PROGRAM, DYNAMIC ANALYSIS SYSTEM AND DYNAMIC ANALYSIS DEVICE

Abstract

To provide highly accurate analysis results for a dynamic image while ensuring homogeneity.SOLUTION: A dynamic analysis server 10 comprises: a storage section for storing a dynamic analysis algorithm that performs dynamic analysis on a dynamic image obtained by dynamic imaging with radiation on a subject; a receiving section for receiving, from a first data collection device (for example, a data collection server 40 of a hospital A), a first data set including a first dynamic image obtained by dynamic imaging on a first subject and information obtained by a first examination other than the dynamic imaging on the first subject, and receiving, from a second data collection device (for example, a data collection server 40 of a hospital B), a second data set including a second dynamic image obtained by dynamic imaging on a second subject and information obtained by a second examination other than the dynamic imaging on the second subject; and a learning section for modifying the dynamic analysis algorithm on the basis of the first data set and the second data set.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a program, a dynamic analysis system, and a dynamic analysis device. [Background technology]

[0002] In recent years, attempts have been made to diagnose using dynamic images obtained from dynamic radiography of patients. Because dynamic radiography is a new technology, there is little data on normal and abnormal cases, and few doctors have knowledge of dynamic images and their analysis.

[0003] Dynamic images have the advantage of being able to see the movement of each tissue, providing a wider variety of information than still images. However, for doctors who must review a large number of images every day, the review time per image increases. Therefore, it is more important than still images to provide doctors with the results of image analysis so that they can properly understand the information that can only be obtained from dynamic images without increasing the review time per image. For example, a dynamic diagnostic support information generation system has been proposed that analyzes dynamic images consisting of multiple frame images, distinguishes between normal and abnormal images, and generates diagnostic support information (see Patent Document 1).

[0004] In addition, in diagnosis and clinical research using dynamic images, there is a demand for the ability to view and compare information obtained using conventional diagnostic methods other than dynamic imaging (medical images taken with other modalities, test results, etc.). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-169400 Summary of the Invention [Problem to be solved by the invention]

[0006] However, dynamic images contain significantly more information than still images, and because they are moving images, they allow for not only morphological but also functional diagnosis, resulting in a wide range of analytical results. The information obtained by analyzing dynamic images is diverse, and the types of analysis become more diverse and complex. Furthermore, as the amount of collected dynamic image data increases, the types of analysis targets (diseases to be analyzed, respiratory analysis, blood flow analysis, organ movement analysis, etc.) increase, and analytical accuracy improves, leading to the evolution of dynamic analysis algorithms.

[0007] If such dynamic analysis algorithms are stored in each image management device installed in each hospital and analysis is performed on each image management device, it is difficult to reflect daily evolution in all image management devices. When a new dynamic analysis algorithm is developed, on-premise devices must be updated through version upgrades, which is not timely. As a result, differences in the types and accuracy of analysis occur between image management devices, and ultimately between hospitals, resulting in a lack of fairness in medical services. From the perspective of providing simple, high-quality medical services to many people, it is desirable to obtain similar analysis results regardless of the dynamic image taken at any medical facility, in order to ensure the uniformity of medical services.

[0008] The present invention has been made in consideration of the above-mentioned problems in the conventional technology, and has as its object to provide highly accurate analysis results for dynamic images while ensuring homogeneity. [Means for solving the problem]

[0009] In order to solve the above problem, the invention of claim 1 is a program for correcting a dynamic analysis algorithm that performs dynamic analysis on dynamic images obtained by performing dynamic imaging on a subject using radiation, the program causing a computer to execute the following steps: receiving from a first data collection device a first anonymized data set that includes first dynamic images obtained by performing dynamic imaging on a first subject using radiation and information obtained by a first examination other than dynamic imaging on the first subject; receiving from a second data collection device a second anonymized data set that includes second dynamic images obtained by performing dynamic imaging on a second subject using radiation and information obtained by a second examination other than dynamic imaging on the second subject; and correcting the dynamic analysis algorithm based on the first data set and the second data set. The first test and the second test include at least one of a pulmonary function test, a cardiac function test, a scintigraphy test, a plain X-ray test, and an ultrasound test. It is characterized by the following. The invention described in claim 2 is characterized in that, in the program described in claim 1, the pulmonary function test is a spirometry test. The invention described in claim 3 is characterized in that, in the program described in claim 1 or 2, the cardiac function test is an electrocardiogram test. The invention described in claim 4 is characterized in that, in the program described in any one of claims 1 to 3, the scintigraphy test is a lung scintigraphy test. The invention described in claim 5 is characterized in that, in the program described in any one of claims 1 to 4, the information obtained by the pulmonary function test includes vital capacity, total lung capacity, functional residual capacity, residual volume, RV / TLC, expiratory reserve volume, and forced expiratory volume in one second.

[0010] Claim 6 The invention described in claim 1 Any one of ~5 In the program described above, the first test and the second test are different.

[0011] Claim 7 The invention described in claim 1 Any one of ~6 In the program described in the above, the first dataset and the second dataset include correct labels, and the correct labels include at least one of an interpretation result for a dynamic image and a diagnostic result based on the interpretation result.

[0013] Claim 8 The invention described in claims 1 to 7 In the program described in any one of the above, the dynamic analysis algorithm includes at least one of an analysis subject specific algorithm and a disease diagnosis support algorithm.

[0014] Claim 9 The invention described in claims 1 to 8 In the program described in any one of the above, in the process of receiving the first data set and the second data set, at least one of the first data set and the second data set is received via a dedicated line.

[0015] Claim 10 The invention described in claims 1 to 8 In the program described in any one of the above, in the process of receiving the first data set and the second data set, at least one of the first data set and the second data set is received via a VPN (Virtual Private Network).

[0016] Claim 11 The invention described in claims 1 to 8 In the program described in any one of claims 1 to 5, in the process of receiving the first data set and the second data set, the first data set is received via a dedicated line, and the second data set is received via a VPN.

[0017] Claim 12 The invention described in claims 1 to 11 The program described in any one of the above is characterized in that it causes the computer to execute a process of inputting a third dynamic image obtained by performing dynamic radiography on a third subject, and a process of outputting diagnostic support information for the third dynamic image based on a dynamic analysis algorithm modified by the learning unit.

[0018] Claim 13 The invention described in claim 12In the program described in the above, the first data set and the second data set include correct labels, and the correct labels include at least one of an interpretation result for a dynamic image and a diagnostic result based on the interpretation result, and in the process of outputting the diagnostic support information, information regarding the diagnosis of the dynamic image is output as the diagnostic support information.

[0019] Claim 14 The invention described in claim 12 In the program described in the above, the process of outputting the diagnostic assistance information is characterized in that classification information related to dynamic images is output as the diagnostic assistance information.

[0020] Claim 15 The invention described in claims 1 to 14 A dynamic analysis system comprising a dynamic analysis device that executes the program described in any one of the above, the first data collection device, and the second data collection device.

[0021] Claim 16 The invention described in claim 12~14 and a hospital terminal that transmits the third dynamic image to the dynamic analysis device and receives the diagnostic support information from the dynamic analysis device.

[0022] Claim 17The invention described in the item (1) above includes a storage unit that stores a dynamic analysis algorithm for performing dynamic analysis on dynamic images obtained by performing dynamic imaging on a subject using radiation; a receiving unit that receives, from a first data collection device, a first anonymized data set including first dynamic images obtained by performing dynamic imaging on a first subject using radiation and information obtained by a first examination other than dynamic imaging on the first subject; and a receiving unit that receives, from a second data collection device, a second anonymized data set including second dynamic images obtained by performing dynamic imaging on a second subject using radiation and information obtained by a second examination other than dynamic imaging on the second subject; and a learning unit that corrects the dynamic analysis algorithm stored in the storage unit based on the first data set and the second data set. The first test and the second test include at least one of a pulmonary function test, a cardiac function test, a scintigraphy test, a plain X-ray test, and an ultrasound test. This is a dynamic analysis device characterized by the following. [Effects of the Invention]

[0023] According to the present invention, it is possible to provide highly accurate analysis results for dynamic images while ensuring homogeneity. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a system configuration diagram of a dynamic analysis system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of a dynamic analysis server. [Figure 3] FIG. 10 illustrates an example of the data configuration of a user management table. [Figure 4] FIG. 10 is a diagram illustrating an example of the data configuration of a data management table of a behavior data set DB. [Figure 5] FIG. 2 is a block diagram showing a functional configuration of a hospital terminal. [Figure 6] FIG. 2 is a block diagram showing the functional configuration of a data collection server. [Figure 7] FIG. 2 is a block diagram showing a functional configuration of a service provider terminal. [Figure 8]10 is a flowchart showing an in-hospital data collection process executed by a data collection server. [Figure 9] 10 is a flowchart illustrating a data set transmission process executed by a data collection server. [Figure 10] 10 is a flowchart showing a data set receiving process executed by the dynamic analysis server. [Figure 11] 10 is a flowchart showing an application learning process executed by a dynamic analysis server. [Figure 12] This is an image of supervised learning using a dataset containing correct labels. [Figure 13] This is an image of unsupervised learning using a dataset that does not contain correct labels. [Figure 14] FIG. 10 is a conceptual diagram illustrating a process for creating a normal model using normal case data. [Figure 15] This is an illustration of how machine learning is performed by changing the weighting for each hospital that provides a dataset. [Figure 16] 10 is a flowchart showing an application use permission determination process executed by the dynamic analysis server. [Figure 17] 10 is a ladder chart showing the process executed by the dynamic analysis server and the hospital terminal when using the dynamic analysis app. DETAILED DESCRIPTION OF THE INVENTION

[0025] Hereinafter, an embodiment of a dynamic analysis system according to the present invention will be described with reference to the drawings, but the scope of the invention is not limited to the illustrated examples.

