Diagnosis Support System

Abstract

To provide a diagnosis support system which contributes to reduction in a cardiovascular disease risk.SOLUTION: There is provided a diagnosis support system 100 which evaluates a cardiovascular disease risk with calcification of a portion other than cardiac vessels. A diagnosis support device comprises: an acquisition function which acquires image reading information about an image reading result of an image obtained by imaging a patient; a determination function which determines whether or not there exists calcification on the basis of the image reading information; a risk evaluation function which evaluates a cardiovascular disease risk on the basis of medical information of a patient; and an output function which outputs the determination result by the determination function and the evaluation result by the evaluation function.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The embodiments disclosed in the present specification and drawings relate to a diagnosis support system. [Background technology]

[0002] A known method for assessing the risk of developing cardiovascular disease (CVD) (hereinafter referred to as CVD risk) is to calculate a numerical value indicating cardiovascular disease risk (hereinafter referred to as CVD risk value) using age, sex, lifestyle habits, and the results of various health checkups.

[0003] It is necessary to detect patients at high risk of cardiovascular disease early, encourage cardiovascular disease prevention, and reduce the risk of cardiovascular disease. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2013-000347 A Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems that the embodiments disclosed in this specification and the drawings aim to solve is to contribute to reducing the risk of cardiovascular disease. However, the problems that the embodiments disclosed in this specification and the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0006] A diagnostic support system according to an embodiment evaluates the risk of cardiovascular disease using non-cardiovascular calcification as a trigger. The diagnostic support system includes an acquisition unit, a determination unit, an evaluation unit, and an output unit. The acquisition unit acquires interpretation information relating to the results of interpretation of an image taken of a patient. The determination unit determines whether or not calcification is present based on the interpretation information. The evaluation unit evaluates the risk of cardiovascular disease based on the patient's medical information. The output unit outputs the determination result by the determination unit and the evaluation result by the evaluation unit. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a diagnosis support system according to the first embodiment. [Diagram 2] FIG. 2 shows an example of mammary artery calcification found based on a mammography image. [Diagram 3] FIG. 3 is a diagram showing an example of aortic calcification found based on a chest X-ray image. [Figure 4] FIG. 4 shows an example of aortic calcification found based on an axial lung CT image. [Diagram 5] FIG. 5 shows an example of aortic calcification found based on a sagittal lung CT image. [Figure 6] FIG. 6 is a diagram showing an example of calcification of a coronary artery found based on an axial lung CT image. [Figure 7] FIG. 7 is a flowchart illustrating a processing procedure of the diagnosis support processing by the diagnosis support system according to the first embodiment. [Figure 8] FIG. 8 is a diagram showing an example of an image interpretation report acquired by the diagnosis support system according to the first embodiment. [Figure 9] FIG. 9 is a diagram showing an example of an image interpretation report to which the evaluation result of the cardiovascular disease risk has been added by the diagnosis support system according to the first embodiment. [Figure 10]FIG. 10 is a diagram illustrating a data flow when diagnosis support is performed by the diagnosis support system according to the first embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of the configuration of a diagnosis support system according to the second embodiment. As shown in FIG. [Figure 12] FIG. 12 is a flowchart illustrating a processing procedure of the diagnosis support processing by the diagnosis support system according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of a medical image processing apparatus will be described in detail with reference to the drawings. In the following description, components having substantially the same functions and configurations are denoted by the same reference numerals, and repeated description will be given only when necessary.

[0009] (First embodiment) 1 is a diagram showing the configuration of a diagnosis support system 100. The diagnosis support system 100 is connected to a medical examination system 300, an image interpretation system 400, and a medical image diagnostic apparatus 500 via a network 200.

[0010] The network 200 is, for example, a LAN (Local Area Network). The connection to the network 200 may be a wired connection or a wireless connection. If security is ensured by a VPN (Virtual Private Network) or the like, the connection line is not limited to a LAN. It may be connected to a public communication line such as the Internet.

[0011] The medical checkup system 300 manages information related to the medical checkup of patients. The medical checkup system 300 is, for example, a Hospital Information System (HIS) that manages information related to medical facilities such as hospitals. In the medical checkup system 300, electronic medical records (EMR), personal health information (PHR), electronic medical records of subjects, information related to various tests, examination results, etc. are recorded in a storage device. The medical checkup system 300 may be called a medical information system. Also, patients may be called examinees.

