Medical system and medical information processing device

Abstract

To provide a novel technique for noninvasively detecting the state of a circulatory system of a patient.SOLUTION: A medical system 1 of an exemplary mode comprises a data acquisition unit 10 and a data processing unit 20. The data acquisition unit 10 acquires data from an eyeground of a patient by using at least one optical method. The data processing unit 20 processes the data acquired by the data acquisition unit 10 in order to generate information on the circulatory system of the patient.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] This invention relates to a medical system and a medical information processing apparatus.

Background Art

[0002] Symptoms of diseases and signs of exacerbation are complex, and various techniques have been developed to detect them. For example, Patent Document 1 discloses a technique for determining the risk of infectious diseases without using advanced medical knowledge, which determines the risk from the presence or absence of abnormalities in each of arterial oxygen saturation, body temperature, and heart rate.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] One object of this invention is to provide a new technique for non-invasively detecting the state of a patient's circulatory system.

Means for Solving the Problems

[0005] A medical system according to some exemplary aspects includes a data acquisition unit and a data processing unit. The data acquisition unit acquires data from a patient's fundus using at least one optical method. The data processing unit processes the data acquired by the data acquisition unit to generate information regarding the patient's circulatory system.

[0006] In some exemplary aspects, the information regarding the circulatory system includes information regarding the tendency to form blood clots.

[0007] In some exemplary aspects, the information regarding the tendency to form blood clots includes information regarding the properties of blood.

[0008] In some exemplary embodiments, the information regarding the blood properties includes information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolytic system.

[0009] In some exemplary embodiments, the information regarding the circulatory system includes information regarding thrombus symptoms.

[0010] In some exemplary embodiments, the information regarding thrombus symptoms includes information indicating the distribution of blood flow velocity within a blood vessel.

[0011] In some exemplary embodiments, the information regarding thrombus symptoms includes information regarding a structure formed within a blood vessel.

[0012] In some exemplary embodiments, the information regarding the circulatory system includes information regarding the state of the circulatory system associated with an infectious disease.

[0013] In some exemplary embodiments, the information regarding the circulatory system includes at least one of information indicating a state regarding sepsis, information indicating a state regarding disseminated intravascular coagulation syndrome (DIC), information indicating a state regarding thrombus, and information indicating a state regarding vascular occlusion.

[0014] In some exemplary embodiments, the at least one optical method includes at least one of optical coherence tomography blood flow measurement (OCT blood flow measurement), optical coherence tomography angiography (OCT-A), and color fundus photography.

[0015] In some exemplary embodiments, the at least one optical method includes OCT blood flow measurement, and the data processing unit generates information regarding the blood coagulation fibrinolytic system based at least on the blood flow data obtained by the OCT blood flow measurement.

[0016] In some exemplary embodiments, the data acquisition unit includes an OCT device and a calculation unit. The OCT device applies an optical coherence tomography (OCT) scan to the fundus of a patient to collect data. The calculation unit calculates a blood flow velocity and a blood vessel diameter based at least on the data collected by the OCT device. The data processing unit generates information regarding the blood coagulation fibrinolysis system based at least on the blood flow velocity and the blood vessel diameter calculated by the calculation unit.

[0017] In some exemplary embodiments, the data processing unit includes a WSR calculation unit. The WSR calculation unit calculates a wall shear rate (WSR) based at least on the blood flow velocity and the blood vessel diameter.

[0018] In some exemplary embodiments, the data processing unit includes a storage unit and a WSS calculation unit. The storage unit stores blood viscosity information acquired in advance. The WSS calculation unit calculates a wall shear stress (WSS) based at least on the wall shear rate and the blood viscosity information.

[0019] In some exemplary embodiments, the data acquisition unit includes an OCT device and a blood flow information generation unit. The OCT device repeatedly applies an optical coherence tomography (OCT) scan to a predetermined region of the fundus of a patient to collect time-series data. The blood flow information generation unit generates blood flow information representing the spatial distribution and the temporal change of the blood flow velocity based at least on the time-series data collected by the OCT device. The data processing unit generates information regarding the blood coagulation fibrinolysis system based at least on the blood flow information generated by the blood flow information generation unit.

[0020] In some exemplary embodiments, the data processing unit generates information regarding a structure formed in a blood vessel based at least on the blood flow information.

[0021] In some exemplary embodiments, the data processing unit includes a WSR information generation unit. The WSR information generation unit generates WSR information representing the spatial distribution and the temporal change of the wall shear rate (WSR) based at least on the blood flow information.

[0022] In some exemplary embodiments, the data processing unit generates information regarding a structure formed in a blood vessel based at least on blood flow information and WSR information.

[0023] In some exemplary embodiments, the data processing unit includes a storage unit and a WSS information generation unit. The storage unit stores blood viscosity distribution information acquired in advance. The WSS information generation unit generates WSS information representing the spatial distribution and temporal change of wall shear stress (WSS) based at least on WSR information and blood viscosity distribution information.

[0024] In some exemplary embodiments, the data processing unit generates information regarding a structure formed in a blood vessel based at least on blood flow information and WSS information.

[0025] In some exemplary embodiments, the data processing unit includes a first inference processing unit. The first inference processing unit performs an inference process that takes, as an input, data acquired from a patient's fundus oculi by the data acquisition unit and outputs information regarding the patient's circulatory system, using a first trained model constructed by machine learning using first training data including the first data acquired from the fundus oculi using at least one optical method and diagnostic result data.

[0026] In some exemplary embodiments, the data processing unit includes a second inference processing unit. The second inference processing unit performs an inference process that takes, as an input, data generated by processing data acquired from a patient's fundus oculi by the data acquisition unit and outputs information regarding the patient's circulatory system, using a second trained model constructed by machine learning using second training data including the second data generated by processing the first data acquired from the fundus oculi using at least one optical method and diagnostic result data.

[0027] A medical system according to some exemplary embodiments further includes a transmission unit. The transmission unit transmits information regarding the circulatory system generated by the data processing unit to a doctor terminal located at a remote position with respect to the data acquisition unit.

[0028] Medical systems according to some exemplary embodiments further include a doctor terminal.

[0029] Medical systems according to some exemplary embodiments further include an operation unit for remotely operating a data acquisition unit.

[0030] Medical information processing apparatuses according to some exemplary embodiments include a data reception unit and a data processing unit. The data reception unit receives data acquired from the fundus of a patient using at least one optical method. The data processing unit processes the data received by the data reception unit to generate information regarding the patient's circulatory system.

[0031] Medical information processing apparatuses according to some exemplary embodiments further include a first transmission unit. The first transmission unit transmits the information regarding the circulatory system generated by the data processing unit to a doctor terminal located at a remote position with respect to the location where the data was acquired.

[0032] Medical systems according to some exemplary embodiments include a medical information processing apparatus according to an exemplary embodiment and a doctor terminal.

[0033] Medical systems according to some exemplary embodiments further include a data acquisition device and a second transmission unit. The data acquisition device acquires data from the fundus of a patient using at least one optical method. The second transmission unit transmits the data acquired by the data acquisition device to the medical information processing apparatus. The data reception unit receives the data transmitted by the second transmission unit. The data processing unit processes the data transmitted by the second transmission unit and received by the data reception unit to generate information regarding the patient's circulatory system.

Advantages of the Invention

[0034] According to an exemplary embodiment, it is possible to provide a new technique for non-invasively detecting the state of a patient's circulatory system.

Brief Description of the Drawings

[0035]

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Modes for Carrying Out the Invention

[0036] In the present disclosure, some exemplary aspects regarding a medical system and a medical information processing apparatus will be described. A person having ordinary knowledge in this technical field will understand that the aspects according to the present disclosure provide various modifications and equivalents, and that the aspects according to the present disclosure or their modifications or equivalents provide other various aspects such as a medical method, a system control method, an apparatus control method, a program, a recording medium, and the like.

[0037] Some exemplary aspects generate information regarding a patient's circulatory system by processing, with a computer, data acquired from the fundus of a patient by at least one optical method (optical modality). This computer processing may include inference. This inference may be executed, for example, by an algorithm using a learned model (inference model) constructed by machine learning, an algorithm not using a learned model, or a combination thereof.

[0038] The data subjected to computer processing in some exemplary aspects may be data acquired by any ophthalmic examination, and may be, for example, data acquired by any ophthalmic modality device. The ophthalmic modality device may be, for example, an optical coherence tomography (OCT) device, a fundus camera, a scanning laser ophthalmoscope, a slit lamp microscope, a surgical microscope, or the like. In some exemplary aspects, the optical coherence tomography device is used, for example, for optical coherence tomography blood flow measurement, optical coherence tomography angiography (OCT-A), or the like. In some exemplary aspects, fundus imaging devices such as a fundus camera, a scanning laser ophthalmoscope, a slit lamp microscope, a surgical microscope, or the like are used, for example, for color fundus imaging. Note that the data subjected to computer processing is not limited to these, and may further include, for example, other types of examination data, electronic medical record data, interview data, patient background information (age, treatment history, medical history, medication history, surgical history, etc.).

[0039] Exemplary embodiments are configured to generate predetermined information regarding a patient's circulatory system from such data. The information generated by some exemplary embodiments may include at least one of quantitative information and qualitative information, and may include, for example, any of the following information: information regarding thrombosis tendency; information regarding thrombus symptoms; information regarding the state of the circulatory system associated with an infectious disease; information indicating a state regarding sepsis; information indicating a state regarding disseminated intravascular coagulation syndrome (DIC); information indicating a state regarding a thrombus; information indicating a state regarding vascular occlusion.

[0040] Information regarding thrombosis tendency is information indicating the tendency for thrombi to form in a patient's circulatory system (intravascular, intracardiac), and includes, for example, information regarding thrombosis risk. The information regarding thrombosis tendency may include either information regarding blood properties or information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolytic system. Information regarding blood properties includes information regarding the nature of the blood and / or information regarding the state of the blood. Information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolytic system includes information indicating changes in blood properties resulting from activation of the system that causes blood to coagulate (coagulation system, blood coagulation factors), and / or information indicating changes in blood properties resulting from activation of the system that dissolves thrombi and blood clots (fibrinolytic system). The information regarding thrombosis tendency may include, for example, viscosity, wall shear stress, wall shear rate, the amount or ratio of a specific component, the ratio between specific components, information indicating changes in any of these, information indicating the distribution of any of these, etc.