[0026] [Configuration of dynamic analysis system] FIG. 1 shows the system configuration of a dynamic analysis system 100 according to an embodiment of the present invention. 1, the dynamic analysis system 100 is configured with a dynamic analysis server 10, a hospital terminal 30 used by medical staff belonging to each hospital, a data collection server 40 that manages medical information within each hospital, and a service provider terminal 50 used by an administrator of the service provider company. The hospital terminal 30, the data collection server 40, and the service provider terminal 50 are each capable of data communication with the dynamic analysis server 10 via a VPN (Virtual Private Network) connection.

[0027] The dynamic analysis server 10 is a dynamic analysis device that stores and manages medical information uploaded from the data collection server 40 in a dynamic data set DB (DataBase) 152. In addition, in response to a service usage request from a hospital terminal 30, the dynamic analysis server 10 provides services such as various requested application software (hereinafter referred to as apps) to the requesting hospital terminal 30. The apps provided by the dynamic analysis server 10 include a dynamic atlas app 20, a health / disease dynamic information provision app 21, a statistical analysis app 22, a diagnosis support app 23, etc.

[0028] The hospital terminal 30 is a computer device such as a PC (Personal Computer) or tablet terminal used in each hospital. The hospital terminal 30 is used to display medical information stored in the dynamic analysis server 10 and to use various applications provided by the dynamic analysis server 10. The hospital terminal 30 accesses the dynamic analysis server 10 via a web browser and displays processing results provided in the form of a web application. A dedicated application may be downloaded to the hospital terminal 30, and the processing results by the dedicated application may be displayed on the hospital terminal 30.

[0029] The data collection server 40 is a data collection device that collects data sets containing medical information such as dynamic images obtained by performing dynamic radiography on patients in a hospital, and information obtained by tests other than dynamic radiography on patients (including medical images obtained by performing tests other than dynamic radiography, test results such as measurement values, etc.). The data collection server 40 performs anonymization processing (processing to make the information anonymous or pseudonymized, etc., so that individuals cannot be identified) on the patient information included in the data sets collected within the hospital, and uploads the data to the dynamic analysis server 10. While FIG. 1 illustrates a state in which the data collection server 40 is installed within each hospital, the data collection server 40 may be installed either inside or outside the hospital. It should be noted that only for hospitals that are designated as hospitals that provide data sets to the dynamic analysis server 10, data sets are provided to the dynamic analysis server 10 from the data collection server 40 of the hospital.

[0030] The service provider develops a dynamic analysis algorithm and creates various applications. The service provider terminal 50 loads the various applications created by the service provider onto the dynamic analysis server 10. The service provider may be not only a development company that develops applications using a dynamic dataset and provides these applications on the dynamic analysis server 10, but also a research institute (university, etc.) that conducts research and development of applications, or a company that develops and sells applications incorporating dynamic analysis and various diagnostic support functions using an API (Application Programming Interface). There may also be multiple service providers.

[0031] [Configuration of Dynamic Analysis Server] FIG. 2 shows the functional configuration of the dynamic analysis server 10. As shown in Figure 2, the dynamic analysis server 10 is configured with a control unit 11, an operation unit 12, a display unit 13, a communication unit 14, a memory unit 15, a dynamic atlas application 20, a healthy / disease dynamic information provision application 21, a statistical analysis application 22, a diagnostic support application 23, etc., and each unit is connected by a bus.

[0032] The control unit 11 is composed of a CPU (Central Processing Unit), RAM (Random Access Memory), etc., and comprehensively controls the processing operations of each unit of the dynamic analysis server 10. The CPU of the control unit 11 reads out the system program and various processing programs stored in the storage unit 15, expands them in the RAM, and executes various processes according to the expanded programs. The control unit 11 may also be equipped with a GPU (Graphics Processing Unit) for performing AI processing.

[0033] The operation unit 12 is configured with a keyboard having cursor keys, letter and number input keys, various function keys, etc., and a pointing device such as a mouse, and outputs instruction signals input by key operations on the keyboard or mouse operations to the control unit 11.

[0034] The display unit 13 is configured with a monitor such as an LCD (Liquid Crystal Display), and displays various screens according to instructions of a display signal input from the control unit 11.

[0035] The communication unit 14 is configured by a network interface and the like, and transmits and receives data to and from external devices connected via a communication network.

[0036] The storage unit 15 is configured with an HDD (Hard Disk Drive), an SSD (Solid State Drive), etc., and stores various processing programs, parameters and files necessary for executing the programs, etc. For example, the storage unit 15 stores a web server program for realizing the function of a web server that communicates with a web browser installed in the hospital terminal 30 via the HTTP protocol and provides various web screens to the web browser, application programs that run on the web server, etc.

[0037] The storage unit 15 stores application programs for implementing a dynamic atlas application 20, a health and disease dynamic information provision application 21, a statistical analysis application 22, and a diagnosis support application 23.

[0038] The storage unit 15 also stores a user management table 151 and a behavior data set DB 152.

[0039] The user management table 151 is a table for managing information (user information) for each user (healthcare worker) who uses the dynamic analysis system 100.

[0040] FIG. 3 shows an example of the data configuration of the user management table 151. As shown in FIG. In the user management table 151, a user ID, a password, an access permission, a continuous use contract, etc. are associated with each user. The user ID is the user's identification information. The password is used to authenticate the user when logging in to the system. The access permission is information (yes / no) indicating whether or not access to the dynamic analysis server 10 is permitted. The continuous use contract is information (yes / no) indicating whether or not there is a contract for continuous use of the services (each application and data) provided by the dynamic analysis server 10, and is defined for each application and data.

[0041] The dynamic data set DB 152 is a database that stores and manages data collected in each hospital and received from each hospital's data collection server 40. The dynamic data set DB 152 has a data management table 153 and an image storage area 154. The data management table 153 is a table for managing data sets collected at each hospital.

[0042] FIG. 4 shows an example of the data configuration of the data management table 153. Anonymous IDs, behavioral information, attribute information, diagnostic results, and related test information are associated and stored in the data management table 153. In the data management table 153, one row of data is treated as one case data.

[0043] Anonymous IDs are identification information used to distinguish between subjects (patients) who are the subject of dynamic radiography or other examinations so that individuals cannot be identified. A unique anonymous ID is assigned to each subject, but it is not possible to identify personal information such as the patient ID or patient name corresponding to the subject from the anonymous ID.

[0044] The dynamic information is information obtained by performing dynamic imaging on the subject corresponding to the anonymous ID. The dynamic information includes dynamic images and movement information. Dynamic radiography is radiography that captures dynamics such as changes in the shape of lung expansion and contraction due to breathing, and heartbeat. In dynamic radiography, the subject is repeatedly irradiated with pulsed radiation such as X-rays at predetermined intervals (pulse irradiation), or is irradiated continuously at a low dose without interruption (continuous irradiation), thereby obtaining multiple images that show the subject's dynamics. Dynamic radiography includes video imaging, but does not include still images taken while displaying video (fluoroscopy).

[0045] A dynamic image is a series of images (image data) obtained by dynamic shooting. Each of the multiple images that make up a dynamic image is called a frame image. Dynamic images include moving images, but do not include images obtained by capturing still images while displaying the moving image. The dynamic images themselves are stored in the image storage area 154 with tag information attached, and the tag information for the dynamic image is stored in the "dynamic image" field of the data management table 153.

[0046] Motion information is information that indicates the movement of a subject (tissues related to the respiratory system, circulatory system, orthopedics, swallowing, etc.) obtained by analyzing dynamic images. The motion information includes information such as the position obtained for each frame image, the velocity obtained from the difference between frame images, and the maximum velocity and size change rate analytically obtained from this information. For example, information that quantifies the movement of tissues, such as the lung field area change rate, airway diameter narrowing rate, and diaphragm velocity, is used as the motion information.