[0012] The image interpretation system 400 manages information related to the interpretation of medical images. The image interpretation system 400 is, for example, a medical image management system (Picture Archiving and Communication System: PACS). The image interpretation system 400 associates medical images output from the medical image diagnostic device 500, examination information, image interpretation reports created based on the medical images, and the like, and stores them in a storage device. The image interpretation system 400 includes a display device that displays the medical images output from the medical image diagnostic device 500, an image interpretation report creation device in which an image interpretation doctor who has confirmed the medical images creates an image interpretation report, and the like.

[0013] The medical image diagnostic device 500 is, for example, a mammography device, an X-ray computed tomography device, a magnetic resonance imaging device, an ultrasonic diagnostic device, an X-ray diagnostic device, or the like.

[0014] The diagnosis support system 100 can transmit and receive various information between the medical examination system 300, the image interpretation system 400, and the medical image diagnostic device 500 via the network 200. The diagnosis support system 100 evaluates the risk of developing cardiovascular disease (CVD) (hereinafter referred to as cardiovascular disease risk) using calcification other than that of the cardiovascular system as a trigger. Specifically, the diagnosis support system 100 evaluates the risk of cardiovascular disease using calcification found based on an image (hereinafter referred to as examination image) acquired in an examination other than the heart (hereinafter referred to as an unintended examination) as a trigger. In other words, the diagnosis support system 100 evaluates the risk of cardiovascular disease using calcification obtained based on an image of an examination not intended for an examination of the heart as a trigger. Such examination images are acquired by an examination order for an unintended examination. The examination order for an unintended examination includes information on the examination contents, such as the examination site (site other than the heart), disease name, examination purpose, and modality, i.e., examination information. The test order also includes information such as the test ID, test date, patient ID of the patient undergoing the test, and patient name.

[0015] Cardiovascular disease is a disease in the circulatory system such as the heart and blood vessels. Cardiovascular disease includes, for example, heart disease and vascular disease.

[0016] Calcifications found based on examination images are vascular calcifications. Calcifications found based on examination images obtained in unintended examinations may be called incidental findings. Unintended examinations include, for example, mammography, chest X-ray, and lung CT scans. Examination images include, for example, mammography, chest X-ray, and lung CT scans.

[0017] FIG. 2 is a diagram showing an example of breast artery calcification (BAC) B1 found based on a mammography image A1. FIG. 3 is a diagram showing an example of aortic calcification B2 found based on a chest X-ray image A2. FIG. 4 is a diagram showing an example of aortic calcification B3 found based on a lung field CT image A3 of an axial section (body axis section). FIG. 5 is a diagram showing an example of aortic calcification B4 found based on a lung field CT image A4 of a sagittal section (sagittal section). FIG. 6 is a diagram showing an example of coronary artery calcification B5 found based on a lung field CT image A5 of an axial section.

[0018] The diagnostic support system 100 includes a diagnostic support device 10. The diagnostic support device 10 includes a memory 11, a communication interface 12, a display 13, an input interface 14, and a processing circuit 15. Although the diagnostic support device 10 will be described below as a single device that executes multiple functions, the multiple functions may be executed by separate devices. For example, the functions executed by the diagnostic support device 10 may be distributed and installed in different console devices or workstation devices.

[0019] The memory 11 is a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or an integrated circuit that stores various information. The memory 11 may be a portable storage medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a flash memory, in addition to an HDD or SSD. The memory 11 may be a drive device that reads and writes various information between the memory 11 and a semiconductor memory element such as a flash memory or a RAM (Random Access Memory). The storage area of ​​the memory 11 may be in the diagnosis support device 10 or in an external storage device connected via a network.

[0020] The memory 11 stores programs executed by the processing circuit 15, various data used in the processing of the processing circuit 15, and the like. As the programs, for example, programs that are installed in advance in a computer from a network or a non-transient computer-readable storage medium and cause the computer to realize each function of the processing circuit 15 are used. Note that the various data handled in this specification are typically digital data. The memory 11 is an example of a storage unit.

[0021] The communication interface 12 is a network interface that controls the transmission of communications with the image interpretation system 400, the medical examination system 300, and other external devices via the network 200.

[0022] The display 13 displays various types of information. For example, the display 13 outputs medical information generated by the processing circuit 15, a GUI (Graphical User Interface) for receiving various operations from an operator, and the like. For example, the display 13 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 13 may display an interpretation report. The display 13 is an example of a display unit.