[0041] Information regarding thrombus symptoms is information regarding symptoms caused by a thrombus, and may include, for example, either information indicating the distribution of blood flow velocity within a blood vessel or information regarding the structure formed within the blood vessel. The distribution of blood flow velocity within the blood vessel may be, for example, any one or any combination of two or more of a one-dimensional distribution, a two-dimensional distribution, a three-dimensional distribution, and a temporal distribution. The structure formed within the blood vessel may be, for example, a white thrombus, a red thrombus, a mixed thrombus, a hyaline thrombus, something related to the formation mechanism of any of these (e.g., intermediate products), etc.

[0042] Information regarding the cardiovascular state associated with an infectious disease includes information regarding diseases or conditions associated with or caused by the infectious disease, such as vascular inflammation, thrombosis tendency, blood coagulation tendency, sepsis, DIC, pneumonia, lymphadenitis, lymphangitis, etc. The infectious disease to be targeted may be any viral infectious disease, any bacterial infectious disease, or any fungal infectious disease, and may be, for example, Coronavirus Disease 2019 (COVID-19) that pandemic in 2020, Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), influenza, infective endocarditis, etc.

[0043] Sepsis is a very severe state caused by the spread of an infectious disease throughout the body, and causes circulatory shock, DIC, multiple organ failure, etc. Information indicating the state regarding sepsis includes, for example, information regarding symptoms such as inflammation and circulatory insufficiency caused by sepsis.

[0044] DIC is a syndrome in which the blood coagulation reaction that should originally occur only at the bleeding site occurs disorderly in the blood vessels throughout the body. As the pathological condition of DIC, marked coagulation activation continuously occurs in the blood vessels throughout the body and multiple microthrombi develop. As it progresses, it causes consumptive coagulation disorder from organ damage due to microcirculation disorder and bleeding occurs. In addition, since fibrinolysis activation also occurs along with coagulation activation, excessive fibrinolysis of thrombi occurs, promoting bleeding. Information indicating the state regarding disseminated intravascular coagulation syndrome (DIC) includes, for example, information indicating the above pathological conditions (enhancement of the coagulation system, enhancement of the fibrinolysis system, thrombus, bleeding, etc.) of DIC.

[0045] Information indicating the state regarding a thrombus may be any information regarding a thrombus existing or possibly existing in the cardiovascular system (within blood vessels, within the heart), and includes, for example, the presence or absence of a thrombus, the degree of a thrombus, the distribution of a thrombus, the number of thrombi, the probability of thrombus formation, etc.

[0046] Information indicating a state related to vascular occlusion may be any information related to vascular occlusion occurring or potentially occurring in the circulatory system, and includes, for example, the presence or absence of vascular occlusion, the degree of vascular occlusion, the distribution of vascular occlusion sites, the number of vascular occlusion sites, the probability of vascular occlusion occurring, and the like.

[0047] Thus, exemplary embodiments enable non-invasively detecting the state of a patient's circulatory system by, for example, generating information regarding the patient's circulatory system based on data obtained from the fundus of the patient using any of the optical modalities exemplified above. Some exemplary embodiments can generate information regarding one or more of the following: thrombotic tendency (e.g., blood properties and / or changes in blood properties due to enhanced blood coagulation-fibrinolysis system), thrombotic symptoms (e.g., blood flow velocity distribution and / or intra-vascular structures), the state and / or state changes of the circulatory system associated with an infectious disease, sepsis, DIC, thrombus, vascular occlusion, matters similar to any of these, matters derived from any of these, and matters related to the mechanism of any of these. Note that the types of information that can be generated by exemplary embodiments are not limited to these, and may be any type of information that can be generated (e.g., derived, estimated, etc.) by the combination of the optical modality employed and the data processing employed.

[0048] Some exemplary aspects have been devised in consideration of the background as described below, and can achieve corresponding effects. Medical workers such as doctors and nurses are exposed to the risk of nosocomial infection. For example, in the pandemic of coronavirus disease 2019 (COVID-19) that occurred in 2020, the risk of infection to medical workers became a major problem, such as cluster infections occurring in medical institutions where a large number of patients flocked. In addition, the increase in the risk of infection to medical workers can occur not only during infectious disease epidemics but also when disasters or major accidents occur. Generally, to reduce the risk of infection, it is important to ensure sufficient distance between people, so-called social distancing, but it is not easy to achieve this in standard medical care. For example, when performing an examination, doctors and the like often stay right next to the patient to perform the treatment.

[0049] Some exemplary aspects may be configured to be able to provide information generated by computer processing of data acquired by an optical modality to a doctor terminal at a remote location. In addition, some exemplary aspects may be configured to be able to operate an inspection device (optical modality device) or a computer from a remote location. According to these configurations, it becomes possible to use the data obtained from an inspection that could not be performed unless one was right next to the patient for diagnosis. In other words, according to some exemplary aspects, it is possible to maintain social distancing between the patient and the medical worker, and to non-invasively and highly accurately detect complex physiological events such as symptoms and signs of exacerbation.

[0050] Here, the "remote location" only needs to be a positional relationship that can ensure social distancing between the patient and the medical staff. For example, the doctor terminal may be installed in a different room from the inspection device, or may be installed in a different facility from the inspection device. Also, the device (operation device, operation unit) for remotely operating the inspection device may be installed in a different room from the inspection device, or may be installed in a different facility from the inspection device. Note that when the inspection is carried out under a sufficient infectious disease prevention system such as when wearing full protective clothing, it is not necessary to ensure social distancing.

[0051] The matters described in the documents cited in this specification and other arbitrary known technologies can be modified into exemplary modes. This modification may be any of addition, combination, substitution, deletion, omission, and other processing, for example.

[0052] At least a part of the functions of the elements described in this disclosure may be implemented using circuitry or processing circuitry. The circuitry or processing circuitry may be a general-purpose processor, a dedicated processor, an integrated circuit, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), a programmable logic device (e.g., SPLD (Simple Programmable Logic Device), CPLD (Complex Programmable Logic Device), FPGA (Field Programmable Gate Array), conventional circuitry, and any combination thereof, configured and / or programmed to execute at least a part of the disclosed functions. A processor may be regarded as a processing circuitry or circuitry including transistors and / or other circuitry. In this disclosure, terms such as circuitry, circuit, computer, processor, unit, means, part, or the like may include hardware that executes at least a part of the disclosed functions and / or hardware programmed to execute at least a part of the disclosed functions. The hardware may be the hardware disclosed herein, or may be known hardware programmed and / or configured to execute at least a part of the disclosed functions. When the hardware is a processor that may be regarded as a certain type of circuitry, terms such as circuitry, circuit, computer, processor, unit, means, part, or the like may be a combination of hardware and software, and this software may be used to configure the hardware and / or the processor.

[0053] The exemplary aspects described below may be arbitrarily combined. For example, it is possible to at least partially combine two or more exemplary aspects.

[0054] <Configuration of a Medical System> Several examples of the configuration of a medical system in an exemplary embodiment will be described. The exemplary medical system 1 shown in FIG. 1 includes a data acquisition unit 10, a data processing unit 20, and an output unit 30. The medical system 1 may further include an operating device 2.

[0055] In a typical example, the data acquisition unit 10 and the data processing unit 20 are connected via a communication line. This communication line may form a network within a medical institution, for example, or may form a network spanning multiple facilities. The communication technology applied to this communication line may be arbitrary and may be any of various known communication technologies such as wired communication, wireless communication, and short-range communication. The connection mode between the data processing unit 20 and the output unit 30 may be the same. Alternatively, the data processing unit 20 and the output unit 30 may be functional units mounted on the same computer.

[0056] The operating device 2 is used for medical staff to remotely operate the data acquisition unit 10 (inspection device, optical modality device). Also, the operating device 2 is used for medical staff (examiner) to provide instructions and the like to a patient (subject) being examined using the data acquisition unit 10. Also, the operating device 2 may be used to remotely operate the data processing unit 20. The operating device 2 includes, for example, a computer, an operation panel, and the like.

[0057] The data acquisition unit 10 is configured to acquire data from the fundus of a patient using at least one optical modality. The data acquisition unit 10 includes, for example, any optical fundus imaging modality device such as an optical coherence tomography device, a fundus camera, a scanning laser ophthalmoscope, a slit lamp microscope, a surgical microscope, and the like. The data acquisition unit 10 may further be able to acquire, for example, other types of examination data, electronic medical record data, interview data, patient background information, and the like.

[0058] An optical coherence tomography apparatus and / or a fundus camera may be an apparatus in which various imaging preparation operations are automated, as described in, for example, Japanese Patent Application Laid-Open No. 2020-44027. The imaging preparation operations are operations executed to adjust imaging conditions, and examples thereof include alignment adjustment, focus adjustment, optical path length adjustment, polarization adjustment, light quantity adjustment, and the like. Further, operations for automatically maintaining good imaging conditions achieved by the imaging preparation operations may be executable. Examples of such operations include automatic alignment adjustment (tracking) in accordance with eye movement and automatic optical path length adjustment (Z-lock) in accordance with eye movement. These automatic operations are effective, for example, in an examination that is performed without an examiner being present.

[0059] Data obtained by an optical coherence tomography apparatus (optical coherence tomography data) may be, for example, at least one of three-dimensional image data obtained by applying a three-dimensional scan to the fundus, projection image data of the three-dimensional image data, optical coherence tomography angiography image data, and optical coherence tomography blood flow data.

[0060] Optical coherence tomography angiography is an optical modality for depicting blood vessels using motion contrast technology, and it is possible to depict fine blood vessels. Optical coherence tomography angiography image data is obtained using, for example, an optical coherence tomography apparatus described in Japanese Patent Application Laid-Open No. 2019-58495, Japanese Patent Application Laid-Open No. 2019-154988, and the like.