[0047] When the posterior ribs, sternum, clavicle, spine, diaphragm, and thorax are the imaging target areas, the movement information used includes time series changes in position, time series changes in velocity, maximum distance from the initial position, maximum and minimum velocity, etc. When the heart is the imaging target region, time series changes in size, time series changes in signal value density, size change rate, signal value density change rate, etc. are used as movement information. When the aortic arch is the imaging target region, the time series change in signal value density, the rate of change in signal value density, etc. are used as the movement information. When the airway is the imaging target region, the time series change in the airway diameter size, the rate of airway diameter stenosis, etc. are used as movement information. When the lung field is the imaging target region, the time series change in lung field size, the maximum / minimum lung field area change rate, the signal value density change rate, etc. are used as movement information.

[0048] The movement information may be information acquired from the data collection server 40 of each hospital, or may be information obtained by the dynamic analysis server 10 analyzing the dynamic images acquired from the data collection server 40.

[0049] The attribute information is information indicating the attributes of the subject who is the subject of dynamic imaging and the attributes of the dynamic image. Examples of the attribute information include age, sex, height, weight, BMI, smoking history, imaging month, device information (information on the modality used for dynamic imaging), imaging conditions, imaging region, imaging direction, etc. The attribute information does not include information that can identify an individual (such as name, address, or telephone number).

[0050] The diagnosis result is a diagnosis result for a dynamic image (a subject for which a dynamic image is taken), and includes a normal / abnormal flag and a diagnosis name. The normal / abnormal flag is a flag indicating whether or not the subject has a disease. If the subject does not have a disease, the flag is "normal," and if the subject has a disease, the flag is "abnormal." The diagnosis name is the diagnosis name (such as the name of the disease) when the normal / abnormal flag is "abnormal", that is, when the subject has a disease. When the normal / abnormal flag is "normal", the diagnosis name is "no disease". Note that when the normal / abnormal flag is "normal", the diagnosis name may be left blank. The diseases include, for example, respiratory, circulatory, orthopedic, and swallowing-related diseases. More specifically, respiratory diseases include COPD (chronic obstructive pulmonary disease) and interstitial pneumonia, circulatory diseases include heart failure and pulmonary embolism, and orthopedic diseases include arthropathy and fractures. Furthermore, in addition to normal / abnormal flags for all diseases, normal / abnormal flags for specific diseases may be added, such as a normal / abnormal flag for diseases related to the respiratory system and a normal / abnormal flag for diseases related to the circulatory system.

[0051] In the data management table 153, a record whose normal / abnormal flag is "normal" is a normal case, and a record whose normal / abnormal flag is "abnormal" is an abnormal case.

[0052] Related test information is information obtained by performing tests on a subject other than dynamic radiography, including medical images obtained by other than dynamic radiography, measurements calculated from medical images obtained by other than dynamic radiography, and test results (e.g., measurements) obtained by tests that do not involve images. Tests other than dynamic radiography include pulmonary function tests (e.g., spirometry tests), cardiac function tests (e.g., electrocardiograms), scintigraphy tests, CT scans, plain X-ray tests, MRI scans, ultrasound tests, and pulse oximeter (SpO2: percutaneous arterial oxygen saturation) tests. In Figure 4, related test information includes pulmonary function test results, CT images, plain X-ray images, and lung scintigraphy images. Pulmonary function test results include vital capacity (VC), total lung capacity (TLC), functional residual capacity (FRC), residual volume (RV), RV / TLC, expiratory reserve volume (ERV), forced expiratory volume in one second (FEV1), etc.

[0053] If the related examination information includes medical images (non-dynamic images) obtained by imaging other than dynamic imaging, the medical images themselves are stored in the image storage area 154 with tag information attached, and the tag information for the medical images is stored in the "CT image" field, "plain X-ray image" field, "pulmonary scintigraphy image" field, etc. of the data management table 153.

[0054] The image storage area 154 stores medical images (dynamic images and non-dynamic images) uploaded from the data collection server 40 of each hospital.

[0055] The dynamic atlas app 20, the health / disease dynamic information provision app 21, the statistical analysis app 22, and the diagnostic support app 23 are apps provided to the hospital terminal 30, which perform processing in response to operations from the hospital terminal 30 and provide the processing results to the hospital terminal 30.

[0056] The dynamic atlas app 20 and the health and disease dynamic information providing app 21 are contents created using data from the dynamic dataset DB 152, and are created by processing, treating, selecting, etc. the collected data. As data is added to the dynamic dataset DB 152 along with attribute information such as age group, sex, and race, the dynamic atlas app 20 and the health and disease dynamic information providing app 21 are also updated.

[0057] The Dynamic Atlas app 20 provides normal examples of dynamic images, and provides dynamic images and images obtained by existing modalities for comparison (CT, scintigraphy, MRI, ultrasound, still X-ray images, etc.). For example, the Dynamic Atlas app 20 provides anonymized data of normal examples by body part, gender, body shape, age, and device. The Dynamic Atlas app 20 displays search results for normal examples that match the conditions specified by the user (body part, gender, body shape, age, imaging device, imaging facility, photographer, diagnosis result, etc.) on the hospital terminal 30. For example, a user of the hospital terminal 30 can use the Dynamic Atlas app 20 to check representative dynamic images of normal lung function and related examination information (CT images, etc.) at that time.

[0058] The health and disease dynamic information provision application 21 provides dynamic images of normal and abnormal cases (cases) for each case, as well as images obtained by existing modalities for comparison. For example, when a user of the hospital terminal 30 specifies "pulmonary embolism" in the health and disease dynamic information provision application 21, they can view dynamic images of representative pulmonary embolism and related examination information (CT images, etc.) at that time.

[0059] The statistical analysis application 22 and the diagnostic support application 23 are added as new applications when valuable analytical methods are developed. The statistical analysis application 22 and the diagnostic support application 23 are developed by performing correlation, testing, regression analysis, time-series data analysis, etc. according to the site and case.

[0060] The statistical analysis application 22 provides the results of statistical analysis of data used in the dynamic atlas application 20 and the healthy and disease dynamic information providing application 21. The statistical analysis application 22 provides the hospital terminal 30 with statistical data (average, variance, time series data, etc.) for normal and abnormal cases by body part, body shape, age, and device.

[0061] The diagnostic support application 23 is a dynamic analysis application that performs dynamic analysis of a specified function or a specified disease on dynamic images for diagnostic support, and is realized by software processing in collaboration with the control unit 11 and a dynamic analysis application program corresponding to the analysis content.

[0062] The diagnostic support applications 23 include applications for each analysis target and applications for supporting disease diagnosis. The analysis target specific app performs dynamic analysis of predetermined functions (ventilation analysis, blood flow analysis, orthopedic analysis, diaphragm measurement, etc.) on dynamic images. Examples of analysis target specific apps available include a ventilation analysis app, a blood flow analysis app, an orthopedic analysis app, and a diaphragm measurement app. A disease diagnosis support app performs dynamic analysis of dynamic images related to a predetermined disease (COPD, interstitial pneumonia, etc.), determines whether or not the disease is present, and provides information used for that determination. Examples of disease diagnosis support apps available include a COPD diagnosis support app and an interstitial pneumonia diagnosis support app.

[0063] When the hospital terminal 30 accesses the dynamic analysis server 10 from a login account (user ID and password) corresponding to each user, the control unit 11 refers to the user management table 151 in the storage unit 15 to determine whether to permit use of the app. If the user ID and password entered into the hospital terminal 30 are registered in the user management table 151, the "access permission" corresponding to the entered user ID is "yes," and the "continuous use contract" for the app corresponding to the entered user ID is "yes," the control unit 11 permits the user corresponding to this user ID to use the app. Furthermore, if the user ID and password entered into the hospital terminal 30 are registered in the user management table 151 and the "access permission" corresponding to the entered user ID is "yes," the control unit 11 permits the user corresponding to this user ID to use the app when a new use contract is concluded, even if the "continuous use contract" for the app corresponding to the entered user ID is "no."

[0064] In response to a viewing request from the hospital terminal 30, the control unit 11 provides the requested medical information to the requesting hospital terminal 30.

[0065] The algorithms used in the programs corresponding to each dynamic analysis app (app for each analysis target, disease diagnosis support app) of the diagnostic support app 23 correspond to "dynamic analysis algorithms that perform dynamic analysis on dynamic images obtained by performing dynamic imaging on a subject using radiation." In other words, the memory unit 15, which stores the programs corresponding to each dynamic analysis app, corresponds to the "storage unit that stores dynamic analysis algorithms."

[0066] The dynamic analysis algorithm includes at least one of an analysis target specific algorithm (for example, ventilation analysis, blood flow analysis, orthopedic analysis, etc.) and a disease diagnosis support algorithm (for example, diagnosis support and differential diagnosis for COPD and interstitial pneumonia, etc.).

