Diagnosis assisting apparatus, diagnosis assisting system, and non-

Abstract

To enable a medical worker to more easily determine the probability that a person to be examined has a disease after examination.SOLUTION: A score calculation unit (202) configured to calculate a risk score indicating a possibility of having a disease for medical dataset using a AI model (300) created by learning using teacher medical dataset, and a determination unit (203) configured to determine a category class to which the risk score belongs from three or more category classes using a diagnosability indicator of the AI model. 201.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

[0002] In recent years, the practical application of AI (Artificial Intelligence) technology has progressed rapidly, and methods have been proposed for automatically analyzing electrocardiogram waveforms by treating them as time-series data or images and using machine learning or deep learning (AI learning) models. Patent Document 1 proposes a method for generating data for electrocardiogram analysis that enables efficient deep learning of the relationships between multiple types of waveform information and leads in an electrocardiogram. Patent Document 2 proposes a method for generating data representing a two-dimensional image having a waveform image region and a rhythm image region as data for electrocardiogram analysis. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2020-130772 [Patent Document 2] Japanese Patent Publication No. 2022-054202 Summary of the Invention [Problem to be solved by the invention]

[0004] The classification model obtained through learning represents the probability that a test subject has an injury or disease as a continuous risk score ranging from 0 to 1. The higher the risk score, the higher the probability that the test subject has an injury or disease. However, this only indicates a qualitative trend. Even if the same risk score is output, if the performance of the AI ​​model differs, the diagnostic ability of the corresponding model will also differ. Therefore, it is difficult for medical professionals to accurately determine the probability that a test subject will have an injury or disease after an AI test based solely on the risk score value. Some aspects of the present invention aim to provide technology that enables medical professionals to more easily determine the probability that a test subject will have an injury or disease after the test. [Means for solving the problem]

[0005] According to some embodiments, a diagnostic support device is provided, which includes a data acquisition unit that acquires medical data on a subject to be examined, a score calculation unit that calculates a risk score representing the possibility of the subject having an illness or injury for the medical data using an AI model created by learning using training medical data, and a determination unit that determines the classification class to which the risk score belongs from three or more classification classes using a diagnostic performance index of the AI ​​model. [Effects of the Invention]

[0006] Some embodiments allow medical personnel to more easily determine the probability that a test subject has a disease or injury following a test. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 2 is a block diagram illustrating an example of the hardware configuration of the diagnosis support device according to the first embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of the functional configuration of a diagnosis support device according to the first embodiment. [Figure 3] FIG. 2 is a block diagram illustrating an example of an AI model according to the first embodiment. [Figure 4] FIG. 2 is a schematic diagram illustrating an example of a classification class according to the first embodiment. [Figure 5]FIG. 3 is a diagram for explaining an example of a method for determining a recommended action according to the first embodiment. [Figure 6] FIG. 2 is a flow chart illustrating an example of a method executed by the diagnosis support device of the first embodiment. [Figure 7] FIG. 2 is a flow chart illustrating an example of a method executed by the diagnosis support device of the first embodiment. [Figure 8] FIG. 3 is a schematic diagram showing an example of a screen according to the first embodiment. [Figure 9] FIG. 1 is a block diagram illustrating an example of the configuration of a diagnosis support system according to a first embodiment. [Figure 10] FIG. 10 is a block diagram illustrating an example of the hardware configuration of a diagnosis support device according to a second embodiment. [Figure 11] FIG. 10 is a block diagram illustrating an example of the flow of processing executed by a processor of a diagnosis support device according to a second embodiment. [Figure 12] FIG. 10 is a schematic diagram illustrating an example of a classification class output in the second embodiment. [Figure 13] FIG. 10 is a schematic diagram illustrating an example of an AI output display according to the second embodiment. [Figure 14] FIG. 10 is a schematic diagram illustrating another example of an AI output display according to the second embodiment. [Figure 15] FIG. 10 is a schematic diagram illustrating another example of an AI output display according to the second embodiment. [Figure 16] FIG. 10 is a diagram for explaining an example of a method for determining a recommended action according to the second embodiment. [Figure 17] FIG. 10 is a diagram for explaining another example of a method for determining a recommended action in the second embodiment. [Figure 18] FIG. 10 is a block diagram illustrating an example of the configuration of a diagnosis support system according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the invention according to the claims, and not all combinations of features described in the embodiments are necessarily essential to the invention. Two or more of the features described in the embodiments may be arbitrarily combined. Furthermore, the same reference numerals are used for the same or similar components, and redundant explanations will be omitted.