[0061] Optical coherence tomography blood flow measurement is an optical modality for measuring the blood flow state (hemodynamics). Optical coherence tomography blood flow data is acquired using, for example, an optical coherence tomography device described in JP-A-2019-54994, JP-A-2020-48730, etc. In some exemplary embodiments, optical coherence tomography blood flow measurement can obtain, as optical coherence tomography blood flow data, blood flow velocity, blood flow volume, blood vessel diameter, waveform data representing the time-series change (temporal change, time-dependent change) of blood flow velocity, waveform data representing the time-series change of blood flow volume, etc. The waveform data is typically a time-series change graph of blood flow velocity represented by a two-dimensional coordinate system with time on the horizontal axis and blood flow velocity on the vertical axis. Note that the optical modality used for fundus blood flow measurement is not limited to optical coherence tomography blood flow measurement, and may be, for example, laser speckle flowgraphy (LSFG) described in WO2008 / 069062, etc.

[0062] Examples of image data (fundus camera image data) that can be acquired by a fundus camera include, for example, color fundus image data, infrared fundus image data, fluorescence angiography fundus image data (fluorescein angiography image data, indocyanine green angiography image data, etc.). In some exemplary embodiments, color fundus image data is acquired using a fundus camera.

[0063] A scanning laser ophthalmoscope may be, for example, a device described in JP-A-2014-226156. Examples of image data (scanning laser image data) that can be acquired by a scanning laser ophthalmoscope include, for example, color fundus image data, monochromatic fundus image data, fluorescence angiography fundus image data, etc. In some exemplary embodiments, color fundus image data is acquired using a scanning laser ophthalmoscope.

[0064] A slit lamp microscope may be an apparatus effective for remote imaging, for example, as described in Japanese Patent Application Laid-Open No. 2019-213734. The image data acquired by the slit lamp microscope may be, for example, at least one of color fundus image data, anterior segment cross-sectional image data, and anterior segment three-dimensional image data. In some exemplary embodiments, color fundus image data is acquired using a slit lamp microscope.

[0065] A surgical microscope may be an apparatus effective for remote surgery, for example, as described in Japanese Patent Application Laid-Open No. 2002-153487. In some exemplary embodiments, color fundus image data is acquired using a surgical microscope.

[0066] In this embodiment, at least one of the inspection devices (for example, an optical coherence tomography device, a fundus camera, etc.) included in the data acquisition unit 10 may be capable of remote operation and / or remote control.

[0067] For example, considering the risk of infection to medical staff, the examination room where examinations using the inspection device are performed and the operation room where the operation of this inspection device is performed can be separated. In addition to the inspection device, the examination room is provided with a speaker and a display for outputting instructions (such as voice, image, video, etc.) of the operator in the operation room, a video camera for photographing the examinee (patient) in the examination room, a microphone for inputting the voice of the examinee, a computer connected to the inspection device, and the like.

[0068] On the other hand, the operation room is provided with an operation device 2 for remotely operating the inspection device. The operation device 2 is provided with a computer, an operation panel, a display, a video camera, a microphone, and the like. The computer executes processing for remote operation. The computer is connected to the inspection device in the examination room. The operation panel, the video camera, and the microphone are used for inputting instructions to the examinee. The display displays the data acquired by the inspection device and information for remote operation (such as a screen, information from the examination room, etc.).

[0069] With such a configuration, an operator (medical staff) in the operation room can remotely operate the inspection device in the inspection room using, for example, an application programming interface (API), and can send instructions to the subject using a videophone or the like. As a result, the subject can undergo the inspection alone according to the instructions of the operator at a remote location, and as a result, it is possible to significantly reduce the risk of infection from the subject to the operator.

[0070] In order to more suitably perform the inspection of a single patient (subject), the inspection device (described above) with automated preparation operations can be used. In this case, it is considered that the inspection can be performed without requiring instructions from the operator. In some cases, it may not be necessary to arrange an assistant (such as an operator). However, since it is also assumed that it may be difficult for some patients to undergo the inspection alone, for example, an assistant may be waiting at a remote location, or an assistant may monitor the inspection status from a remote location. Note that the assistant (such as an operator) who sends instructions to the patient may be an anthropomorphic computer system (typically, an automatic response system using artificial intelligence technology).

[0071] The data processing unit 20 executes various data processes. The data processing unit 20 of this embodiment is configured to process the data acquired by the data acquisition unit 10 in order to generate information regarding the circulatory system of the patient.

[0072] The information generated by the data processing unit 20 of this embodiment may be, for example, at least one of the following information: information regarding the tendency of thrombosis (information regarding the blood properties and / or information indicating changes in blood properties due to the enhancement of the blood coagulation fibrinolytic system); information regarding thrombus symptoms (a method of indicating the blood flow velocity distribution in the blood vessel and / or information regarding the structure formed in the blood vessel); information regarding the state (and / or state change) of the circulatory system associated with an infectious disease; information indicating the state regarding sepsis; information indicating the state regarding DIC; information indicating the state regarding thrombus; information indicating the state regarding vascular occlusion.

[0073] Some examples of the processes executed by the data processing unit 20 will be described in the aspects described below. The data processing unit 20 may or may not use a learned model (inference model) constructed by machine learning.

[0074] FIG. 2 shows an example of a data structure for processing (recording, transmitting, etc.) data generated by the data processing unit 20. The data structure 100 in this example includes a thrombosis tendency data section 110, a thrombosis symptom data section 120, an infectious disease associated data section 130, a sepsis data section 140, a DIC data section 150, a thrombus data section 160, and a vascular occlusion data section 170.

[0075] The thrombosis tendency data section 110 is an area (such as a folder) where information regarding the thrombosis tendency generated by the data processing unit 20 is recorded. The thrombosis tendency data section 110 includes a blood property data section 111. The blood property data section 111 is an area where information regarding the blood properties generated by the data processing unit 20 is recorded. The blood property data section 111 includes a blood property change data section 112. The blood property change data section 112 is an area where information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolytic system generated by the data processing unit 20 is recorded.

[0076] The thrombosis symptom data section 120 is an area where information regarding the thrombosis symptoms generated by the data processing unit 20 is recorded. The thrombosis symptom data section 120 includes a blood flow velocity distribution data section 121 and an intravascular structure data section 122. The blood flow velocity distribution data section 121 is an area where information indicating the distribution of blood flow velocity in the blood vessels generated by the data processing unit 20 is recorded. The intravascular structure data section 122 is an area where information regarding the structures formed in the blood vessels generated by the data processing unit 20 is recorded.

[0077] The infectious disease associated data section 130 is an area where information regarding the state of the circulatory system associated with an infectious disease generated by the data processing section 20 is recorded. The sepsis data section 140 is an area where information indicating the state regarding sepsis generated by the data processing section 20 is recorded. The DIC data section 150 is an area where information indicating the state regarding DIC generated by the data processing section 20 is recorded. The thrombus data section 160 is an area where information indicating the state regarding a thrombus generated by the data processing section 20 is recorded. The vascular occlusion data section 170 is an area where information indicating the state regarding vascular occlusion generated by the data processing section 20 is recorded.

[0078] In some exemplary embodiments, the data structure 100 includes at least one of the above-described data sections 110 to 170. In some exemplary embodiments, the data structure 100 may include data sections other than the above-described data sections 110 to 170. For example, the data structure 100 may include a fundus data section in which data acquired from the fundus by the data acquisition section 10 is recorded, a processed data section in which data obtained by subjecting the data acquired from the fundus by the data acquisition section 10 to a predetermined process is recorded, an arbitrary data section in which any type of data is recorded, and the like. The arbitrary data section may record, for example, data acquired by an arbitrary inspection device, electronic medical record data, interview data, patient information (for example, patient identifier, patient background information), and the like.

[0079] Some backgrounds of the present embodiment configured to generate these data will be described. In "Is disseminated intravascular coagulation syndrome (DIC) involved in the death of coronavirus disease 2019?" (Japan Medical Journal Homepage: https: / / www.jmedj.co.jp / Journal / paper / detail.php?id=14500), it is considered that DIC (thromboembolism due to DIC) induced by coronavirus disease 2019 (COVID-19) is one of the causes of death from severe coronavirus disease 2019, that myocarditis may also occur, that almost no examinations of cardiac and vascular echocardiograms performed in a closed and close manner have been carried out and the thrombosis in deep veins and the heart is unknown, that when myocarditis is complicated with DIC, cardiac thrombosis is likely to form and thromboembolism and multiple organ failure may occur, that sepsis may occur due to COVID-19 infection, that it is difficult to diagnose microthrombus disorders caused by sepsis, etc., that it is difficult to diagnose because the initial cardiovascular abnormalities are in microvessels, that examinations of the blood coagulation system including D-dimer and cardiac and vascular echocardiograms are considered effective for patients with coronavirus disease 2019, that if DIC can be diagnosed, dramatic improvement of symptoms can be expected by anticoagulant therapy, that prevention of thrombosis can be the basis of the treatment of coronavirus disease 2019, etc. are pointed out.

[0080] "It is presumed that many severe COVID-19 patients have fallen into sepsis" (Japan Medical Journal Homepage: https: / / www.jmedj.co.jp / Journal / paper / detail.php?id=14563) points out that many severe cases and deaths due to coronavirus disease 2019 (COVID-19) have sepsis. The present embodiment provides a non-invasive method for providing information on sepsis.

[0081] "Manual for Responding to Severe Adverse Events by Disease - Disseminated Intravascular Coagulation (Systemic Hypercoagulable Disorder, Consumptive Coagulopathy)", June 2007. The Ministry of Health, Labour and Welfare has pointed out that in sepsis, the balance between blood coagulation and thrombolysis is disrupted, and a syndrome with a poor prognosis called DIC occurs, where thrombi form throughout the body and bleeding occurs in the microvessels. This aspect provides a non - invasive method for providing information regarding DIC, blood coagulation, thrombolysis, thrombi, bleeding, etc.