[0067] The communication unit 14 receives, from a first data collection device (e.g., the data collection server 40 of Hospital A), a first anonymized data set including a first dynamic image obtained by performing dynamic radiography on a first subject and information obtained by a first examination other than dynamic radiography on the first subject, and receives, from a second data collection device (e.g., the data collection server 40 of Hospital B), a second anonymized data set including a second dynamic image obtained by performing dynamic radiography on a second subject and information obtained by a second examination other than dynamic radiography on the second subject. That is, the communication unit 14 functions as a receiving unit.

[0068] "Information obtained by tests other than dynamic radiography" includes numerical values ​​(numerical values ​​obtained by tests such as spirometry), images obtained by tests other than dynamic radiography (scintigraphy tests, CT scans, etc.), etc. "Information obtained by tests other than dynamic radiography" may also include flags indicating normal or abnormal.

[0069] Here, the first test and the second test may be different or the same, and the first test included in the first data set and the second test included in the second data set may be partially the same. Furthermore, the first test and the second test each include at least one of a pulmonary function test (e.g., spirometry, etc.), a cardiac function test (e.g., electrocardiogram, etc.), a scintigraphy test, a CT test, a plain X-ray test, an MRI test, and an ultrasound test.

[0070] The first data set and the second data set may include correct labels and constitute training data used in machine learning. The correct label includes at least one of an interpretation result for the dynamic image and a diagnosis result based on the interpretation result. The interpretation result is information obtained by interpreting a dynamic image, and includes the position, size, type, etc. of a shadow such as a tumor. The diagnosis result is a result of a diagnosis based on the image interpretation result, and is information including a distinction between normal and abnormal, a diagnosis such as COPD, and the like.

[0071] The control unit 11 stores the received first data set and second data set in the dynamic state data set DB 152 of the storage unit 15. The correct answer label corresponds to the "diagnosis result" in the data management table 153 shown in FIG.

[0072] The control unit 11 modifies the dynamic analysis algorithm stored in the memory unit 15 (storage unit) based on the first data set and the second data set. That is, the control unit 11 functions as a learning unit.

[0073] In summary, the communication unit 14 receives data sets from each of the multiple data collection devices (such as the data collection server 40). Each data set includes dynamic images obtained by performing dynamic radiography on a certain subject and information obtained by an examination other than dynamic radiography on the same subject as the subject targeted for dynamic radiography, and is anonymized. The control unit 11 performs machine learning for dynamic analysis and diagnostic support based on the data sets received from each of the multiple data collection devices (data sets stored in the dynamic data set DB 152), and modifies the dynamic analysis algorithm.

[0074] As machine learning, support vector machines (SVM), random forests, deep learning, etc. can be used.

[0075] The control unit 11 inputs, via the communication unit 14, a third dynamic image obtained by performing dynamic radiography on a third subject (patient to be diagnosed) using radiation. The control unit 11 outputs diagnostic support information for the third dynamic image based on the dynamic analysis algorithm (learning model) corrected by the control unit 11 (learning unit) via the communication unit 14. Specifically, the control unit 11 inputs the third dynamic image received from the hospital terminal 30 to a dynamic analysis app selected from the diagnostic support apps 23, acquires diagnostic support information generated by the dynamic analysis app, and transmits the diagnostic support information via the communication unit 14 to the hospital terminal 30 that sent the third dynamic image.

[0076] The dynamic analysis app uses artificial intelligence (AI) to perform dynamic analysis on the dynamic images of the target for diagnosis. The dynamic analysis app generates diagnostic support information using a dynamic analysis algorithm that has been trained (corrected) through machine learning based on a dataset.

[0077] Among the diagnostic support applications 23, a dynamic analysis application trained based on training data (a data set including correct answer labels) outputs information relating to the diagnosis of the dynamic image as diagnostic support information. The control unit 11 transmits (outputs) the information relating to the diagnosis of the dynamic image acquired from the dynamic analysis application to the hospital terminal 30 that transmitted the third dynamic image via the communication unit 14. The information relating to the diagnosis of the dynamic image includes the diagnosis result (normal / abnormal, diagnosis name, etc.), the interpretation result, etc.

[0078] Among the diagnostic support applications 23, a dynamic analysis application that has been trained without training data (based on a data set that does not include a correct answer label) outputs classification information regarding dynamic images as diagnostic support information. The control unit 11 transmits (outputs) the classification information regarding dynamic images acquired from the dynamic analysis application via the communication unit 14 to the hospital terminal 30 that transmitted the third dynamic images. The classification information regarding dynamic images is information indicating classifications (groups) obtained by extracting data features through machine learning and classifying the dynamic images into multiple groups. The type of disease, symptom, etc. that each group corresponds to (high probability of COPD, low probability of COPD, etc.) is determined by a human.

[0079] Charges for app usage can be made on a per-use, flat-rate, or pay-as-you-go basis. For individual users, for example, by charging each time an app is used, users can reduce app purchase costs and initial setup costs. Alternatively, individual users may sign a monthly or yearly usage contract and collect a fixed monthly or yearly usage fee, which reduces the initial setup costs for users and clarifies the monthly or yearly payment fees (subscription). In addition, a fee may be collected from individual users according to the amount of use of the app (pay-as-you-go system).

[0080] In addition, the dynamic atlas application 20 may allow users to use a predetermined number of dynamic images free of charge, and if a paid contract is signed, the data may be viewable without any restrictions. In addition, apps that do not require a fee (such as a dynamics atlas app 20 and a health / disease dynamics information providing app 21) may be provided. If the subscription is not renewed or if the user fails to pay, the user will lose their right to use the app.

[0081] The dynamic analysis server 10 may also use an API to provide researchers, developers of dynamic analysis systems, and the like with AI applications (application software using machine learning or deep learning) for dynamic analysis, various diagnostic support, and the like. This allows researchers, developers, and the like to create applications incorporating the dynamic analysis and various diagnostic support functions provided in the form of an API. When executing an application created in this way, the API on the dynamic analysis server 10 is accessed and the dynamic analysis and various diagnostic support functions are used as tools. In this case, the functions corresponding to the various apps provided by the API may be charged for each use, or a pay-as-you-go system may be used. Furthermore, an annual contract may be concluded for each device that uses the functions corresponding to the various apps, and usage fees may be collected on an annual basis.

[0082] [Hospital terminal configuration] FIG. 5 shows the functional configuration of the hospital terminal 30. As shown in FIG. 5, the hospital terminal 30 comprises a control unit 31, an operation unit 32, a display unit 33, a communication unit 34, a storage unit 35, etc., and each unit is connected by a bus.

[0083] The control unit 31 is composed of a CPU, RAM, etc., and comprehensively controls the processing operations of each unit of the hospital terminal 30. The CPU of the control unit 31 reads out the system program and various processing programs stored in the storage unit 35, expands them in the RAM, and executes various processes according to the expanded programs.

[0084] The operation unit 32 is configured with a keyboard having cursor keys, letter and number input keys, various function keys, etc., and a pointing device such as a mouse, and outputs instruction signals input by operating the keys on the keyboard or the mouse to the control unit 31. The operation unit 32 may also be provided with a touch panel on the display screen of the display unit 33, and in this case, outputs instruction signals input via the touch panel to the control unit 31.

[0085] The display unit 33 is configured with a monitor such as an LCD, and displays various screens according to instructions of a display signal input from the control unit 31. For example, the display unit 33 displays various web screens based on display data for the various web screens received from the dynamic analysis server 10.

[0086] The communication unit 34 is configured by a network interface and the like, and transmits and receives data to and from external devices connected via a communication network.

[0087] The storage unit 35 is configured with an HDD, SSD, etc., and stores various processing programs, parameters and files required for executing the programs, etc. For example, the storage unit 35 stores a web browser program for realizing a web browser, etc.

[0088] The control unit 31 transmits a third dynamic image (a dynamic image of the patient to be diagnosed) obtained by performing dynamic radiography on a third subject to the dynamic analysis server 10 via the communication unit 34, and receives diagnostic support information from the dynamic analysis server 10. The diagnostic support information is output for the third dynamic image based on a dynamic analysis algorithm.

[0089] Configure Data Collection Server FIG. 6 shows the functional configuration of the data collection server 40. As shown in FIG. 6, the data collection server 40 comprises a control unit 41, an operation unit 42, a display unit 43, a communication unit 44, a storage unit 45, etc., and each unit is connected by a bus.

[0090] Since the components that make up the data collection server 40 are basically the same as the components that make up the hospital terminal 30, only the parts that are characteristic of the data collection server 40 will be described, and components that are similar to those of the hospital terminal 30 will not be described.

[0091] The storage unit 45 stores a DB 451. The DB 451 stores dynamic images obtained by dynamic radiography performed in the hospital where the data collection server 40 is installed, information obtained by examinations other than dynamic radiography (including non-dynamic images, examination results such as measurement values), and the like.