[0023] The input interface 14 receives various input operations from an operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 15. For example, the input interface 14 receives input of medical information, input of various command signals, and the like from the operator. The input interface 14 is realized by a mouse, a keyboard, a trackball, a switch button, a touch screen in which a display screen and a touch pad are integrated, a non-contact input circuit using an optical sensor, and a voice input circuit for performing various processes of the processing circuit 15. The input interface 14 is connected to the processing circuit 15, and converts the input operations received from the operator into electrical signals and outputs them to the control circuit. In this specification, the input interface is not limited to only those having physical operating parts such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the processing circuit 15 is also included as an example of the input interface. The input interface 14 is an example of an input unit.

[0024] The processing circuitry 15 controls the overall operation of the diagnosis support device 10. The processing circuitry 15 is a processor that executes an acquisition function 151, a judgment function 152, a risk assessment function 153, a report creation function 154, and an output function 155 by calling and executing programs in the memory 11.

[0025] 1, the acquisition function 151, the judgment function 152, the risk assessment function 153, the report creation function 154, and the output function 155 are realized by a single processing circuit 15. However, a processing circuit may be configured by combining a plurality of independent processors, and each processor may execute a program to realize each function. Also, the acquisition function 151, the judgment function 152, the risk assessment function 153, the report creation function 154, and the output function 155 may be called an acquisition circuit, a judgment circuit, a risk assessment circuit, a report creation circuit, and an output circuit, respectively, and may be implemented as individual hardware circuits. The above description of each function executed by the processing circuit 15 is the same for each of the following embodiments and modifications.

[0026] Although the diagnosis support device 10 is described as a single console that executes multiple functions, the multiple functions may be executed by different devices. For example, the functions of the processing circuitry 15 may be distributed and installed in different devices.

[0027] The term "processor" used in the above description means a circuit such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an ASIC, a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). The processor realizes its function by reading and executing a program stored in the memory 11. Note that instead of storing a program in the memory 11, the program may be directly built into the circuit of the processor. In this case, the processor realizes its function by reading and executing the program built into the circuit. Note that each processor in this embodiment is not limited to being configured as a single circuit for each processor, and may be configured as one processor by combining multiple independent circuits to realize its function. Furthermore, the multiple components in FIG. 1 may be integrated into one processor to realize its function. The above description of the "processor" is also applicable to the following embodiments and modified examples.

[0028] The processing circuitry 15 acquires information on the results of interpretation of an image of a patient (hereinafter referred to as interpretation information) by the acquisition function 151. The interpretation information includes information on calcification other than cardiovascular calcification. Specifically, the processing circuitry 15 acquires information on the results of interpretation of an examination image as interpretation information by the acquisition function 151. The processing circuitry 15 that realizes the acquisition function 151 is an example of an acquisition unit.

[0029] The image interpretation information is, for example, an image interpretation report, a computer-aided diagnosis (hereinafter referred to as CAD) diagnosis result, an electronic medical record, etc. When the image interpretation information is an image interpretation report, the processing circuitry 15 acquires the image interpretation report from, for example, the image interpretation system 400. When the image interpretation information is a CAD diagnosis result, the processing circuitry 15 acquires the CAD diagnosis result from, for example, the health checkup system 300 or the image interpretation system 400. Alternatively, the processing circuitry 15 may acquire an examination image from the image interpretation system 400 and execute CAD on the examination image to acquire the CAD diagnosis result.

[0030] The processing circuitry 15 determines whether or not calcification is present based on the image reading information by the determination function 152. For example, when the image reading information is an image reading report, the processing circuitry 15 determines whether or not vascular calcification is present based on the calcification finding information described in the image reading report. When the image reading information is a CAD diagnosis result, the processing circuitry 15 determines whether or not vascular calcification is present based on the CAD diagnosis result, for example. The processing circuitry 15 that realizes the determination function 152 is an example of a determination unit.

[0031] When the risk assessment function 153 determines that calcification is present, the processing circuit 15 assesses the risk of cardiovascular disease based on the medical information of the patient who underwent an unintended examination. Specifically, the processing circuit 15 acquires medical information necessary for assessing the patient's risk of cardiovascular disease, and assesses the risk of cardiovascular disease based on the medical information. In this case, the processing circuit 15 acquires, for example, the patient's PHR from the medical examination system 300, and extracts medical information necessary for assessing the risk of cardiovascular disease from the acquired PHR. The processing circuit 15 that realizes the risk assessment function 153 is an example of an evaluation unit.