[0082] "COVID - 19 and Coagulopathy: Frequently Asked Questions" (AMERICAN SOCIETY OF HEMATOLOGY homepage: https: / / www.hematology.org / covid - 19 - and - coagulopathy) points out that for patients infected with coronavirus disease 2019 (COVID - 19), there is a possibility that various microvascular thrombus formations occur mainly in the lungs when DIC occurs, the correlation between blood test values indicating enhanced blood coagulation and disease severity strongly suggests this possibility, the frequent occurrence of heart and kidney disorders not only in the lungs is also thought to be due to intravascular thrombus formation, and there are cases presenting vasculitis symptoms such as Kawasaki disease as skin symptoms, etc. This aspect provides a non - invasive method for providing information regarding thrombi, blood coagulation, vascular inflammation, etc.

[0083] "Guidelines for the Diagnosis and Treatment of Coronavirus Disease 2019 (COVID-19), 2nd Edition" (Ministry of Health, Labour and Welfare website: https: / / www.mhlw.go.jp / content / 000631552.pdf) points out that D-dimer, CRP (C-reactive protein), LDH (serum lactate dehydrogenase), ferritin, lymphocytes, creatinine, etc. may be useful as markers for the progression of coronavirus disease 2019 (COVID-19). In particular, the correlation between blood test values indicating enhanced blood coagulation and disease progression has been pointed out. In addition to these, there are also documents indicating the usefulness of cardiac troponin (Tn), IL-1β, IL-6, IL-8, TNFα, IFNα, etc. This aspect provides a non-invasive method for providing information on changes in blood properties as reflected in these markers of disease progression.

[0084] The data processing unit 20 of this aspect may be designed and configured in consideration of such a background. Some exemplary aspects of the data processing unit 20 will be described later. It should be noted that the data processing unit 20 (and the data structure 100) can also be designed and configured in the same manner when targeting other infectious diseases.

[0085] A configuration example of the data processing unit 20 of this aspect is shown in FIG. 3. The data processing unit 20 in this example includes an eye image data processing unit 21 and an eye blood flow data processing unit 22.

[0086] The eye image data processing unit 21 may include, for example, a processor that operates according to a program created based at least on the above-mentioned medical findings. In this case, the eye image data processing unit 21 can generate information regarding the patient's circulatory system by processing at least the image data (eye image data) obtained from the fundus of the patient by the data acquisition unit 10 using this processor.

[0087] The eye image data input to the processor may be, for example, optical coherence tomography image data, color fundus image data, or the like. The information output from the processor may be, for example, information regarding the tendency of thrombosis, information regarding thrombosis symptoms, information regarding the state of the circulatory system accompanying an infectious disease, information indicating the state regarding sepsis, information indicating the state regarding DIC, information indicating the state regarding thrombosis, and information indicating the state regarding vascular occlusion, as described above.

[0088] The eye image data processing unit 21 may include, for example, a learned model constructed by machine learning based at least on the above-described medical findings. In this case, the eye image data processing unit 21 can generate information regarding the circulatory system of the patient by processing the image data (eye image data) acquired from the fundus of the patient by the data acquisition unit 10 using at least this learned model.

[0089] The eye image data input to the learned model may be, for example, optical coherence tomography image data, color fundus image data, or the like. The information output from the learned model may be, for example, information regarding the tendency of thrombosis, information regarding thrombosis symptoms, information regarding the state of the circulatory system accompanying an infectious disease, information indicating the state regarding sepsis, information indicating the state regarding DIC, information indicating the state regarding thrombosis, and information indicating the state regarding vascular occlusion, as described above.

[0090] An example of the eye image data processing unit 21 configured using machine learning is shown in FIG. 4. The eye image data processing unit 21 in this example includes an inference processing unit 210. The inference processing unit 210 is configured to execute an inference process for deriving information regarding the circulatory system of the patient from the eye image data acquired by the data acquisition unit 10 using a learned model constructed by machine learning using training data including clinical data (eye image data and diagnostic result data).

[0091] The eye image data included in the training data is, for example, image data acquired using the same optical modality as that of the data acquisition unit 10, but it may also be other modalities. Examples of other modalities include optical modalities different from that of the data acquisition unit 10, ultrasonic modality, electrical modality, magnetic modality, electromagnetic modality, and the like. The diagnostic result data included in the training data may be, for example, data obtained by a doctor or other inference model (trained model) based on the relevant eye image data.

[0092] Based on such training data, through machine learning (supervised learning), a trained model (inference model) can be created that takes the eye image data acquired by the data acquisition unit 10 as input and outputs estimated diagnostic data related to the circulatory system. The training data used in machine learning may include data created by a computer based on clinical data. Machine learning may include transfer learning.

[0093] The inference processing unit 210 includes the trained model obtained in this way. It inputs the eye image data acquired by the data acquisition unit 10 into the trained model and sends the estimated diagnostic data output from the trained model to the output unit 30.

[0094] The machine learning algorithms that can be used in the exemplary embodiment are not limited to supervised learning and may be, for example, any algorithm such as unsupervised learning, semi-supervised learning, reinforcement learning, transduction, multi-task learning, etc., or may also be a combination of any two or more algorithms.

[0095] The machine learning techniques that can be used in the exemplary embodiment are arbitrary and may be, for example, any technique such as neural network, support vector machine, decision tree learning, correlation rule learning, genetic programming, clustering, Bayesian network, representation learning, extreme learning machine, etc., or may also be a combination of any two or more techniques.

[0096] An example of the configuration of the inference processing unit 210 is shown in FIG. 5. The inference processing unit 210 in this example includes a first pre-trained model 211 and a second pre-trained model 212. Note that, in some exemplary embodiments, the inference processing unit 210 may include only one of the first pre-trained model 211 and the second pre-trained model 212.

[0097] The first pre-trained model 211 is constructed by machine learning using training data including eye image data and diagnostic result data. For example, the first pre-trained model 211 includes a convolutional neural network (CNN). This convolutional neural network includes, for example, an input layer to which eye image data is input, a convolutional layer that applies filtering (convolution) to the input eye image data to create a feature map, a pooling layer that performs data compression while holding the features obtained in the convolutional layer, a fully connected layer that extracts characteristic findings from all the data obtained in the pooling layer and makes a determination, and an output layer that outputs the data obtained in the fully connected layer. By inputting the eye image data acquired by the data acquisition unit 10 into the first pre-trained model 211, information regarding the circulatory system considering predetermined features is generated.

[0098] The features considered by the first pre-trained model 211 may include, for example, features related to the drawing state, features related to the drawing target, and the like. Examples of features related to the drawing state include color tone, luminance, and the like. Examples of features related to the drawing target include features related to the fundus blood vessels, features related to the optic disc, features related to the macula, and the like.

[0099] In this aspect, in order to generate information regarding the circulatory system, features related to the fundus blood vessels are particularly considered. Examples of features related to the fundus blood vessels include distribution, thickness (vessel diameter), degree of curvature (running property), bleeding, and the like. For example, from the eye image data of patients with sepsis or DIC, features such as rupture, bleeding, and abnormal running of the retinal microvessels may be detected.

[0100] As one example, a case where image data representing the morphology (structure) of the fundus oculi, such as optical coherence tomography angiography image data, is acquired will be described. In this case, the first pre-trained model 211 includes, for example, a convolutional neural network constructed by machine learning using training data including optical coherence tomography angiography image data and diagnostic result data. Note that the training data may include arbitrary image data, such as fluorescein angiography fundus image data. The convolutional neural network of this embodiment includes, for example, an input layer into which optical coherence tomography angiography image data is input, a convolutional layer that applies filtering (convolution) to the input optical coherence tomography angiography image data to create a feature map related to the vascular structure, a pooling layer that performs data compression while retaining the features of the vascular structure obtained in the convolutional layer, a fully connected layer that extracts characteristic findings of the vascular structure from all the data obtained in the pooling layer and makes a determination, and an output layer that outputs the data obtained in the fully connected layer. By inputting the optical coherence tomography angiography image data acquired by the data acquisition unit 10 into the first pre-trained model 211, information regarding the circulatory system considering the vascular structure of the fundus oculi is generated.

[0101] As another example, a case where color fundus image data is acquired will be described. In this case, the first pre-trained model 211 includes, for example, a convolutional neural network constructed by machine learning using training data including color fundus image data and diagnostic result data. The convolutional neural network of this embodiment includes, for example, an input layer into which color fundus image data is input, a convolutional layer that applies filtering (convolution) to the input color fundus image data to create a feature map related to color information (for example, R value, G value, B value), a pooling layer that performs data compression while retaining the features of the color information obtained in the convolutional layer, a fully connected layer that extracts characteristic findings of the color information from all the data obtained in the pooling layer and makes a determination, and an output layer that outputs the data obtained in the fully connected layer. By inputting the color fundus image data acquired by the data acquisition unit 10 into the first pre-trained model 211, information regarding the circulatory system considering the color tone of the fundus oculi is generated.

[0102] The second trained model 212 is constructed by machine learning using training data including data generated by processing eye image data acquired from the fundus using a predetermined modality and diagnostic result data.

[0103] When the data generated by processing the eye image data is image data, the second trained model 212 includes, for example, a convolutional neural network. This convolutional neural network includes, for example, an input layer into which the image data generated by processing the eye image data is input, a convolutional layer that applies filtering (convolution) to the input eye image data to create a feature map, a pooling layer that performs data compression while retaining the features obtained in the convolutional layer, a fully connected layer that extracts characteristic findings from all the data obtained in the pooling layer and makes a determination, and an output layer that outputs the data obtained in the fully connected layer. By inputting the data generated by processing the eye image data acquired by the data acquisition unit 10 into the second trained model 212, information regarding the circulatory system considering predetermined features is generated. The features considered by the second trained model 212 may be the same as or different from the features considered by the first trained model 211.