[0092] The data collection server 40 can also be connected to an external HDD that can only be accessed by hospital personnel. An anonymous ID correspondence table is stored in the external HDD. The anonymous ID correspondence table stores information about patients in the hospital (patient ID, patient name, address, telephone number, etc.) associated with anonymous IDs. In other words, in the external HDD, the anonymous ID correspondence table associates patients (subjects) with anonymous IDs. Note that patients for whom an anonymous ID has not been generated do not have records in the anonymous ID correspondence table. For security reasons, the anonymous ID correspondence table is not stored in the data collection server 40. The data collection server 40 cannot access the anonymous ID correspondence table in the external HDD for any purpose other than referencing and registering the anonymous IDs of target patients.

[0093] [Configuration of service provider terminal] FIG. 7 shows the functional configuration of the service provider terminal 50. As shown in FIG. As shown in FIG. 7, the service provider terminal 50 comprises a control unit 51, an operation unit 52, a display unit 53, a communication unit 54, a memory unit 55, etc., and each unit is connected by a bus. Each component of the service provider terminal 50 is basically the same as each component of the hospital terminal 30, and therefore a description thereof will be omitted.

[0094] [Operation of dynamic analysis system] Next, the operation of the dynamic analysis system 100 will be described.

[0095] <In-hospital data collection and processing> 8 is a flowchart showing the in-hospital data collection process executed by the data collection server 40. The in-hospital data collection process is realized by software processing in cooperation between the CPU of the control unit 41 and a program stored in the storage unit 45.

[0096] First, when a dynamic radiography device in a hospital performs dynamic radiography on a subject, the control unit 41 of the data collection server 40 acquires dynamic images from the dynamic radiography device via the communication unit 44 (step S1).

[0097] Next, the control unit 41 acquires attribute information of the subject who is the subject of dynamic imaging or attribute information of the dynamic image (step S2). Specifically, the control unit 41 acquires attribute information from additional information attached to the dynamic image file, or acquires attribute information (patient information) corresponding to the subject from an electronic medical record device in the hospital via the communication unit 44. The control unit 41 may display an input screen for attribute information on the display unit 43 and accept input of attribute information by operation of the operation unit 42 by a medical professional.

[0098] Next, the control unit 41 acquires related examination information relating to examinations other than dynamic imaging that were performed on the subject who was the subject of dynamic imaging (step S3). Specifically, the control unit 41 extracts examination information stored in the storage unit 45, in which the subject (patient) is the same as the dynamic image acquired in step S1. Here, the control unit 41 may limit the examination information to only those examinations performed within a predetermined period based on the date and time of the dynamic imaging, or may limit the examination information to only those examinations related to the imaging site of the dynamic imaging.

[0099] Next, the control unit 41 acquires a diagnosis result for the dynamic image acquired in step S1 (step S4). Specifically, the control unit 41 displays a diagnosis result input screen on the display unit 43, and accepts input of normal (no disease) / abnormal (disease present) and diagnosis name (if disease present) through operation of the operation unit 42 by a medical professional. The diagnosis result may also include information such as annotations added to the dynamic image.

[0100] Next, the control unit 41 determines whether or not there is an anonymous ID for the patient (subject who is the subject of dynamic imaging) (step S5). Specifically, the control unit 41 accesses the external HDD via the communication unit 44, and refers to the anonymous ID correspondence table stored in the external HDD to determine whether or not there is a record for the patient. If there is an anonymous ID for the patient (step S5; YES), the control unit 41 uses the anonymous ID assigned to the patient (step S6).

[0101] In step S5, if there is no anonymous ID for the patient (step S5; NO), the control unit 41 assigns a new unique anonymous ID to the patient (step S7). The control unit 41 assigns a numerical value unrelated to the patient as the anonymous ID so that the patient cannot be identified from the anonymous ID. The control unit 41 accesses the external HDD via the communication unit 44 and stores the patient information (patient ID, patient name, etc.) and the assigned anonymous ID in the anonymous ID correspondence table in association with each other.

[0102] After step S6 or step S7, the control unit 41 deletes personal information (patient ID, patient name, etc.) from each piece of acquired information (step S8), so that the data set uploaded to the dynamic analysis server 10 later will be anonymized.

[0103] Next, the control unit 41 associates the anonymous ID, dynamic image, attribute information, related examination information, and diagnosis result, and stores them in the DB 451 of the storage unit 45 (step S9). This completes the in-hospital data collection process.

[0104] In the in-hospital data collection process, in step S1, dynamic images are acquired at the time when dynamic photography is performed, but the timing of acquiring dynamic images is not limited to this, and dynamic images that have been captured and stored in advance may also be acquired.

[0105] <Dataset transmission process> 9 is a flowchart showing the data set transmission process executed by the data collection server 40. The data set transmission process is realized by software processing in cooperation between the CPU of the control unit 41 and a program stored in the storage unit 45.

[0106] The control unit 41 of the data collection server 40 determines whether it is time to transmit data (step S11). For example, the control unit 41 determines that it is time to transmit data when a predetermined time, such as nighttime when communication volume is low, has arrived, or when communication volume is equal to or less than a predetermined value. Alternatively, the control unit 41 may determine that it is time to transmit data when the amount of data in the DB 451 of the storage unit 45 that has not yet been transmitted reaches or exceeds a predetermined value.

[0107] If it is not the timing to transmit data (step S11; NO), the process returns to step S11 and the process is repeated.

[0108] In step S11, if it is time to send data (step S11; YES), the control unit 41 sends a data set (anonymized) that associates the anonymous ID, dynamic image, attribute information, related test information, and diagnostic results stored in DB451 of the memory unit 45 to the VPN-connected dynamic analysis server 10 via the communication unit 44 (step S12). Here, the data set transmitted from the data collection server 40 to the dynamic analysis server 10 may include movement information corresponding to the dynamic image. This completes the data set transmission process.

[0109] After the data set transmission process, the control unit 41 of the data collection server 40 may delete the transmitted data set from the DB 451. Alternatively, the control unit 41 may leave the transmitted data set in the DB 451 with a note indicating that the data set has been transmitted. Alternatively, the data collection server 40 may be configured to transmit a data set to the dynamic analysis server 10 only when the user selects to transmit the data set, according to user settings.

[0110] In addition, since some hospitals may have restrictions on communication with external networks, the dataset may be saved on a recording medium or the like and physically handed over to the administrator of the dynamic analysis server 10, who may then import the dataset from the recording medium into the dynamic analysis server 10.

[0111] <Dataset reception process> 10 is a flowchart showing the data set reception process executed by the dynamic analysis server 10. The data set reception process is realized by software processing in cooperation between the CPU of the control unit 11 and a program stored in the storage unit 15.

[0112] The control unit 11 of the dynamic analysis server 10 determines whether or not a data set (anonymized) has been received from any of the VPN-connected data collection servers 40 via the communication unit 14 (step S21).

[0113] If no data set is received from any of the data collection servers 40 (step S21; NO), the process returns to step S21 and the process is repeated.

[0114] In step S21, when a data set is received from any of the data collection servers 40 (step S21; YES), the control unit 11 stores the received data set in the dynamic data set DB 152 of the storage unit 15 (step S22). Specifically, the control unit 11 stores information about the received data set in association with the data in the data management table 153 (see FIG. 4), and also stores medical images (dynamic images, non-dynamic images) in the image storage area 154. This completes the data set reception process.

[0115] By repeating the dataset reception process, the dynamic analysis server 10 acquires anonymized datasets that associate anonymous IDs, dynamic images, attribute information, related test information, and diagnostic results from the data collection servers 40 of multiple hospitals. The related test information included in each dataset may differ for each dataset. For example, the communication unit 14 of the dynamic analysis server 10 receives a dataset including dynamic images and pulmonary function test results from the data collection server 40 of Hospital A, and receives a dataset including dynamic images and CT images from the data collection server 40 of Hospital B.

[0116] The movement information stored in the data management table 153 of the movement data set DB 152 may be calculated by the movement analysis server 10 or may be included in the data set received from the data collection server 40.

[0117] The motion information includes motion information related to the respiratory system, circulatory system, orthopedics, swallowing, etc., depending on the corresponding dynamic image. Motion information related to the respiratory system includes the lung field area change rate, airway diameter narrowing rate, diaphragm velocity, etc. Motion information related to the circulatory system includes the speed at which the heart wall moves, etc. Motion information related to orthopedics includes the trajectories (position change information) of bending and straightening joints such as knees and elbows, and the speed at which the joints are straightened, etc.

[0118] For example, the control unit 11 detects the position (area) of the lung field from dynamic images of the front of the chest, calculates the lung field area for each frame image, and then calculates the lung field area change rate from the movement of the lung field in a series of dynamic images. The control unit 11 also detects the position of the airway from dynamic images of the front of the chest and calculates the airway diameter for each frame image. The control unit 11 then calculates the airway diameter stenosis rate from the movement of the airway in the series of dynamic images. The control unit 11 also detects the position of the diaphragm from dynamic images (multiple frame images) of the front of the chest, calculates the diaphragm velocity between the frame images, and determines the maximum diaphragm velocity from the movement of the diaphragm in the series of dynamic images.