[0032] The evaluation of the cardiovascular disease risk is, for example, a numerical value indicating the risk of developing cardiovascular disease (hereinafter, referred to as a CVD risk value). In this case, when the processing circuit 15 determines that calcification exists, it acquires medical information necessary for calculating the CVD risk value from the medical examination system 300, and calculates the CVD risk value based on the acquired medical information. Examples of a method for calculating the CVD risk value include CVD risk calculation tools such as ACC / AHH, Framingham, JBS3, Assign Score, and Qrisk2. The CVD risk calculation tool to be used may be preset by the user, or may be selected by the user when calculating the CVD risk value. Also, instead of acquiring the medical information necessary for calculating the CVD risk value from the medical examination system 300, the user may be allowed to input the medical information. The medical information necessary for calculating the CVD risk value differs depending on the CVD risk calculation tool. Examples of the medical information necessary for calculating the CVD risk value include age, sex, total cholesterol value, HDL cholesterol value, blood pressure, presence or absence of a smoking habit, and diabetes (blood sugar level). The CVD risk value may be called a cardiovascular disease risk value.

[0033] When the report creation function 154 determines that calcification is present, the processing circuitry 15 adds the evaluation result to the image interpretation report. Specifically, when the determination function 152 determines that calcification is present, the processing circuitry 15 adds the evaluation result calculated by the risk assessment function 153 to the image interpretation report of the patient. When the evaluation result is a CVD risk value, the processing circuitry 15 adds the CVD risk value to the image interpretation report. The processing circuitry 15 that realizes the report creation function 154 is an example of a report creation section.

[0034] The processing circuitry 15 outputs the determination result on calcification by the determination function 152 and the evaluation result of the cardiovascular disease risk by the risk assessment function 153 through the output function 155. For example, the processing circuitry 15 outputs the evaluation result of the cardiovascular disease risk together with image interpretation information including the image interpretation result on the presence or absence of calcification. In this case, the processing circuitry 15 outputs the evaluation result to the health checkup system 300, the image interpretation system 400, the medical image diagnostic device 500, etc. via the network 200. Also, for example, when the evaluation result is a CVD risk value, the processing circuitry 15 outputs an image interpretation report in which the CVD risk value is described. The processing circuitry 15 that realizes the output function 155 is an example of an output unit.

[0035] Next, the operation of the diagnosis support processing executed by the diagnosis support system 100 will be described. The diagnosis support processing is a processing for acquiring image interpretation information on the image interpretation result of the examination image acquired in the non-intentional examination, judging whether or not calcification is present based on the image interpretation information, evaluating the risk of cardiovascular disease for the patient who underwent an examination other than the heart when it is judged that calcification is present, and outputting the evaluation result together with the image interpretation information. The diagnosis support system 100 executes the diagnosis support processing, for example, based on acquiring new image interpretation information from the image interpretation system 400.

[0036] FIG. 7 is a flowchart showing an example of the procedure of the diagnosis support process. In FIG. 7, as an example, an example will be described in which the unintended examination is a "mammography examination", the examination image is a "mammography image", the interpretation information is an "interpretation report", and a "CVD risk value" is calculated as an evaluation result of cardiovascular disease risk. Note that the processing procedures in each process described below are merely examples, and each process can be appropriately changed as much as possible. Also, steps can be omitted, replaced, and added to the processing procedures described below as appropriate depending on the embodiment.

[0037] (Diagnosis support processing) (Step S101) The processing circuitry 15 acquires an interpretation report for the mammography image from the interpretation system 400 using the acquisition function 151.

[0038] FIG. 8 is a diagram showing an example of an image interpretation report 30 for a mammography image. The image interpretation report 30 shown in FIG. 8 includes a patient information display section 31, a site information display section 32, a mass information display section 33, a calcification information display section 34, a finding information display section 35, and a comment display section 36. The patient information display section 31 displays a patient ID, name, date of birth, sex, and the like. The site information display section 32 displays information for specifying and selecting a site of the breast. The mass information display section 33 displays finding information related to the mass. The mass information display section 33 displays, for example, check boxes for selecting the size and shape of a detected mass. The calcification information display section 34 displays finding information related to calcification of the mammary artery. The calcification information display section 34 displays, for example, check boxes for selecting the nature and form of the detected calcification. The finding information display section 35 displays, for example, finding information related to the mammary gland, skin, lymph node, and the like. In the comment display section 36, for example, comments made by an image interpreting doctor regarding a lesion are displayed in text format.