[0104] The data input into the second trained model 212 is not limited to image data, and may be, for example, numerical data, distribution data, time series data, or the like. When data in a form other than image data is input into the second trained model 212, the second trained model 212 is constructed according to the form of the input data and the features to be considered. For example, when processing time series data such as waveform data or follow-up observation data, the second trained model 212 may include a recurrent neural network (RNN).

[0105] When the data input into the inference processing unit 210 is video data, the trained model for processing the video data may have, for example, a structure combining a convolutional neural network and a recurrent neural network.

[0106] The ocular blood flow data processing unit 22 may include, for example, a processor that operates according to a program created based at least on the medical knowledge as described above. In this case, the ocular blood flow data processing unit 22 can generate information regarding the circulatory system of the patient by processing at least the data (ocular blood flow data) acquired from the fundus of the patient by the data acquisition unit 10 with this processor.

[0107] The ocular blood flow data input to the processor may be, for example, data acquired by optical coherence tomography blood flow measurement. The data acquired by optical coherence tomography blood flow measurement may be, for example, image data of a waveform representing the time-series change of blood flow dynamics (blood flow velocity, blood flow volume, etc.), image data of a map representing the spatial distribution of blood flow dynamics, image data representing both the spatial distribution and the time-series change of blood flow dynamics, a series of pairs of numerical values representing the time-series change of blood flow dynamics and time, a series of pairs of numerical values representing the spatial distribution of blood flow dynamics and coordinates, a series of triplets of numerical values, coordinates, and time representing both the spatial distribution and the time-series change of blood flow dynamics, and the like. The information output from the processor may be, for example, any of the information regarding the thrombosis tendency, the information regarding the thrombosis symptoms, the information regarding the state of the circulatory system accompanying an infectious disease, the information indicating the state regarding sepsis, the information indicating the state regarding DIC, the information indicating the state regarding thrombosis, and the information indicating the state regarding vascular occlusion as described above.

[0108] The ocular blood flow data processing unit 22 may include, for example, a learned model constructed by machine learning based at least on the medical knowledge as described above. In this case, the ocular blood flow data processing unit 22 can generate information regarding the circulatory system of the patient by processing at least the data (ocular blood flow data) acquired from the fundus of the patient by the data acquisition unit 10 using this learned model.

[0109] The ocular blood flow data input to the learned model may be, for example, data obtained by optical coherence tomography blood flow measurement, similar to the case of the above-described processor. The information output from the learned model may also be, for example, information similar to the case of the above-described processor.

[0110] An example of the ocular blood flow data processing unit 22 configured using machine learning is shown in FIG. 6. The ocular blood flow data processing unit 22 in this example includes an inference processing unit 220. The inference processing unit 220 is configured to execute an inference process for deriving information regarding the patient's circulatory system from the ocular blood flow data acquired by the data acquisition unit 10 using a learned model constructed by machine learning using training data including clinical data (ocular blood flow data and diagnostic result data).

[0111] The ocular blood flow data included in the training data is, for example, data acquired using the same optical modality as the optical modality of the data acquisition unit 10, but may be other modalities. Other modalities include an optical modality different from the optical modality of the data acquisition unit 10, an ultrasonic modality, an electrical modality, a magnetic modality, an electromagnetic modality, and the like. The diagnostic result data included in the training data may be, for example, data obtained by a doctor or another inference model (learned model) based on the relevant ocular blood flow data.

[0112] By such machine learning (supervised learning) based on such training data, a learned model (inference model) can be created that takes the ocular blood flow data acquired by the data acquisition unit 10 as input and outputs estimated diagnostic data regarding the circulatory system. The training data used for machine learning may include data created by a computer based on clinical data. Machine learning may include transfer learning. The machine learning algorithms and machine learning techniques may be the same as in the case of the eye image data processing unit 21.

[0113] The inference processing unit 220 includes the learned model thus obtained, inputs the ocular blood flow data acquired by the data acquisition unit 10 into the learned model, and sends the estimated diagnosis data output from the learned model to the output unit 30.

[0114] An example of the configuration of the inference processing unit 220 is shown in FIG. 7. The inference processing unit 220 in this example includes a first learned model 221 and a second learned model 222. In some exemplary embodiments, the inference processing unit 220 may include only one of the first learned model 221 and the second learned model 222. Unless otherwise specified, various matters regarding the learning model provided in the inference processing unit 220 may be the same as the corresponding matters in the learning model provided in the inference processing unit 210.

[0115] The first learned model 221 is constructed by machine learning using training data including ocular blood flow data and diagnosis result data. The first learned model 221 includes, for example, a model corresponding to the type (mode) of input data and the type (mode) of output data. For example, the first learned model 221 may include a convolutional neural network similar to the first learned model 211 of the ocular image data processing unit 21. By inputting the ocular blood flow data acquired by the data acquisition unit 10 into the first learned model 221, information regarding the circulatory system is generated. The features considered by the first learned model 221 may be the same as or different from the features considered by the first learned model 211 of the ocular image data processing unit 21.

[0116] The second trained model 222 is constructed by machine learning using training data including data generated by processing data acquired from the fundus using a predetermined modality and diagnostic result data. The type of data generated by processing the data acquired from the fundus can be arbitrary, for example, image data, numerical data, distribution data, time series data, etc. The second trained model 222 includes, for example, a model corresponding to the type (mode) of input data and the type (mode) of output data. By inputting the data obtained by processing the data acquired by the data acquisition unit 10 into the second trained model 222, information regarding the circulatory system is generated. The features considered by the second trained model 222 may be the same as or different from the features considered by the second trained model 212 of the eye image data processing unit 21. Also, the data input into the second trained model 222 may be, for example, any of the following types: eye blood flow data generated by processing eye blood flow data acquired from the fundus using a predetermined modality; data of a type other than eye blood flow data generated by processing eye blood flow data acquired from the fundus using a predetermined modality; eye blood flow data generated by processing data of a type other than eye blood flow data acquired from the fundus using a predetermined modality.

[0117] The output unit 30 outputs the result of the processing executed by the data processing unit 20. The mode of the output processing is arbitrary and may be, for example, any of transmission, display, recording, and printing. The information output by the output unit 30 may be the result of the processing executed by the data processing unit 20 itself (information regarding the patient's circulatory system), information including the processing result, or information obtained by processing the processing result. For example, the medical system 1 may further include a report creation unit (not shown) that creates a report based on the information regarding the circulatory system obtained by the data processing unit 20. In this case, the output unit 30 can output the created report.

[0118] The output unit 30 illustrated in FIG. 1 includes a transmission unit 31. The transmission unit 31 transmits the result of the process executed by the data processing unit 20 toward the doctor terminal 3. The doctor terminal 3 is arranged at a remote position with respect to the data acquisition unit 10.

[0119] The transmission of data from the output unit 30 to the doctor terminal 3 may be direct or indirect. Direct transmission is a mode of transmitting the result of the process (information regarding the cardiovascular system, a report, etc.) from the output unit 30 to the doctor terminal 3. Indirect transmission is a mode of transmitting the result of the process to a device other than the doctor terminal 3 (such as a server, a database, etc.) and providing the doctor terminal 3 with the result of the process via the device.

[0120] As in this example, by arranging the doctor terminal 3 at a remote position with respect to the data acquisition unit 10 and configuring to provide the doctor terminal 3 with the information (or information based thereon) generated by the data processing unit 20 based on the data acquired by the data acquisition unit 10 from the fundus of the patient, it is possible to secure the social distance between the doctor (medical staff) and the patient and reduce the infection risk of the doctor (medical staff).

[0121] <Usage form of the medical system> The usage form of the medical system 1 according to an exemplary mode will be described. The flowchart of FIG. 8 shows an example of the usage form of the medical system 1. Although this example uses a learned model, in an example that does not use a learned model, the construction and installation of the learned model (steps S1 and S2) are unnecessary. For example, instead of them, the creation and installation of a processing program are performed.

[0122] (S1: Construct a learned model) As a preparation for the operation of the medical system 1, a learned model used in the data processing unit 20 is constructed. Note that the process performed at this stage may be an update (parameter adjustment / updating) of a learned model that is already in operation.

[0123] (S2: Mount the trained model on the data processing unit) As a further preparation for the operation of the medical system 1, the trained model constructed in step S1 is mounted on the data processing unit 20. In this step, for example, the trained model constructed in step S1 is transmitted to the medical system 1 through a communication line.

[0124] (S3: Acquire data from the fundus of the patient) The subject may be, for example, a patient with a confirmed diagnosis of coronavirus disease 2019 (COVID-19) or a suspected patient of coronavirus disease 2019 (COVID-19). The data acquisition unit 10 of the medical system 1 acquires data from the fundus of the patient using at least one optical modality.

[0125] The data acquisition unit 10 can apply, for example, optical coherence tomography and / or color fundus photography to the fundus. The data acquired by optical coherence tomography may be, for example, any of three-dimensional image data, projection image data, optical coherence tomography angiography image data, and optical coherence tomography blood flow data. The data acquired by color fundus photography may be, for example, color frontal image data representing the morphology of the fundus.

[0126] At least a part of the examination performed in this step may be a remote examination using the operating device 2.

[0127] (S4: Input data to the data processing unit) The data acquired in step S3 is sent to the data processing unit 20. In this example, at least a part of the data input to the data processing unit 20 is input to the trained model constructed in step S1.

[0128] (S5: Generate information related to the circulatory system) The data processing unit 20 processes the data input in step S4 to generate information regarding the patient's circulatory system. As a result, for example, at least one of the following information can be obtained: information regarding the tendency of thrombosis (information regarding blood properties and / or information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolytic system); information regarding thrombus symptoms (a method for indicating the blood flow velocity distribution in a blood vessel and / or information regarding a structure formed in a blood vessel); information regarding the state (and / or state change) of the circulatory system associated with an infectious disease; information indicating the state regarding sepsis; information indicating the state regarding DIC; information indicating the state regarding a thrombus; information indicating the state regarding vascular occlusion.

[0129] The information generated by the data processing unit 20 is recorded, for example, according to the data structure 100 in FIG. 2. As a result, a data package regarding the patient's circulatory system is obtained.