[0119] When calculating the movement information in the dynamic analysis server 10 or the data collection server 40, a diagnosis support application 23 (dynamic analysis application) corresponding to the movement information may be used.

[0120] <App learning process> 11 is a flowchart showing the application learning process executed by the dynamic analysis server 10. The application learning process is realized by software processing in cooperation between the CPU of the control unit 11 and a program stored in the storage unit 15.

[0121] First, the control unit 11 of the dynamic analysis server 10 determines a dynamic analysis application to be learned (step S31). Specifically, the control unit 11 selects, as the dynamic analysis application to be learned, an application from the diagnostic support applications 23 that corresponds to the disease (illness) of the dataset added to the dynamic dataset DB 152. For example, if the diagnosis of the dataset is "COPD," the dynamic analysis application for ventilation analysis is the learning application, and if the diagnosis of the dataset is "thrombus," the dynamic analysis application for blood flow analysis is the learning application.

[0122] Next, the control unit 11 reads out the data sets stored in the dynamic data set DB 152, and learns a dynamic analysis algorithm corresponding to the dynamic analysis application to be learned based on each data set (step S32). Note that information indicating whether or not the data sets stored in the dynamic data set DB 152 have been used for learning (correction) of each dynamic analysis application may be added to the data sets, and only data sets that have not been used by the dynamic analysis application to be learned may be used for this learning.

[0123] Dynamic analysis algorithms include those that have functions such as image processing of dynamic images, triage processing, diagnostic judgment processing, and clustering processing (sorting based on similarity) based on the characteristics of dynamic images. Triage generally refers to determining the priority of examinations and treatments according to the severity of the condition. Triage processing in dynamic analysis determines the priority of image confirmation for dynamic images. For example, triage processing using clustering can assign priorities such as "abnormal, immediate confirmation (priority: high)," "likely abnormal (priority: medium)," and "images that appear normal (priority: low)."

[0124] Next, the control unit 11 evaluates the learning result of the learned (modified) dynamic analysis algorithm (step S33). For example, the control unit 11 evaluates whether or not the modified dynamic analysis algorithm can obtain a desired output result (analysis result) from the sample data (dynamic image).

[0125] Here, the control unit 11 determines whether the learned dynamic analysis algorithm satisfies a predetermined update criterion (step S34). Specifically, the control unit 11 automatically determines whether the learned dynamic analysis algorithm satisfies the update criterion based on a score for evaluating the learning result. It should be noted that the process of step S34 may be carried out by a person in charge of quality control of the dynamic analysis application, who may carry out a final validity evaluation and guarantee the quality before updating the application.

[0126] If the learned dynamic analysis algorithm does not satisfy the update criteria (step S34; NO), the process returns to step S32, and the control unit 11 performs learning again, for example, by excluding data that is not suitable for learning.

[0127] In step S34, if the learned dynamic analysis algorithm satisfies the update criteria (step S34; YES), the control unit 11 updates the learning target application in the dynamic analysis server 10 to reflect the learned dynamic analysis algorithm (step S35). This completes the application learning process.

[0128] (Study example 1) 12 is an image diagram of supervised learning using a dataset including correct labels (diagnosis results). The control unit 11 of the dynamic analysis server 10 receives inputs such as dynamic images, lung field area change rate, airway diameter stenosis rate, diaphragm velocity, pulmonary function test results, and attribute information (age, sex, smoking history, height, weight, BMI), and outputs the diagnosis results (no disease, COPD, bronchial asthma, lung cancer, etc.) for each case data stored in the dynamic dataset DB 152, and generates a classifier.

[0129] When the dynamic analysis app is used from the hospital terminal 30, the dynamic analysis app uses a dynamic analysis algorithm including a trained classifier to output a diagnosis prediction result for the dynamic image to be diagnosed. Specifically, the dynamic analysis app calculates movement information (lung field area change rate, airway diameter stenosis rate, diaphragm velocity, etc.) from the dynamic image to be diagnosed, and inputs the dynamic image, lung field area change rate, airway diameter stenosis rate, diaphragm velocity, pulmonary function test results, attribute information (age, sex, smoking history, height, weight, BMI), etc. into the trained classifier to obtain an output result (diagnosis prediction result). However, with regard to related test information such as pulmonary function test results and attribute information, only information that has been acquired as information corresponding to the dynamic image to be diagnosed needs to be used.

[0130] (Study example 2) 13 is an illustration of unsupervised learning using a dataset that does not include a correct answer label. The control unit 11 of the dynamic analysis server 10 performs machine learning for each case data stored in the dynamic dataset DB 152 using dynamic images, lung field area change rate, airway diameter stenosis rate, diaphragm velocity, pulmonary function test results, attribute information (age, sex, smoking history, height, weight, BMI), etc., and divides the dynamic images into multiple groups based on the similarity between the data (clustering).

[0131] When the dynamic analysis app is used from the hospital terminal 30, the dynamic analysis app uses a dynamic analysis algorithm for grouping obtained by machine learning to output information (clustering results) indicating the group to which the dynamic image to be diagnosed belongs. Specifically, the dynamic analysis app calculates movement information (lung field area change rate, airway diameter narrowing rate, diaphragm velocity, etc.) from the dynamic image to be diagnosed, and obtains an output result (clustering result) based on the dynamic image, lung field area change rate, airway diameter narrowing rate, diaphragm velocity, pulmonary function test results, attribute information (age, sex, smoking history, height, weight, BMI), etc. However, with regard to related test information such as pulmonary function test results and attribute information, only information that has been acquired as information corresponding to the dynamic image to be diagnosed may be used.

[0132] (Study example 3) FIG. 14 is a conceptual diagram illustrating the process of creating a normal model using normal case data. The control unit 11 of the dynamic analysis server 10 constructs a normal model using machine learning (e.g., support vector machine, random forest, deep learning) based on dynamic images obtained by dynamic imaging of disease-free subjects, lung area change rate, airway diameter stenosis rate, diaphragm velocity, pulmonary function test results, attribute information (age, sex, smoking history, height, weight, BMI), etc. The control unit 11 automatically derives normal feature items that characterize the "normal model." For example, the control unit 11 identifies an item indicating the relationship between height and BMI as normal feature item 1, an item indicating the relationship between smoking history and pulmonary function test results as normal feature item 2, and an item indicating the relationship between airway diameter stenosis rate and pulmonary function test results as normal feature item 3.

[0133] When the dynamic analysis app is used from the hospital terminal 30, the dynamic analysis app determines whether the dynamic image to be diagnosed is normal or abnormal by calculating the degree of deviation from a normal model. The dynamic analysis app calculates the degree of deviation from the "normal model" of the information corresponding to the dynamic image to be diagnosed for each of normal feature items 1, 2, 3, .... Then, if any one of the deviation degrees corresponding to each normal feature item is greater than a predetermined threshold, it may be determined to be abnormal, or it may comprehensively evaluate each normal feature item and recalculate the overall deviation degree, and if the overall deviation degree is greater than a predetermined threshold, it may be determined to be abnormal. There are no particular limitations on the method for determining whether something is normal or abnormal.

[0134] (Study example 4) Figure 15 illustrates the concept of machine learning when weighting is changed for each hospital that provided a dataset. One attribute information included in the dataset is "facility imaging frequency." The dataset from Hospital A, which has a relatively high imaging frequency, is weighted higher than the dataset from Hospital B (e.g., Hospital A is weighted 0.8, and Hospital B is weighted 0.2), increasing the degree of reflection in the learning results. On the other hand, the dataset from Hospital B, which has a relatively low imaging frequency, is determined to be less familiar with imaging and has less diagnostic experience, and is therefore less reflected in the learning results. Learning Example 4 is similar to Learning Example 1 in that it uses dynamic images, lung field area change rate, airway diameter stenosis rate, diaphragm velocity, pulmonary function test results, attribute information (age, gender, smoking history, height, weight, BMI), etc. as inputs and diagnosis results (no disease, COPD, bronchial asthma, lung cancer, etc.) as outputs to generate a classifier.

[0135] Here, we have explained the case where weighting for machine learning is changed based on the "frequency of facility photography." However, weighting may also be applied to the data used for machine learning and deep learning based on the photography level of each photographer (number of photographs (experience), whether or not re-photographs are taken, the required photography time for each body part, the variance of the effectiveness of the photographed images (ROI deviation), etc.) and the presence or absence of specialists within the hospital.

[0136] The dynamic analysis algorithm may be learned outside the dynamic analysis server 10. For example, a service provider may obtain a data set to be used for learning from the dynamic analysis server 10, learn and evaluate the dynamic analysis algorithm, and then update the dynamic analysis app on the dynamic analysis server 10 to reflect the corrected dynamic analysis algorithm.

