example, learning to estimate bone density may be performed using X-ray images of bones with identified bone density as training data. Learning to estimate the likelihood of fracture may be performed using X-ray images of bones and data on whether the patient subsequently suffered a fracture within a predetermined period as training data. Relative comparison of bone density does not need to be learned; it can be obtained by dividing the estimated bone density by YAM. Furthermore, learning for future prediction may be performed using X-ray images of bones with identified bone density and data on what the patient's bone density was after a predetermined period, or whether or not a fracture occurred, as training data. By including lifestyle habits such as exercise, diet, smoking, and alcohol consumption of the patients used as training data, it is possible to construct a machine model 131 that can estimate or predict with even greater accuracy.

[0080] In Embodiment 2, a machine model 131 trained to estimate bone conditions such as bone density was used as an example. In this case, the input image data is an X-ray image of the bone, and the output is the estimation result regarding the bone condition. However, the machine model 131 is not limited to this type of model. For example, the machine model 131 may be a cell pathology analysis model. In this case, the input image data may be a microscopic image of a cell, and the output may be whether or not the cell has a pathological mutation. Alternatively, the image data may be an X-ray image, a CT image, or a mammography image, and the output may be whether or not cancer is present.

[0081] According to the configuration of the analysis system 70A in Embodiment 2 described above, the derived basis data 311 can be provided to the user together with the analysis result data 173. Therefore, in addition to the effects of the analysis system 70 in Embodiment 1, it has the effect of improving the user's confidence in the analysis results. Furthermore, when explaining the results to a patient, it has the effect of making it easier for the patient to accept the analysis results.

[0082] Next, the flow of the analysis method S2 executed by the control unit 16 according to Embodiment 2 will be described. The 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 Embodiment 1.

[0083] Step S24 is the step in which the communication control unit 11 transmits the analysis result data 173 analyzed by the analysis unit 13, the derived basis data 311, and the encrypted patient information 302 to the user terminal 20 (not shown).

[0084] According to the analysis method S2 described above, the derived basis data 311 can be provided to the user together with the analysis result data 173. Therefore, in addition to the effects of the analysis method S1 according to Embodiment 1, it has the effect of improving the user's confidence in the analysis results. Furthermore, when explaining the results to patients, it has the effect of making it easier for patients to accept the analysis results.

[0085] [Examples of implementation using software] The functions of the analysis systems 70 and 70A (hereinafter referred to as "the System") are programs that cause a computer to function as the System, and these functions can be realized by programs that cause a computer to function as each part of the System.

[0086] In this case, the system includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., memory) as hardware for executing the program. By executing the program using this control device and storage device, the functions described in each of the embodiments are realized.

[0087] The above program may be recorded on one or more computer-readable recording media, not temporary ones. These recording media may or may not be provided by the above device. In the latter case, the program may be supplied to the above device via any wired or wireless transmission medium.

[0088] Furthermore, some or all of the functions of the above-mentioned parts can also be realized by logic circuits. For example, an integrated circuit in which logic circuits functioning as the above-mentioned parts are formed is also included in the scope of this disclosure. In addition, it is also possible to realize the functions of the above-mentioned parts by, for example, a quantum computer.

[0089] The inventions described in this disclosure have been explained above based on the drawings and embodiments. However, the inventions described in this disclosure are not limited to the embodiments described above. That is, the inventions described in this disclosure can be modified in various ways within the scope shown in this disclosure, and embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the inventions described in this disclosure. In other words, it should be noted that it is easy for those skilled in the art to make various modifications or alterations based on this disclosure. Furthermore, it should be noted that these modifications or alterations are included in the scope of this disclosure.

[0090] (summary) (Aspect 1) The analysis device according to Embodiment 1 of this disclosure comprises: an acquisition unit that acquires the medical data of a subject and second identification information obtained by encrypting 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 analyzed by the analysis unit and the second identification information to the user terminal.

[0091] (Aspect 2) The analysis device according to Embodiment 2 of this disclosure may transmit the analysis results and the second identification information to the user terminal that decodes the second identification information in Embodiment 1.

[0092] (Aspect 3) In the analysis device according to aspect 3 of this disclosure, in aspect 1 or 2 above, the user terminal may include an application for decryption, and the application may also include a function for encryption.

[0093] (Aspect 4) The analysis device according to aspect 4 of this disclosure may transmit the application body that performs the decoding to the user terminal in aspect 3 above.

[0094] (Appendix 5) In the analysis device according to aspect 5 of this disclosure, in any one of aspects 1 to 4 above, the second identification information does not need to be displayed on the user terminal.

[0095] (Aspect 6) The analysis device according to embodiment 6 of the present disclosure further comprises a determination unit that determines whether the acquired second identification information is encrypted using a predetermined method, in any one of embodiments 1 to 5, and if the acquisition unit determines that the second identification information is not encrypted using a predetermined method, it may delete the acquired second identification information.

[0096] (Aspect 7) In the analysis apparatus according to aspect 7 of this disclosure, the medical data may be image data in any one of aspects 1 to 6 described above.

[0097] (Pattern 8) The analysis device according to embodiment 8 of the present disclosure is, in any one of embodiments 1 to 7 above, wherein the medical data is image data of the subject's bones, and the analysis unit may output an estimated result regarding the condition of the subject's bones as the analysis result.

[0098] (Aspect 9) The analysis device according to aspect 9 of the present disclosure further comprises a generation unit that generates basis information relating to the basis for deriving the analysis result in any one of aspects 1 to 8, and the transmission unit may transmit the basis information included in the analysis result to the user terminal.