[0130] (S6: Create a report) The medical system 1 (the report creation unit not shown above) creates a report based on the information regarding the patient's circulatory system generated in step S5.

[0131] (S7: Transmit the report) The transmission unit 31 of the output unit 30 transmits the report created in step S6 to the doctor terminal 3 at a remote location with respect to the data acquisition unit 10 or to a computer capable of providing information to the medical terminal 3. The doctor terminal 3 is not limited to a computer used by a doctor and may be a computer (medical staff terminal) used by medical staff other than doctors.

[0132] According to such a medical system 1, it is possible to ensure social distancing between medical staff and patients and reduce the risk of infection from patients to medical staff. Furthermore, since the medical system 1 is configured to acquire data from the fundus of a patient using non-invasive optical modalities such as optical coherence tomography and color fundus photography and generate information regarding the patient's circulatory system from this data, it is possible to provide a technique for non-invasively detecting the state of the patient's circulatory system. The state of the circulatory system detected thereby is based on, for example, the aforementioned medical findings and / or other medical findings, and examples thereof include symptoms, signs of exacerbation, and the risk of exacerbation.

[0133] <First Embodiment of Medical System> An exemplary embodiment of the medical system 1 described above will be described. In the present embodiment, when the data acquisition unit 10 performs optical coherence tomography, particularly when performing optical coherence tomography blood flow measurement, will be described. Based on the aforementioned medical findings, the present embodiment is configured to generate information regarding the blood coagulation fibrinolytic system from the ocular blood flow data acquired by optical coherence tomography blood flow measurement. Unless otherwise specified, the medical system of the present embodiment may have the same configuration as the medical system 1 described above.

[0134] A configuration example of the medical system according to the present embodiment is shown in FIG. 9. The medical system 1A of this example includes a data acquisition unit 10A, a data processing unit 20A, and an output unit 30. The output unit 30 and the transmission unit 31 are the same as the output unit 30 and the transmission unit 31 in the medical system 1 described above, respectively. The same applies to the operation device 2 and the doctor terminal 3.

[0135] The data acquisition unit 10A is an example of the data acquisition unit 10 of the medical system 1 described above and includes an optical coherence tomography (OCT) device 11 and a calculation unit 12.

[0136] The optical coherence tomography device 11 applies a scan for optical coherence tomography blood flow measurement to the fundus of a patient's eye. The calculation unit 12 obtains ocular blood flow data based on the data collected by the optical coherence tomography device 11 through this scan. The ocular blood flow data includes the blood flow velocity and the blood vessel diameter at the position where the scan is applied. The method of the scan executed by the optical coherence tomography device 11 and the method of the calculation executed by the calculation unit 12 may be any known method, and for example, the method described in Japanese Patent Application Laid-Open No. 2020-48730 can be used.

[0137] The data processing unit 20A processes the ocular blood flow data acquired by the data acquisition unit 10A in order to generate information regarding the blood coagulation fibrinolytic system. A configuration example of the data processing unit 20A is shown in FIG. 10. The data processing unit 20A in this example includes a wall shear rate (WSR) calculation unit 231, a storage unit 232, a wall shear stress (WSS) calculation unit 233, and an information generation unit 234.

[0138] The WSR calculation unit 231 calculates the wall shear rate (WSR) based on the blood flow velocity and the blood vessel diameter calculated by the calculation unit 12 of the data acquisition unit 10A. The method of calculating the wall shear rate from the blood flow velocity and the blood vessel diameter is arbitrary, and for example, the method described in the following document can be used: Taiji Nagaoka and Akitoshi Yoshida, "Noninvasive Evaluation of Wall Shear Stress on Retinal Microcirculation in Humans", IOVS. 2006, Vol. 47, 1113-1119. Although this document measures the blood flow velocity and the blood vessel diameter using laser Doppler velocimetry (LDV), it is obvious to those skilled in the art that the same wall shear rate calculation method can also be applied to the blood flow velocity and the blood vessel diameter obtained using optical coherence tomography as in this embodiment.

[0139] Based on this document, the optical coherence tomography device 11 of the data acquisition unit 10A collects data by applying a scan over at least one cardiac cycle. The calculation unit 12 calculates, as the blood flow velocity, the time average (V mean ) of the (centerline) blood flow velocity in one cardiac cycle. Further, the calculation unit 12 calculates the blood vessel diameter (D) from the cross-sectional image data constructed from the data collected by the scan over the above-mentioned one cardiac cycle. The WSR calculation unit 231 calculates the wall shear rate (WSR) by the following formula: WSR = 8 × V mean / D.

[0140] The calculation unit 12 can calculate the blood vessel cross-sectional area (Area) from the cross-sectional image data constructed from the data collected by the scan over the above-mentioned one cardiac cycle. Further, the calculation unit 12 can calculate the blood flow volume (BF) by multiplying the time average (V mean ) of the blood flow velocity by the blood vessel cross-sectional area (Area): BF = V mean × Area.

[0141] The storage unit 232 stores blood viscosity information 232a. The blood viscosity information 232a includes a blood viscosity value η. The blood viscosity value η may be a measured value or a standard value. The measurement of the blood viscosity is performed, for example, using a cone-plate viscometer. Also, the blood viscosity may be estimated from data obtained by blood tests such as hematocrit (Ht), red blood cell count, red blood cell constants (such as mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), etc.). Alternatively, the blood viscosity may be estimated by substituting a specified value into a blood parameter such as plasma viscosity. As the standard value, a normal value or a disease value determined from the range of blood viscosity values obtained from clinical data or experimental data can be used.

[0142] The WSS calculation unit 233 calculates the wall shear stress (WSS) based on at least the wall shear rate calculated by the WSR calculation unit 231 and the blood viscosity value included in the blood viscosity information 232a. The method for calculating the wall shear stress from the wall shear rate and the blood viscosity value may be arbitrary. For example, using the method described in the above-mentioned literature (Nagaoka and Yoshida), the WSS calculation unit 233 calculates the wall shear stress (WSS) by the following formula: WSS = η × WSR.

[0143] The information generation unit 234 generates information regarding the patient's circulatory system based on at least the wall shear stress calculated by the WSS calculation unit 233. In the present embodiment, information regarding the blood coagulation and fibrinolysis system can be generated as information regarding the patient's circulatory system.

[0144] According to Michael R. Condon et al., "Appearance of an erythrocyte population with decreased deformability and hemoglobin content following sepsis", Am J Physiol Heart Circ Physiol 284: H2177 - 2184, 2003, it is shown that in the blood of sepsis model animals, the deformability of erythrocytes is damaged and the wall shear stress is increased. The increase in wall shear stress is considered to be related to the tendency of thrombosis because it promotes vascular endothelial injury. Based on such a background, the information generation unit 234 can evaluate the value of the wall shear stress calculated by the WSS calculation unit 233 and generate information including the result.

[0145] In addition, when a standard value (predetermined value, default value) is adopted as the blood viscosity, that is, when it is assumed that η is constant in the above formula "WSS = η × WSR", the value of WSR and the value of WSS correspond one-to-one. Therefore, in this case, it is not necessary to provide the storage unit 232 and the WSS calculation unit 233. Furthermore, the information generation unit 234 may be configured to generate information regarding the patient's circulatory system (information regarding the blood coagulation and fibrinolysis system) based on the value of the wall shear rate calculated by the WSR calculation unit 231.

[0146] <Second Embodiment of the Medical System> Another exemplary embodiment of the medical system 1 will be described. Similar to the first embodiment, the data acquisition unit 10 executes optical coherence tomography blood flow measurement in this embodiment, and thereby generates information regarding the blood coagulation and fibrinolysis system from the acquired ocular blood flow data, and generates information regarding the structure formed in the blood vessel. Unless otherwise specified, the medical system of this embodiment may have the same configuration as the above-described medical system 1 and / or 1A.

[0147] A configuration example of the medical system according to this embodiment is shown in FIG. 11. The medical system 1B of this example includes a data acquisition unit 10B, a data processing unit 20B, and an output unit 30. The output unit 30 and the transmission unit 31 are the same as the output unit 30 and the transmission unit 31 in the above-described medical system 1, respectively. The same applies to the operation device 2 and the doctor terminal 3.

[0148] The data acquisition unit 10B is an example of the data acquisition unit 10 of the above-described medical system 1, and includes an optical coherence tomography (OCT) device 13 and a blood flow information generation unit 14.

[0149] The optical coherence tomography device 13 applies a scan for optical coherence tomography blood flow measurement to the fundus of a patient's eye. The optical coherence tomography device 13 repeatedly applies an optical coherence tomography scan to a predetermined region of the fundus of the patient's eye to collect time-series data. This optical coherence tomography scan includes an A-scan for at least one position (A-line), and may be, for example, A-scans, B-scans, circle scans, etc. for a plurality of positions. Thereby, time-series data corresponding to each scan application position is obtained.

[0150] The blood flow information generation unit 14 generates ocular blood flow data based on the time series collected by the optical coherence tomography device 13. The ocular blood flow data includes blood flow information representing the spatial distribution and temporal change of the blood flow velocity. The space in which the distribution of the blood flow velocity is defined may be any of a one-dimensional space, a two-dimensional space, and a three-dimensional space. The temporal change of the blood flow velocity is defined for each point (each position) in the space. Thus, the blood flow information obtained by the blood flow information generation unit 14 represents the temporal change of the blood flow velocity at each point in the one-dimensional space, two-dimensional space, or three-dimensional space inside the blood vessel to which the optical coherence tomography blood flow measurement is applied. Such blood flow information is described, for example, in Robert S. Reneman and Arnold P. G. Hoeks, "Wall shear stress as measured in vivo: consequences for the design of the arterial system", Med Biol Eng Comput (2008) 46:499 - 507.

[0151] The data processing unit 20B can generate information regarding the blood coagulation fibrinolysis system by processing the ocular blood flow data (blood flow information) acquired by the data acquisition unit 10A. Also, the data processing unit 20B can generate information regarding the structure formed in the blood vessel by processing the ocular blood flow data (blood flow information) acquired by the data acquisition unit 10A.