[0137] <Dynamic Atlas Update Method> Next, a method for updating the dynamic atlas application 20 will be described. First, an administrator of the service provider company uses the service provider company terminal 50 to acquire data from the VPN-connected behavior analysis server 10. The control unit 51 of the service provider company terminal 50 sends a data acquisition request for the behavior data set DB 152 to the behavior analysis server 10 via the communication unit 54, and acquires the data set from the behavior analysis server 10.

[0138] Next, a specialist (authoritative physician) selects data suitable for the dynamic atlas app 20 from the data sets acquired from the dynamic analysis server 10. The specialist uses the selected data to conduct clinical research, academic research, app development, and the like.

[0139] Next, the specialist determines whether or not to update the dynamic atlas application 20, i.e., whether or not the selected data is data that should be added to the dynamic atlas application 20. Note that instead of the specialist's determination, the dynamic atlas application 20 may be updated using a standard image (with less deviation) from among dynamic images whose normal / abnormal flag is "normal." If the specialist decides not to update the dynamic atlas application 20, the data set is acquired again from the dynamic analysis server 10 and data selection is performed.

[0140] If the specialist decides to update the dynamic atlas app 20, the administrator of the service provider company updates the dynamic atlas app 20 from the operation unit 52 of the service provider company terminal 50, as instructed by the specialist. The control unit 51 of the service provider company terminal 50 transmits an update instruction and update contents for the dynamic atlas app 20 to the dynamic analysis server 10 via the communication unit 54. In the behavior analysis server 10 , the control unit 11 updates the behavior atlas application 20 based on the information received from the service provider terminal 50 .

[0141] The Health and Disease Trends Information App 21 will also be updated under the supervision of medical specialists, just like the Trends Atlas App 20.

[0142] <App use permission decision process> 16 is a flowchart showing the application use permission determination process executed by the dynamic analysis server 10. The application use permission determination process is realized by software processing in cooperation between the CPU of the control unit 11 and a program stored in the storage unit 15.

[0143] First, on the hospital terminal 30, a user (medical worker) operates the operation unit 32 to select one of the apps provided by the VPN-connected dynamic analysis server 10 from a web browser, and the control unit 11 of the dynamic analysis server 10 accepts access to the selected app from the user via the communication unit 14 (step S41).

[0144] The control unit 11 of the dynamic analysis server 10 transmits display data for displaying a login screen to the hospital terminal 30 via the communication unit 14. On the hospital terminal 30, when the user operates the operation unit 32 to enter a user ID and password on the login screen displayed on the display unit 33, the control unit 31 transmits the entered user ID and password to the dynamic analysis server 10 via the communication unit 34.

[0145] The control unit 11 of the dynamic analysis server 10 acquires the user ID and password input from the operation unit 32 of the hospital terminal 30 via the communication unit 14. Then, the control unit 11 refers to the user management table 151 (see FIG. 3) stored in the memory unit 15, and confirms the user information of the user corresponding to the user ID input at the hospital terminal 30 (step S42).

[0146] Next, the control unit 11 determines whether the user's access is permitted (step S43). Specifically, the control unit 11 determines that the access is permitted if a record corresponding to the combination of the user ID and password entered from the hospital terminal 30 exists in the user management table 151 and "Access Permission" is "Yes."

[0147] If the user's access is permitted (step S43; YES), the control unit 11 determines whether a continuous use contract has been made for the selected application (step S44). Specifically, the control unit 11 refers to the user management table 151, and if the "continuous use contract" for the selected application is "yes" in the record corresponding to the user ID input from the hospital terminal 30, determines that a continuous use contract has been made for the selected application.

[0148] If a continuous use contract has not been made for the selected app (step S44; NO), the control unit 11 prompts the user of the hospital terminal 30 to select how to use the app (step S45). Specifically, the control unit 11 displays a selection screen on the display unit 33 of the hospital terminal 30 to select either one-time use or continuous use, and accepts either selection on the operation unit 32.

[0149] Here, if the user selects one-time use (step S46; one-time use), the control unit 11 makes the user of the hospital terminal 30 enter into a one-time use contract and performs processing to collect the fee (step S47). There are no particular restrictions on the payment method used by the user.

[0150] In step S46, if the user selects continued use (step S46; continued use), the control unit 11 has the user of the hospital terminal 30 enter into a continued use contract and processes monthly billing (step S48). The control unit 11 reflects in the user management table 151 that a continued use contract has been entered into for the selected app. There are no particular limitations on the payment method used by the user. The billing method may also be annual payment, pay-as-you-go billing, or the like.

[0151] After step S47 or step S48, if a continuous use contract has been made for the selected application in step S44 (step S44; YES), control unit 11 permits the user to use the application (step S49).

[0152] After step S49, or if the user's access is not permitted in step S43 (step S43; NO), the application use permission determination process ends.

[0153] When the dynamic analysis server 10 permits the use of data in response to access from the hospital terminal 30, the process is similar to the application use permission determination process.

[0154] <Processing when using the dynamic analysis app> 17 is a ladder chart showing the process when the dynamic analysis app is used, which is executed by the dynamic analysis server 10 and the hospital terminal 30. The process when the dynamic analysis app is used is a process that is performed after step S49 when the user accesses any of the diagnostic support apps 23 (dynamic analysis apps) in step S41 of the app usage permission determination process. Data communication is performed between the dynamic analysis server 10 and the hospital terminal 30 via a VPN.

[0155] When a user (medical worker) operates the operation unit 32 of the hospital terminal 30 to select a dynamic image (third dynamic image) to be diagnosed, the control unit 31 transmits the selected dynamic image to the dynamic analysis server 10 via the communication unit 34 (step S51). Here, attribute information and related test information corresponding to the dynamic image may be transmitted together with the dynamic image.

[0156] The control unit 11 of the dynamic analysis server 10 receives the dynamic image transmitted from the hospital terminal 30 via the communication unit 14, and inputs the dynamic image into the dynamic analysis application selected by the user at the time of access (step S52). When the control unit 11 receives the attribute information and related test information together with the dynamic image, it inputs these data sets into the dynamic analysis application.

[0157] Next, the control unit 11 acquires diagnostic support information generated by the dynamic analysis from the dynamic analysis application (step S53). Examples of the diagnostic support information include an analysis result report (including diagnostic results, classification information, etc.), annotations for the dynamic image, measurement results such as distance measurement performed on the dynamic image, and images to be referred to during diagnosis. Next, the control unit 11 transmits the diagnostic assistance information to the hospital terminal 30 via the communication unit 14 (step S54).

[0158] The control unit 31 of the hospital terminal 30 receives the diagnostic support information transmitted from the dynamic analysis server 10 via the communication unit 34 (step S55). In the hospital terminal 30, the diagnostic support information is displayed on the display unit 33 (step S56). The user diagnoses the dynamic image while referring to the diagnostic support information. This completes the process when using the dynamic analysis application.

[0159] In step S51, it is desirable to reduce unnecessary data communication volume by transmitting necessary data (uncompressed dynamic image data, compressed dynamic image data, still image data, etc.) depending on the dynamic analysis application being used.

[0160] Furthermore, although the above description has been given of a case where a dynamic analysis application (dynamic analysis algorithm) selected by the user is used for a dynamic image, multiple types of dynamic analysis may be performed on the dynamic image at the same time.

[0161] Also, when using the functions corresponding to each app in API format, similar to the processing when using the dynamic analysis app, dynamic images are sent from the hospital terminal 30 to the dynamic analysis server 10 (along with attribute information corresponding to the dynamic image, related test information, etc., if necessary), and the hospital terminal 30 receives diagnostic support information from the dynamic analysis server 10.

[0162] As described above, according to this embodiment, the dynamic analysis server 10 modifies (trains) the dynamic analysis algorithm based on data sets collected by multiple data collection servers 40, and therefore can provide highly accurate analysis results while ensuring homogeneity for dynamic images, which are more important to analyze than still images. Therefore, the dynamic analysis server 10 can provide users with more reliable dynamic analysis results (such as a determination of whether a dynamic image is normal or abnormal, or a diagnosis that can be read from the dynamic image), thereby assisting in diagnosis. In particular, for diagnostic support applications, machine learning and deep learning can be performed using large amounts of data that are updated daily, making it easier to increase the types of analysis targets and improve the accuracy of analysis.

[0163] Furthermore, new applications and data can be provided at any time while collecting data using the dynamic analysis server 10. By providing various applications on the dynamic analysis server 10, various applications can be managed centrally, and uniform information can be provided to users at each hospital using the latest applications.

[0164] Furthermore, even after the dynamic analysis server 10 begins to be used in the dynamic analysis system 100, the accuracy of the dynamic analysis application can be updated as needed (post-market learning).

[0165] Furthermore, since the tests related to the information contained in the data set may differ for each data collection server 40, it is possible to provide analysis results using a dynamic analysis algorithm that has been trained to take into account tests that are not performed at the hospital to which the user himself / herself belongs.