[0099] (Aspect 10) In the analysis apparatus according to aspect 10 of this disclosure, in aspect 9 above, the medical data may be image data, and the basis information may be basis image data in which the medical data is supplemented with information indicating the basis region that served as the basis for deriving the analysis result.

[0100] (Aspect 11) In the analysis apparatus according to embodiment 11 of this disclosure, the basis image data in embodiment 10 may be image data in which the basis region is shown on the image data.

[0101] (Aspect 12) The analysis device according to embodiment 12 of this disclosure, in embodiment 8, wherein the analysis unit measures the bone density of the subject's bone at the time the image data was captured, and the prior Recorded picture The bone density of the subject's bones at a predetermined time after the time the image data was acquired, and before Recorded picture The location where the fracture is suspected at the time the image data was acquired, the possibility of the fracture occurring, and the preceding Recorded picture The system may output at least one of the following as an estimation result: the location where a fracture is estimated to occur within a predetermined period from the time the image data was acquired, and the probability of such a fracture occurring.

[0102] (Aspect 13) An analysis method according to aspect 13 of this disclosure includes at least one processor acquiring medical data of a subject and second identification information obtained by encrypting the subject's first identification information from a user terminal, analyzing the acquired medical data, and transmitting the analysis results and the second identification information to the user terminal.

[0103] (Aspect 14) The analysis program according to aspect 14 of this disclosure is an analysis program for causing a computer to function as an analysis device described in any one of aspects 1 to 12, and is a computer program for causing the computer to function as the acquisition unit, the analysis unit, and the transmission unit.

[0104] (Aspect 15) The recording medium according to aspect 15 of this disclosure is a computer-readable non-temporary recording medium on which the analysis program described in aspect 14 is recorded. [Explanation of Symbols]

[0105] 1, 1A analysis device 11. Communication Control Unit (Transmitter Unit) 12 Acquisition Department 13 Analysis Department 18 Generation part 19 Judgment section 20 User Terminals 131 Machine Models 173 Analysis Results Data (Analysis Results) 301 Image data (medical image data) 302 Encrypted patient information (second identification information) 310 Processed image data (basis image data) 311 Derivation basis data (information regarding the basis for deriving the analysis results) 320 Patient Identification Information (First Identification Information) 503 Domain (Rational Domain)

Claims

1. Equipped with a user terminal and an analysis device, The aforementioned user terminal is The system generates a second identification information by encrypting the first identification information of the subject, and transmits the subject's medical data and the second identification information to the analysis device for analysis. The aforementioned analysis device is An acquisition unit that acquires the aforementioned medical data and the aforementioned second identification information from the user terminal, An analysis unit that analyzes the acquired medical data, A transmission unit transmits the analysis results analyzed by the analysis unit and the second identification information to the user terminal. An analysis system equipped with the following features.

2. The analysis results and the second identification information are transmitted to the user terminal that decodes the second identification information. The analysis system according to claim 1.

3. The user terminal is equipped with an application for decryption, and the application is equipped with a function for encryption. The analysis system according to claim 1.

4. The application body that performs the decryption is transmitted to the user terminal. The analysis system according to claim 3.

5. The second identification information is not displayed on the user terminal. The analysis system according to any one of claims 1 to 3.

6. The system further includes a determination unit that determines whether the acquired second identification information is the first identification information encrypted in a predetermined manner, If the acquisition unit determines that the second identification information is not encrypted in a predetermined manner, it deletes the acquired second identification information. The analysis system according to any one of claims 1 to 3.

7. The aforementioned medical data is image data. The analysis system according to any one of claims 1 to 3.

8. The aforementioned medical data is image data of the subject's bones. The analysis unit outputs an estimated result regarding the bone condition of the subject as an analysis result. The analysis system according to any one of claims 1 to 3.

9. The system further comprises a generation unit that generates supporting information regarding the basis for deriving the aforementioned analysis results, The transmission unit transmits the basis information, including it in the analysis result, to the user terminal. The analysis system according to any one of claims 1 to 3.

10. The aforementioned medical data is image data. The aforementioned supporting information is supporting image data, which is the image data to which information indicating the supporting region that served as the basis for deriving the analysis result is added. The analysis system according to claim 9.

11. The analysis system according to claim 10, wherein the basis image data is image data in which the basis region is shown on the image data.

12. The analysis unit outputs at least one of the following as estimation results: the bone density of the subject's bones at the time the image data was captured; the bone density of the subject's bones after a predetermined period has elapsed since the image data was captured; the location where a fracture is estimated to occur and the likelihood of such a fracture occurring at the time the image data was captured; and the location where a fracture is estimated to occur and the likelihood of such a fracture occurring within the predetermined period from the time the image data was captured. The analysis system according to claim 8.

13. An analysis method performed by an analysis system comprising a user terminal and an analysis device, The aforementioned user terminal To generate a second identification information by encrypting the first identification information of the subject, The medical data of the subject to be analyzed by the analysis device, and the second identification information are transmitted to the analysis device. The aforementioned analysis device, The medical data and the second identification information are obtained from the user terminal. The acquired medical data will be analyzed, The analysis results and the second identification information are transmitted to the user terminal. An analysis method that includes this.

14. An analysis program for causing a computer to function as an analysis system according to any one of claims 1 to 3, wherein the computer functions as the acquisition unit, the analysis unit, and the transmission unit.

15. A computer-readable non-temporary recording medium that stores the analysis program described in claim 14.

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