[0152] A configuration example of the data processing unit 20B is shown in FIG. 12. The data processing unit 20B in this example includes a wall shear rate (WSR) information generation unit 235, a storage unit 236, a wall shear stress (WSS) information generation unit 237, and an information generation unit 238.

[0153] The WSR information generation unit 235 generates WSR information representing the spatial distribution and temporal change of the wall shear rate (WSR) based at least on the blood flow information generated by the blood flow information generation unit 14 of the data acquisition unit 10B. The method for generating the WSR information representing the spatial distribution and temporal change of the wall shear rate from the blood flow information representing the spatial distribution and temporal change of the blood flow velocity is arbitrary, and for example, the method described in the above literature (Robert S. Reneman and Arnold P. G. Hoeks) can be used.

[0154] The storage unit 236 stores blood viscosity information 236a. The blood viscosity information 236a may be a single value (η) similar to the blood viscosity information 232a in the first embodiment, or may be at least a distribution of blood viscosity values in the target space (the space in which the distribution of blood flow velocity is defined). Note that the single blood viscosity value (η) corresponds to the case where the blood viscosity distribution in the target space is uniform (constant).

[0155] The WSS information generation unit 237 generates WSS information representing the spatial distribution and temporal change of the wall shear stress (WSS) based at least on the WSR information generated by the WSR information generation unit 235 and the blood viscosity distribution information 236a. The method for generating the WSS information may be arbitrary. For example, similar to the first embodiment, the WSS information generation unit 237 can generate the WSS information by multiplying the value of the wall shear rate and the value of the blood viscosity for each point in the target space.

[0156] The information generation unit 238 can generate information regarding a structure (such as a thrombus, thrombosis tendency, etc.) formed in a blood vessel based on at least the blood flow information generated by the blood flow information generation unit 14 of the data acquisition unit 10B and the WSS information generated by the WSS information generation unit 237. Further, the information generation unit 238 can generate information regarding a structure formed in a blood vessel based on at least the blood flow information generated by the blood flow information generation unit 14 of the data acquisition unit 10B and the WSR information generated by the WSR information generation unit 235. Further, the information generation unit 238 can generate information regarding the blood coagulation fibrinolytic system based on any one of the blood flow information generated by the blood flow information generation unit 14 of the data acquisition unit 10B, the WSR information generated by the WSR information generation unit 235, and the WSS information generated by the WSS information generation unit 237 (and other information).

[0157] <Medical Information Processing Apparatus and Medical System> An example of a medical information processing apparatus according to an exemplary aspect and a medical system including the same will be described. Unless otherwise specified, the elements according to the following aspects may be the same as any of the elements of the above-described medical systems 1, 1A, and 1B.

[0158] The exemplary medical information processing apparatus 5 shown in FIG. 13 includes a data reception unit 51, a data processing unit 52, and an output unit 53. Outside the medical information processing apparatus 5 of the present aspect, a data acquisition device 6, an operation device 7, a communication device 8, and a doctor terminal 9 are provided.

[0159] The data acquisition device 6 acquires data from the fundus of a patient using at least one optical method. The operation device 7 is used for medical staff to operate the data acquisition device 6 (examination device). In some exemplary aspects, the operation device 7 is provided at a position away from the data acquisition device 6 and is used to remotely operate the data acquisition device 6. The communication device 8 transmits the data acquired by the data acquisition device 6 to the medical information processing apparatus 5. In some exemplary aspects, the doctor terminal 9 is arranged at a remote position with respect to the data acquisition device 6.

[0160] The data reception unit 51 of the medical information processing apparatus 5 receives data acquired from the fundus of a patient using at least one optical method. In this embodiment, the data reception unit 51 receives data acquired by the data acquisition apparatus 6 and transmitted by the communication apparatus 8. In the example of FIG. 13, data is sent from the data acquisition apparatus 6 to the data reception unit 51 via the communication apparatus 8, but the data input mode to the medical information processing apparatus 5 is not limited to this. For example, the data acquired by the data acquisition apparatus 6 may be stored in a database or the like, and the data may be sent from this database to the data reception unit 51. The data reception unit 51 may include, for example, communication equipment for connecting to a communication line, a drive device for reading data recorded on a recording medium, and the like.

[0161] The data processing unit 52 is configured to process the data received by the data reception unit 51 in order to generate information regarding the circulatory system of the patient. The output unit 53 outputs the information regarding the circulatory system of the patient generated by the data processing unit 52. The output unit 53 in this example includes a transmission unit 54. The transmission unit 54 can transmit the information regarding the circulatory system of the patient generated by the data processing unit 52 toward the doctor terminal 9 at a remote location with respect to the data acquisition apparatus 6.

[0162] Any matter described for any of the medical systems 1, 1A, and 1B described above can be combined with the medical information processing apparatus 5 or a medical system including the same.

[0163] According to such a medical information processing apparatus 5 and a medical system including the same, it is possible to ensure social distancing between medical staff and patients and reduce the risk of infection from patients to medical staff. Furthermore, according to the medical information processing apparatus 5 and the medical system including the same, since data is acquired from the fundus of a patient using non-invasive optical modalities such as optical coherence tomography and color fundus photography, and information regarding the circulatory system of the patient is generated from this data, it is possible to provide a technique for non-invasively detecting the state of the patient's circulatory system. The state of the circulatory system detected thereby is, for example, based on the above-described medical findings and / or other medical findings, and examples thereof include symptoms, signs of exacerbation, risk of exacerbation, and the like.

[0164] <Summary> As described above, the technology according to the present disclosure uses non-invasive optical ophthalmic modalities such as optical coherence tomography blood flow measurement, optical coherence tomography angiography, and color fundus photography to detect conditions related to the circulatory system, such as the tendency of blood vessels to become inflamed, the tendency to form blood clots, the tendency to develop sepsis, and the tendency to develop DIC. For example, changes in blood properties in the tendency of blood coagulation associated with infectious diseases and the like can be detected based on the wall shear rate and wall shear stress obtained from the blood flow velocity and blood vessel diameter obtained by optical coherence tomography blood flow measurement, or can be detected based on the spatial distribution and temporal change of the blood flow velocity in the blood vessel cross-section (temporal change of the blood flow velocity profile). In addition, it is possible to input this data into a learned model constructed by machine learning and output an index regarding the exacerbation of infectious diseases and the like. Thereby, it becomes possible to non-invasively detect early changes in the disease state and provide various diagnostic support information.

[0165] For example, it has been pointed out that in patients with coronavirus disease 2019 (COVID-19), thrombi occur in the microvessels of various organs, mainly in the lungs. In addition, a correlation has been pointed out between various vascular examination values indicating enhanced blood coagulation, which is a cause of thrombosis, and disease aggravation. The progression of severe COVID-19 is considered as follows: (1) infection; (2) rapid immune response and inflammation; (3) disseminated intravascular coagulation (DIC); (4) angiogenesis in multiple organs; (5) death due to cerebral infarction, myocardial infarction, multiple organ failure, etc. There are also known cases where (2) to (4) progress rapidly. In (2) rapid immune response and inflammation, various abnormalities in the coagulation system, such as a decrease in platelets, an increase in D-dimer, a decrease in fibrinogen, and an extension of prothrombin time (PT time), are detected by blood tests.

[0166] The technology according to the present disclosure non-invasively detects the hemodynamics of retinal blood vessels (such as blood flow velocity, blood flow volume, and the shape of blood flow waveforms), and evaluates wall shear stress, thrombi (peripheral vascular occlusion), etc. from the data, thereby early detecting the risk of severe coronavirus disease 2019 (COVID-19).

[0167] When evaluation is performed using a learned model constructed by machine learning, for example, the correlation between various blood test values and hemodynamic information (such as blood flow velocity, blood flow volume, and the shape of blood flow waveforms) is used as teacher data. Thereby, a learned model with hemodynamic information as input and blood test values as output is constructed. By inputting the ocular blood flow data (hemodynamic information) obtained from the fundus of a patient using optical coherence tomography blood flow measurement into this learned model, it becomes possible to estimate blood test values.

[0168] The combination of input data and output data is not limited to this example and can be arbitrarily determined based on medical knowledge and background. For example, the input data may be color fundus image data, optical coherence tomography angiography image data, other optical coherence tomography image data (e.g., morphological image data and / or functional image data), and the like. Further, the output data may be severity, magnitude of severe risk, numerical values of tests other than blood tests, and the like.

[0169] Thus, the technology according to the present disclosure can early detect abnormalities occurring in microvessels and blood flow for diseases in which vascular disorders and blood flow disorders occur throughout the body, using a non-invasive modality.

Explanation of Signs

[0170] 1, 1A, 1B Medical system 2 Operating device 3 Physician terminal 10, 10A, 10B Data acquisition unit 11, 13 Optical coherence tomography device 12 Calculation unit 14 Blood flow information generation unit 20, 20A, 20B Data processing unit 21 Eye image data processing unit 210 Inference processing unit 211 First learned model 212 Second learned model 22 Eye blood flow data processing unit 220 Inference processing unit 221 First learned model 222 Second learned model 231 WSR calculation unit 232 Storage unit 232a Blood viscosity information 233 WSS calculation unit 234 Information generation unit 235 WSR information generation unit 236 Storage unit 236a Blood viscosity information 237 WSS information generation unit 238 Information generation unit 30 Output unit 31 Transmission unit

Claims

1. A data acquisition unit that acquires data from the fundus of a patient using at least one optical method, A data processing unit that processes the data acquired by the data acquisition unit to generate information regarding the circulatory system of the patient comprising wherein the information regarding the circulatory system includes information regarding the tendency to form thrombi, the information regarding the tendency to form thrombi includes information regarding the properties of blood, the information regarding the properties of blood includes information indicating changes in the properties of blood due to enhancement of the blood coagulation fibrinolysis system, the at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement), the data processing unit generates information indicating changes in the properties of blood due to enhancement of the blood coagulation fibrinolysis system based at least on the blood flow data acquired by the OCT blood flow measurement, the data acquisition unit an OCT device that applies an optical coherence tomography (OCT) scan to the fundus to collect data, a calculation unit that calculates the blood flow velocity and the blood vessel diameter based at least on the data collected by the OCT device comprising the data processing unit a WSR calculation unit that calculates the wall shear rate (WSR) based at least on the blood flow velocity and the blood vessel diameter calculated by the calculation unit, an information generation unit that generates information indicating changes in the properties of blood due to enhancement of the blood coagulation fibrinolysis system by evaluating the wall shear rate calculated by the WSR calculation unit based on the relationship between the magnitude of the wall shear rate and the tendency to form thrombi comprising a medical system.