[0039] (Step S102) The processing circuitry 15 uses the determination function 152 to determine whether or not mammary artery calcification (BAC) is present based on the calcification finding information described in the calcification information display section 34 of the image interpretation report 30. If it is determined that mammary artery calcification is present (step S102-Yes), the processing circuitry 15 sequentially executes the processes from step S103 onward. If it is determined that mammary artery calcification is not present (step S102-No), the process proceeds to step S106.

[0040] (Step S103) If it is determined that mammary artery calcification is present, the processing circuit 15, through the risk assessment function 153, obtains from the medical examination system 300 the PHR of the patient who underwent a mammography examination.

[0041] (Step S104) Processing circuitry 15 extracts medical information necessary for calculating the CVD risk value from the acquired PHR using risk assessment function 153, and executes calculation of the CVD risk value using the extracted information. In this way, the CVD risk value of the patient who underwent a mammography examination is calculated.

[0042] (Step S105) Processing circuitry 15 adds the calculated CVD risk value to the image interpretation report by report creation function 154. Fig. 9 is a diagram showing an example in which a CVD risk value has been added to image interpretation report 30 shown in Fig. 8. In the example shown in Fig. 9, image interpretation report 30 is provided with evaluation display section 37. Evaluation display section 37 displays the calculated CVD risk value.

[0043] (Step S106) The processing circuitry 15 outputs, via the output function 155, an image interpretation report containing the CVD risk value to the medical examination system 300.

[0044] 10 is a diagram showing a schematic diagram of a data flow when diagnosis support is performed by the diagnosis support system 100. As shown in FIG. 10, when diagnosis support is performed by the diagnosis support system 100, first, a radiologist inputs an interpretation result of a mammography image obtained by a mammography examination into the image interpretation system 400, and an image interpretation report for the mammography image is created. The image interpretation system 400 outputs the image interpretation report to the diagnosis support system 100.

[0045] As described above, the diagnosis support system 100 acquires an image interpretation report from the image interpretation system 400, and determines whether or not the acquired image interpretation report contains a description indicating the presence of mammary artery calcification (BAC). If there is no mammary artery calcification, the diagnosis support system 100 outputs the image interpretation report to the medical examination system 300 without adding any description to the image interpretation report. If there is mammary artery calcification, the diagnosis support system 100 acquires a PHR related to the patient from the medical examination system 300. Then, the diagnosis support system 100 calculates a CVD risk value using the acquired PHR. Thereafter, the diagnosis support system 100 outputs the image interpretation report to which the CVD risk value has been added to the medical examination system 300.

[0046] The medical examination system 300 obtains an image interpretation report from the diagnosis support system 100. The medical examination system 300 presents the obtained image interpretation report to the medical examination doctor, the patient's family doctor, or the patient himself / herself. If calcification is present in the mammography image, a CVD risk value is recorded in the image interpretation report.

[0047] The effects of the diagnosis support system 100 according to this embodiment will be described below.

[0048] In recent years, it has become clear that extra-coronary calcification (ECC) can be used as a risk marker for cardiovascular disease. For example, it has become clear that vascular calcification found in the results of mammography and lung CT scans can be used as a risk marker for cardiovascular disease. However, in mammography and lung CT scans, vascular calcification is simply noted in the report as an obviously benign finding, and no particular clinical intervention is made. For this reason, when image findings suggest the possibility of a risk for a disease other than the disease being screened, there is no system in place to effectively use that information.

[0049] The diagnosis support system 100 according to the present embodiment can assess the risk of cardiovascular disease using calcification other than that of the cardiovascular system as a trigger. Specifically, the risk of cardiovascular disease can be assessed using calcification found based on an examination image acquired in an examination other than that of the heart (unintended examination) as a trigger. The calcification is, for example, calcification of blood vessels.

[0050] With the above configuration, the diagnosis support system 100 according to this embodiment can raise interest in other diseases such as cardiovascular disease and recommend examinations for other diseases by taking examinations that are not aimed at heart examinations, such as lung cancer screening and breast cancer screening, without increasing the number of procedures currently performed by radiologists. It can also contribute to the early detection of cardiovascular disease, prevention of cardiovascular disease, and reduction of the risk of cardiovascular disease.