[0166] In addition, by using information obtained from pulmonary function tests, cardiac function tests, scintigraphy tests, CT scans, plain X-ray tests, MRI tests, and ultrasound tests as information other than the dynamic images included in the dataset, the dynamic analysis algorithm can be trained by taking into account the results obtained in other tests for the subjects who were the subject of dynamic imaging.

[0167] Furthermore, among the dynamic analysis algorithms, the analysis target specific algorithms can perform ventilation analysis, blood flow analysis, orthopedic analysis, diaphragm measurement, etc. on dynamic images. Furthermore, among the dynamic analysis algorithms, the disease diagnosis support algorithm can determine whether or not a dynamic image is associated with a predetermined disease (COPD, interstitial pneumonia, etc.) and provide information used for this determination.

[0168] In addition, a dynamic analysis application (dynamic analysis algorithm) trained based on a dataset containing correct labels can output information regarding the diagnosis of dynamic images (determination results of whether the image is normal or abnormal, diagnosis name, etc.) as diagnostic support information.

[0169] In addition, a dynamic analysis application (dynamic analysis algorithm) trained based on a dataset that does not include a correct label can output classification information about dynamic images as diagnostic support information.

[0170] The above-described embodiments are merely examples of the program, dynamic analysis system, and dynamic analysis device according to the present invention, and are not intended to limit the scope of the present invention. The detailed configuration and operation of each device constituting the system may also be modified as appropriate without departing from the spirit of the present invention.

[0171] For example, users of the services provided by the dynamic analysis server 10 are not limited to medical staff at hospitals, but may also be researchers affiliated with research institutions such as universities, software developers, and the like.

[0172] In addition, in the above embodiment, the communication unit 14 (receiving unit) of the dynamic analysis server 10 receives the data sets transmitted from each data collection server 40 (data collection device) via VPN, but it is also possible to receive data sets from some data collection devices via VPN and from other data collection devices by a method other than VPN. The communication unit 14 of the dynamic analysis server 10 may receive data sets from each data collection device via a dedicated line. The communication unit 14 of the dynamic analysis server 10 may receive data sets from some data collection devices via a dedicated line, and from other data collection devices via a method other than a dedicated line. Furthermore, the communication unit 14 of the dynamic analysis server 10 may receive data sets from some data collection devices via a VPN, and from other data collection devices via a dedicated line.

[0173] Furthermore, each functional unit constituting the dynamic analysis server 10 may be separated into multiple devices. That is, the dynamic analysis server 10 may be composed of multiple devices. In this case, data communication between the multiple devices required to realize each function of the dynamic analysis server 10 is performed appropriately. Furthermore, the dynamic analysis server 10 may be constructed on a cloud and realized by a cloud server that may include multiple servers, storage, etc.

[0174] In addition, in the above embodiment, uniform analysis results are provided to each hospital, but it is also possible to provide each medical facility with an app that matches the equipment and conditions used at that facility, and change the app provided depending on the facility's situation (equipment information, trends in imaging data). Providing a more suitable app for each facility may improve the accuracy of analysis. In addition, data obtained at other facilities may also be trained using transfer learning to improve the performance of dynamic analysis.

[0175] To eliminate the impact of unintended changes to functions when performing machine learning, for example, when updating an app used at Hospital A, the app may be updated by learning using only data obtained by the same diagnostic device as Hospital A and data containing information on diagnostic results from specialists. This can improve the learning effect by eliminating data obtained by a different diagnostic device or data based on diagnoses by doctors who are not accustomed to making diagnoses.

[0176] Furthermore, in addition to providing various applications (such as the diagnostic support application 23 and the dynamic atlas application 20) on the dynamic analysis server 10, frequently used applications at each hospital may be downloaded onto the data collection server 40, and the applications on the data collection server 40 may be run from each hospital terminal 30. This method is also effective in dealing with poor connection to the dynamic analysis server 10.

[0177] Furthermore, before selecting an app to be used, the hospital terminal 30 may have a recommendation function in which, when a dynamic image of a diagnostic target is captured, the dynamic image is sent to the dynamic analysis server 10, and the control unit 11 of the dynamic analysis server 10 proposes processing suitable for the dynamic image. For example, the control unit 11 of the dynamic analysis server 10 may perform processing such as opening a recommended diagnostic support app 23 on the hospital terminal 30, presenting the processing results of the diagnostic support app 23 to the hospital terminal 30, or proposing an additional imaging order to the hospital terminal 30.

[0178] The programs for executing the processes in each device may be stored on a portable recording medium, and a carrier wave may be used as a medium for providing program data via a communication line. [Explanation of symbols]

[0179] 10 Dynamic analysis server 11 Control section 14 Communications Department 15 Storage section 20 Dynamic Atlas App 21 Health and disease trend information app 22 Statistical Analysis Apps 23 Diagnostic support app 30 Hospital terminals 31 Control Unit 32 Operation section 33 Display section 34 Communications Department 40 Data Collection Servers 41 Control Unit 44 Communications Department 45 Storage section 50 Service provider terminal 100 Dynamic Analysis System 151 User Management Table 152 Dynamics Dataset DB 153 Data Management Table 154 Image storage area

Claims

1. A program for correcting a dynamic analysis algorithm for performing dynamic analysis on a dynamic image obtained by performing dynamic radiography on a subject, On the computer, receiving, from a first data collection device, a first anonymized dataset including a first dynamic image obtained by performing dynamic radiography on a first subject and information obtained by a first examination other than dynamic radiography on the first subject; receiving, from a second data collection device, a second anonymized dataset including second dynamic images obtained by performing dynamic radiography on a second subject and information obtained by a second examination other than dynamic radiography on the second subject; modifying the kinetic analysis algorithm based on the first data set and the second data set; Execute the first test and the second test include at least one of a pulmonary function test, a cardiac function test, a scintigraphy test, a plain X-ray test, and an ultrasound test; A program characterized by.

2. The pulmonary function test is a spirometry test. The program according to claim 1 .

3. The cardiac function test is an electrocardiogram test, 3. The program according to claim 1 or 2,

4. The scintigraphy test is a lung scintigraphy test. The program according to any one of claims 1 to 3,

5. The information obtained by the pulmonary function test includes vital capacity, total lung capacity, functional residual capacity, residual volume, RV / TLC, expiratory reserve volume, and forced expiratory volume in one second. The program according to any one of claims 1 to 4,

6. the first test and the second test are different; The program according to any one of claims 1 to 5,

7. the first dataset and the second dataset include ground truth labels; the correct label includes at least one of an interpretation result for the dynamic image and a diagnosis result based on the interpretation result; The program according to any one of claims 1 to 6,

8. The dynamic analysis algorithm includes at least one of an analysis subject specific algorithm and a disease diagnosis support algorithm; The program according to any one of claims 1 to 7,

9. receiving the first data set and the second data set via a dedicated line; The program according to any one of claims 1 to 8,

10. receiving the first data set and the second data set via a VPN (Virtual Private Network); The program according to any one of claims 1 to 8,

11. receiving the first data set and the second data set, the first data set being received via a dedicated line and the second data set being received via a VPN; The program according to any one of claims 1 to 8,

12. The computer, inputting a third dynamic image obtained by performing dynamic radiography on a third subject; a process of outputting diagnostic support information based on the modified dynamic analysis algorithm for the third dynamic image; To execute the The program according to any one of claims 1 to 11,

13. the first dataset and the second dataset include ground truth labels; the correct label includes at least one of an interpretation result for the dynamic image and a diagnosis result based on the interpretation result; In the process of outputting the diagnostic assistance information, information relating to a diagnosis of a dynamic image is output as the diagnostic assistance information; The program according to claim 12,

14. In the process of outputting the diagnostic assistance information, classification information related to dynamic images is output as the diagnostic assistance information; The program according to claim 12,

15. A dynamic analysis device that executes the program according to any one of claims 1 to 14; the first data collection device; the second data collection device; To have A dynamic analysis system characterized by:

16. A dynamic analysis device that executes the program according to any one of claims 12 to 14; a hospital terminal that transmits the third dynamic image to the dynamic analysis device and receives the diagnostic support information from the dynamic analysis device; To have A dynamic analysis system characterized by:

17. a storage unit for storing a dynamic analysis algorithm for performing dynamic analysis on a dynamic image obtained by performing dynamic imaging on a subject using radiation; receiving, from a first data collection device, a first anonymized dataset including a first dynamic image obtained by performing dynamic radiography on a first subject and information obtained by a first examination other than dynamic radiography on the first subject; a receiving unit that receives, from a second data collection device, an anonymized second dataset including second dynamic images obtained by performing dynamic radiography on a second subject and information obtained by a second examination other than dynamic radiography on the second subject; a learning unit that modifies the dynamic analysis algorithm stored in the storage unit based on the first data set and the second data set; Equipped with the first test and the second test include at least one of a pulmonary function test, a cardiac function test, a scintigraphy test, a plain X-ray test, and an ultrasound test; A dynamic analysis device characterized by the above.

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