2. A data acquisition unit that acquires data from the fundus of a patient using at least one optical method, A data processing unit that processes the data acquired by the data acquisition unit to generate information regarding the circulatory system of the patient comprising wherein the information regarding the circulatory system includes information regarding the tendency to form thrombi, the information regarding the tendency to form thrombi includes information regarding the properties of blood, the information regarding the properties of blood includes information indicating changes in the properties of blood due to enhancement of the blood coagulation fibrinolysis system, the at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement), the data processing unit generates information indicating changes in the properties of blood due to enhancement of the blood coagulation fibrinolysis system based at least on the blood flow data acquired by the OCT blood flow measurement, the data acquisition unit An OCT device that applies an optical coherence tomography (OCT) scan to the fundus to collect data, A calculation unit that calculates a blood flow velocity and a blood vessel diameter based at least on the data collected by the OCT device comprising The data processing unit A WSR calculation unit that calculates a wall shear rate (WSR) based at least on the blood flow velocity and the blood vessel diameter calculated by the calculation unit, A storage unit that stores blood viscosity information acquired in advance, A WSS calculation unit that calculates a wall shear stress (WSS) based at least on the wall shear rate and the blood viscosity information, An information generation unit that generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system by evaluating the wall shear stress calculated by the WSS calculation unit based on the relationship between the magnitude of the wall shear stress and the thrombosis tendency comprising A medical system.

3. A data acquisition unit that acquires data from the fundus of a patient using at least one optical method, A data processing unit that processes the data acquired by the data acquisition unit to generate information regarding the circulatory system of the patient comprising The information regarding the circulatory system includes information regarding thrombosis tendency, The information regarding the thrombosis tendency includes information regarding blood properties, The information regarding the blood properties includes information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, The at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement), The data processing unit generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system based at least on the blood flow data acquired by the OCT blood flow measurement, The data acquisition unit An OCT device that repeatedly applies an optical coherence tomography (OCT) scan to a predetermined region of the fundus to collect time-series data, A blood flow information generation unit that generates blood flow information representing the spatial distribution and temporal change of the blood flow velocity based at least on the time-series data collected by the OCT device comprising The data processing unit A WSR information generation unit that generates WSR information representing the spatial distribution and temporal change of the wall shear rate (WSR) based at least on the blood flow information generated by the blood flow information generation unit An information generation unit that generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system by evaluating the WSR information generated by the WSR information generation unit based on the relationship between the magnitude of the wall shear rate and the thrombus formation tendency. Including A medical system.

4. The data processing unit generates information regarding a structure formed in a blood vessel based at least on the blood flow information and the WSR information. The medical system according to claim 3.

5. A data acquisition unit that acquires data from the fundus of a patient using at least one optical method, A data processing unit that processes the data acquired by the data acquisition unit to generate information regarding the circulatory system of the patient Including The information regarding the circulatory system includes information regarding the thrombus formation tendency, The information regarding the thrombus formation tendency includes information regarding blood properties, The information regarding blood properties includes information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, The at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement), The data processing unit generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system based at least on the blood flow data acquired by the OCT blood flow measurement, The data acquisition unit An OCT device that repeatedly applies an optical coherence tomography (OCT) scan to a predetermined region of the fundus to collect time-series data, A blood flow information generation unit that generates blood flow information representing the spatial distribution and temporal change of the blood flow velocity based at least on the time-series data collected by the OCT device Including The data processing unit A WSR information generation unit that generates WSR information representing the spatial distribution and temporal change of the wall shear rate (WSR) based at least on the blood flow information generated by the blood flow information generation unit, A storage unit that stores blood viscosity distribution information acquired in advance, A WSS information generation unit that generates WSS information representing the spatial distribution and temporal change of the wall shear stress (WSS) based at least on the WSR information and the blood viscosity distribution information, An information generation unit that generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system by evaluating the WSS information generated by the WSS information generation unit based on the relationship between the magnitude of the wall shear stress and the thrombus formation tendency. Including A medical system.

6. The data processing unit generates information regarding a structure formed in a blood vessel based on at least the blood flow information and the WSS information. The medical system according to claim 5.

7. A data reception unit that receives data obtained from the fundus of a patient using at least one optical method; A data processing unit that processes the data received by the data reception unit to generate information regarding the circulatory system of the patient comprising the information regarding the circulatory system includes information regarding a thrombosis tendency; the information regarding the thrombosis tendency includes information regarding blood properties; the information regarding the blood properties includes information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolysis system; the at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement); the data processing unit generates information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolysis system based on at least the blood flow data obtained by the OCT blood flow measurement; the data received by the data reception unit includes a blood flow velocity and a blood vessel diameter calculated based on at least data collected by applying an optical coherence tomography (OCT) scan to the fundus; the data processing unit a WSR calculation unit that calculates a wall shear rate (WSR) based on at least the blood flow velocity and the blood vessel diameter; an information generation unit that generates information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolysis system by evaluating the wall shear rate calculated by the WSR calculation unit based on the relationship between the magnitude of the wall shear rate and the thrombosis tendency comprising a medical information processing device.

8. A data reception unit that receives data obtained from the fundus of a patient using at least one optical method; A data processing unit that processes the data received by the data reception unit to generate information regarding the circulatory system of the patient comprising the information regarding the circulatory system includes information regarding a thrombosis tendency; the information regarding the thrombosis tendency includes information regarding blood properties; the information regarding the blood properties includes information indicating changes in blood properties due to enhancement of the blood coagulation fibrinolysis system; the at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement); The data processing unit generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, based at least on the blood flow data acquired by the OCT blood flow measurement. The data received by the data reception unit includes a blood flow velocity and a blood vessel diameter calculated based at least on data collected by applying an optical coherence tomography (OCT) scan to the fundus oculi. The data processing unit includes a WSR calculation unit that calculates a wall shear rate (WSR) based at least on the blood flow velocity and the blood vessel diameter, a storage unit that stores blood viscosity information acquired in advance, a WSS calculation unit that calculates a wall shear stress (WSS) based at least on the wall shear rate and the blood viscosity information, and an information generation unit that generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, by evaluating the wall shear stress calculated by the WSS calculation unit based on the relationship between the magnitude of the wall shear stress and the thrombus formation tendency. It includes a medical information processing device.

9. A data reception unit that receives data acquired from the fundus oculi of a patient using at least one optical method, a data processing unit that processes the data received by the data reception unit to generate information regarding the circulatory system of the patient It includes wherein the information regarding the circulatory system includes information regarding the thrombus formation tendency, the information regarding the thrombus formation tendency includes information regarding blood properties, the information regarding blood properties includes information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, the at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement), the data processing unit generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, based at least on the blood flow data acquired by the OCT blood flow measurement, the data received by the data reception unit includes blood flow information representing the spatial distribution and temporal change of the blood flow velocity generated based at least on time-series data collected by repeatedly applying an optical coherence tomography (OCT) scan to a predetermined region of the fundus oculi, the data processing unit includes a WSR information generation unit that generates WSR information representing the spatial distribution and temporal change of the wall shear rate (WSR) based at least on the blood flow information. An information generation unit that generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system by evaluating the WSR information generated by the WSR information generation unit based on the relationship between the magnitude of the wall shear rate and the thrombosis tendency comprising a medical information processing device

10. A data reception unit that receives data obtained from the fundus of a patient using at least one optical method, A data processing unit that processes the data received by the data reception unit to generate information regarding the circulatory system of the patient comprising The information regarding the circulatory system includes information regarding the thrombosis tendency, The information regarding the thrombosis tendency includes information regarding blood properties, The information regarding blood properties includes information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system, The at least one optical method includes optical coherence tomography blood flow measurement (OCT blood flow measurement), The data processing unit generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system based at least on the blood flow data obtained by the OCT blood flow measurement, The data received by the data reception unit includes blood flow information representing the spatial distribution and temporal change of the blood flow velocity generated based at least on time-series data collected by repeatedly applying an optical coherence tomography (OCT) scan to a predetermined region of the fundus, The data processing unit A WSR information generation unit that generates WSR information representing the spatial distribution and temporal change of the wall shear rate (WSR) based at least on the blood flow information, A storage unit that stores pre-acquired blood viscosity distribution information, A WSS information generation unit that generates WSS information representing the spatial distribution and temporal change of the wall shear stress (WSS) based at least on the WSR information and the blood viscosity distribution information, An information generation unit that generates information indicating a change in blood properties due to enhancement of the blood coagulation fibrinolytic system by evaluating the WSS information generated by the WSS information generation unit based on the relationship between the magnitude of the wall shear stress and the thrombosis tendency comprising a medical information processing device

11. Further comprising a first transmission unit that transmits the information regarding the circulatory system generated by the data processing unit to a physician terminal located at a remote position with respect to the location where the data received by the data reception unit was obtained The medical information processing device according to any one of Claims 7 to 10

12. The medical information processing apparatus according to claim 11, and the doctor terminal comprising a medical system.

13. A data acquisition device that acquires data from the fundus of the patient using the at least one optical method, and a second transmission unit that transmits the data acquired by the data acquisition device to the medical information processing apparatus further comprising, wherein the data reception unit receives the data transmitted by the second transmission unit, and the data processing unit processes the data transmitted by the second transmission unit and received by the data reception unit to generate information regarding the patient's circulatory system. The medical system according to claim 12.

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