[0051] It is also known that strokes and myocardial infarctions in women increase sharply after the age of 50 (after menopause). It is also known that cardiovascular disease in women often develops after menopause, and that the symptoms are atypical. For this reason, it tends to take a long time for treatment to begin for cardiovascular disease in women, and the prognosis is often poor. However, in general, when interpreting mammography images, calcification in the mammary artery is judged to be "clearly benign calcification." For this reason, even if calcification in the mammary artery is confirmed, no action is taken.

[0052] In the diagnosis support system 100 according to this embodiment, mammography examinations can be treated as non-intentional examinations, and mammography images can be treated as examination images. In this case, under the background that Japanese guidelines recommend undergoing mammography from the age of 40, cardiovascular disease risk can be evaluated using the results of mammography examinations that many women undergo periodically for health checkups, etc. This allows early identification of cardiovascular disease risk in women, who tend to take a long time before starting treatment, leading to the prevention of cardiovascular disease.

[0053] It is also known that smokers are two to three times more likely to develop ischemic heart disease and myocardial infarction than non-smokers.

[0054] In the diagnosis support system 100 according to the present embodiment, a lung CT scan can be treated as an unintended scan, and a lung CT image can be treated as a scan image. Alternatively, a chest X-ray can be treated as an unintended scan, and a chest X-ray image can be treated as a scan image. In this case, for example, the results of lung cancer screening tests such as a chest X-ray test for a non-high-risk group and a low-dose CT test for a high-risk group can be used to evaluate the risk of cardiovascular disease. This allows the risk of cardiovascular disease to be grasped and prevented early for patients at high risk of developing ischemic heart disease or myocardial infarction.

[0055] Furthermore, the diagnosis support system 100 according to this embodiment can obtain image interpretation information on the results of image interpretation of the examination image, determine whether or not calcification is present based on the image interpretation information, and when it is determined that calcification is present, evaluate the risk of cardiovascular disease for the patient who underwent an unintended examination, and output the evaluation result together with the image interpretation information. Furthermore, the diagnosis support system 100 according to this embodiment can output the determination result on the presence of calcification and the evaluation result of the risk of cardiovascular disease.

[0056] In the case where the image interpretation information is an image interpretation report, if it is determined that calcification is present, the diagnosis support system 100 can add an evaluation result to the image interpretation report and output the image interpretation report with the evaluation result added.

[0057] The image interpretation information may also be a computer-aided diagnosis (CAD) result. In this case, for example, the evaluation result of the cardiovascular disease risk and its meaning are described in a letter notifying the CAD diagnosis result to a medical examiner, a patient, or a family doctor.

[0058] The image interpretation report or letter to which the evaluation result of the cardiovascular disease risk has been added is notified to, for example, the medical examination doctor, the family doctor, or the patient from the medical examination system 300. The medical examination doctor or the family doctor can check the evaluation result of the cardiovascular disease risk and consult with the patient about the next action to be taken.

[0059] (Modification of the first embodiment) In this embodiment, an example has been described in which the diagnosis support system 100 is installed as a system separate from the health checkup system 300, the interpretation system 400, and the medical image diagnostic apparatus 500. However, the diagnosis support system 100 may also be installed in a mammography device of the medical image diagnostic apparatus 500, or in a display device for examination images or an interpretation report creation device of the interpretation system 400.

[0060] The image interpretation information may be an electronic medical record. In this case, the diagnosis support system 100 obtains the patient's electronic medical record as the image interpretation information, and determines whether or not vascular calcification is present in the examination image from the unintended examination based on the examination results recorded in the electronic medical record. If vascular calcification is present, the system evaluates the risk of cardiovascular disease, and adds the evaluation result to the electronic medical record and outputs it.

[0061] In addition, the results of periodic examinations may be used to determine whether or not to evaluate the risk of cardiovascular disease based on the change in vascular calcification over time other than that of the cardiovascular system. For example, even if the calcification of blood vessels other than the cardiovascular system is small, if the change in vascular calcification over time is large, the patient may be determined to be at high risk of cardiovascular disease, and the risk of cardiovascular disease may be evaluated.

[0062] In addition to non-cardiovascular vascular calcification, the fat ratio in mammography images may be used to determine whether or not to evaluate the risk of cardiovascular disease. For example, the hormone status of a patient may be estimated based on the fat ratio in mammography images, and the estimation result may be used to determine whether or not to evaluate the risk of cardiovascular disease.

[0063] In addition, the determination result by the machine learning model that stratifies the cardiovascular disease risk may be used as the evaluation of the cardiovascular disease risk. In this case, the evaluation result may be, for example, "high risk", "medium risk", "low risk", etc. Instead of calculating the CVD risk value, the diagnosis support system 100 inputs the medical information of the patient acquired from the medical examination system 300 into the machine learning model and causes the machine learning model to output the determination result regarding the cardiovascular disease risk. Then, the output result of the machine learning model is added to the radiology report or electronic medical record and output as the evaluation of the cardiovascular disease risk. Note that the machine learning model used here is a trained model that generates a determination result regarding the cardiovascular disease risk of the patient based on the medical information of the patient.

[0064] Second embodiment A second embodiment will be described. This embodiment is a modification of the configuration of the first embodiment as follows. Descriptions of configurations, operations, and effects similar to those of the embodiment will be omitted. The diagnosis support system 100 according to this embodiment generates information for supporting reduction of cardiovascular disease risk based on the evaluation result of cardiovascular disease risk, and proposes the information to the patient.

[0065] 11 is a diagram showing the configuration of a diagnosis support system 100 of this embodiment. The processing circuitry 15 executes a support information generating function 156 in addition to the functions described in the first embodiment.

[0066] The processing circuitry 15 generates support information based on the evaluation result of the cardiovascular disease risk by the support information generation function 156. For example, when the patient has a high risk of cardiovascular disease, the processing circuitry 15 presents an action for reducing the cardiovascular disease risk according to the patient's condition in accordance with the CVD risk value. The processing circuitry 15 realizing the support information generation function 156 is an example of a support information generation unit.

[0067] The support information is information for encouraging a patient who is determined to have a high risk of cardiovascular disease to take an action to reduce the risk of cardiovascular disease. The support information is, for example, information on a support application for improving dietary habits, a list of cardiologists near the patient's residence, information on activities that reduce the risk of cardiovascular disease, information encouraging health checkups, etc. The processing circuitry 15 acquires information required for generating the support information from, for example, the health checkup system 300 or the regional medical system, and generates the support information based on the acquired information.

[0068] The processing circuitry 15 outputs the support information generated by the support information generating function 156 together with the interpretation information via the output function 155.

[0069] Next, the operation of the diagnosis support processing executed by the diagnosis support system 100 of this embodiment will be described. Fig. 12 is a flowchart showing an example of the procedure of the diagnosis support processing according to this embodiment. In Fig. 12, as in Fig. 7, an example will be described in which the unintended examination is a "mammography examination", the examination image is a "mammography image", the interpretation information is an "interpretation report", and a "CVD risk value" is calculated as the evaluation result of cardiovascular disease risk. The processing of steps S201-S205 and step S207 is similar to the processing of steps S101-S105 in Fig. 7, respectively, and therefore description thereof will be omitted.

[0070] (Diagnosis support processing) (Step S206) The processing circuit 15 generates support information using the support information generation function 156 based on the calculated CVD risk value.

[0071] (Step S207) The processing circuitry 15 outputs the generated support information to the medical examination system 300 via the output function 155, together with an image interpretation report containing the CVD risk value.

[0072] The medical checkup system 300 presents the obtained support information to the medical examiner, the patient, and the patient's family doctor. For example, the support information is included in an email for sending the medical checkup result to the patient.

[0073] The effects of the diagnosis support system 100 according to this embodiment will be described below.

[0074] The diagnostic support system 100 according to this embodiment can generate support information based on the evaluation of cardiovascular disease risk using the support information generation function 156, based on the evaluation results of cardiovascular disease risk, and output the generated support information together with the interpretation information.

[0075] With the above-described configuration, the diagnosis support system 100 according to this embodiment can contribute to the prevention of cardiovascular disease and reduction of the risk of cardiovascular disease by suggesting actions to be taken to reduce the risk of cardiovascular disease in accordance with the evaluation results to patients who have been determined to be at high risk of cardiovascular disease.

[0076] (Modification of the second embodiment) In some cases, patients undergoing breast cancer screening who are candidates for cardiovascular disease risk assessment may undergo fecal occult blood testing in addition to mammography. In this case, the cardiovascular disease risk estimated based on the patient's gut microbiota may be used as supporting information.

[0077] For example, when the diagnosis support system 100 determines that the patient has a high risk of cardiovascular disease based on the evaluation result of the cardiovascular disease risk, it issues an additional order using a machine learning algorithm that estimates the cardiovascular disease risk based on the patient's intestinal flora. This machine learning algorithm analyzes the intestinal flora from a fecal sample and estimates the presence or absence of cardiovascular disease and the long-term risk of developing cardiovascular disease. The diagnosis support system 100 applies the test results of the fecal occult blood test and the fecal sample to the above-mentioned machine learning algorithm. This makes it possible to provide an evaluation result of the cardiovascular disease risk estimated from the patient's intestinal flora in addition to the calculation result of the CVD risk value based on the patient's medical information.

[0078] The above-mentioned machine learning algorithm may also be used as a method for evaluating the risk of cardiovascular disease. In this case, when the diagnosis support system 100 determines that calcification is present in the examination image, it determines whether or not information on the intestinal flora of the patient is recorded. If information on the intestinal flora is recorded, the diagnosis support system 100 applies the information on the intestinal flora of the patient to the machine learning algorithm to evaluate the risk of cardiovascular disease.

[0079] At least one of the embodiments described above can contribute to reducing the risk of cardiovascular disease.

[0080] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0081] 100...Diagnosis support system 200…Network 300…Medical examination system 400…Image reading system 500…Medical imaging diagnostic equipment 10...Diagnosis support device 11. Memory 12...Communication interface 13…Display 14...Input interface 15...Processing circuit 30…Radiation interpretation report 31...Patient information display section 32…Part information display section 33...Tumor information display section 34...Calcification information display section 35…Finding information display section 36...Comment display section 37...Evaluation display section 151…Acquisition function 152…Judgment function 153...Risk assessment function 154…Report creation function 155…Output function 156…Support information generation function A1…Mammography image A2...Chest X-ray image A3, A4, A5…Lung CT images B1-B5…calcification

Claims

1. A diagnostic support system comprising a processing circuit for evaluating a risk of cardiovascular disease based on information on non-cardiovascular calcification, The processing circuitry includes: Obtaining first interpretation information regarding an interpretation result of an image taken of a patient; determining whether or not the calcification is present based on the first image interpretation information; When it is determined that calcification is present other than in the cardiovascular system, an evaluation of the risk of cardiovascular disease is performed by calculating the risk of developing cardiovascular disease based on medical information other than the image of the patient, and second image interpretation information including the determination result of the calcification, the evaluation result of the risk of cardiovascular disease, and a numerical value indicating the risk of developing cardiovascular disease is output. When it is determined that no calcification is present in any part of the body other than the cardiovascular system, the evaluation of the risk of cardiovascular disease based on medical information other than the image of the patient is not performed, and the first image interpretation information is output. Diagnostic support system.

2. The medical information includes age, sex, total cholesterol level, HDL cholesterol level, blood pressure, smoking habit, and blood glucose level. The diagnosis support system according to claim 1 .

3. a processing circuit for evaluating a risk of developing cardiovascular disease by calculating the risk of developing cardiovascular disease based on information regarding calcifications found based on images obtained in a non-cardiac examination; The processing circuitry includes: obtaining first interpretation information regarding an interpretation result of the image; determining whether or not the calcification is present based on the first image interpretation information; When it is determined that calcification is present in a part other than the heart, an evaluation of the risk of cardiovascular disease is performed based on medical information other than the image, and second image interpretation information including the determination result of the calcification, the evaluation result of the risk of cardiovascular disease, and a numerical value indicating the risk of developing cardiovascular disease is output; When it is determined that no calcification is present in any part other than the heart, the evaluation of the risk of cardiovascular disease based on medical information other than the image is not performed, and the first image interpretation information is output. Diagnostic support system.

4. The calcification is vascular calcification. The diagnosis support system according to claim 3 .

5. the non-cardiac examination is a mammography examination; The image is a mammography image. The diagnosis support system according to claim 3 or 4.

6. The examination other than the heart is a lung CT examination, The image is a lung CT image. The diagnosis support system according to claim 3 or 4.

7. the non-cardiac examination is a chest x-ray; The image is a chest x-ray image. The diagnosis support system according to claim 3 or 4.

8. The first image reading information is an image reading report. The diagnosis support system according to claim 3 .

9. a report creation unit that adds the evaluation result to the image interpretation report when it is determined that the calcification is present; The processing circuit outputs an image interpretation report to which the evaluation result is added. The diagnosis support system according to claim 8.

10. The first image interpretation information is a diagnosis result of a computer-aided diagnosis. The diagnosis support system according to claim 3 .

11. a support information generating unit that generates support information based on the evaluation result of the cardiovascular disease risk, The processing circuitry outputs the support information together with the first interpretation information. The diagnosis support system according to any one of claims 3 to 10.

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