[0009] First Embodiment A first embodiment of the present invention relates to a diagnostic support device. A diagnostic support device is a device that supports diagnoses performed by medical professionals such as doctors, dentists, and veterinarians. The person to be diagnosed (hereinafter referred to as the test subject) may be a human being or another animal (for example, livestock or pets). A person diagnosed with an injury or illness may also be called a patient.

[0010] Diagnosis is the determination of whether a test subject has an illness or injury. For example, it may be determined whether a test subject has a specific illness or injury, or it may be determined whether a test subject has some illness or injury, even if the name of the illness or injury cannot be identified. In the following description, if a test subject has an illness or injury, it is referred to as the test subject being positive, and if a test subject does not have an illness or injury, it is referred to as the test subject being negative.

[0011] The diagnosis support device assists in diagnosis using medical data related to the subject. Medical data is data that can serve as the basis for a diagnosis. For example, the medical data may include data collected from the subject using a testing device (e.g., electrocardiogram data, X-rays, blood test results, height, weight). The medical data may also include findings on the subject by a medical professional. The medical data may also include attributes of the subject (e.g., age, gender, lifestyle, medical history). The medical data may also include family history or genetic information of the subject, and may also include attributes of the subject's blood relatives (e.g., parents, brothers, sisters).

[0012] [Example of hardware configuration for diagnostic support device] An example of the hardware configuration of a diagnosis support device 100 according to the first embodiment will be described with reference to FIG. 1. The diagnosis support device 100 includes the components shown in FIG. 1. The diagnosis support device 100 may include only one of each component shown in FIG. 1, or may include multiple components. The diagnosis support device 100 may not include some of the components shown in FIG. 1, or may include components not shown in FIG. 1. For example, the diagnosis support device 100 may include an inspection device for collecting medical data from a subject. In this case, the diagnosis support device 100 may be called an inspection device having a diagnosis support function.

[0013] The processor 101 is a device that controls the overall operation of the diagnostic support device 100. The processor 101 is configured, for example, by a CPU (Central Processing Unit). The memory 102 is a device that stores programs and temporary data required for the operation of the diagnostic support device 100. The memory 102 is configured, for example, by a RAM (Random Access Memory) or a ROM (Read Only Memory). The operation of the diagnostic support device 100 may be performed, for example, by the processor 101 executing a program stored in the memory 102. Alternatively, part or all of the operation of the diagnostic support device 100 may be performed by a dedicated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). Furthermore, the processor 101 may include a GPU (Graphical Processing Unit). A device having the processor 101 and the memory 102, such as the diagnostic support device 100, may also be called a computer.

[0014] The input device 103 is a device for acquiring input from a user of the diagnostic support device 100 (e.g., a medical professional; hereinafter, the user of the diagnostic support device 100 will be simply referred to as the user). The input device 103 is configured, for example, by a keyboard, a mouse, a touch panel, etc. The output device 104 is a device for outputting to the user. The output device 104 is configured, for example, by a display and a speaker. In the example of FIG. 1 , the input device 103 is described as constituting a part of the diagnostic support device 100, but the input device 103 may be a device separate from the diagnostic support device 100. In this case, the diagnostic support device 100 includes an input interface for connecting to the input device 103. Similarly, the output device 104 may be a part of the diagnostic support device 100, or may be a device separate from the diagnostic support device 100.

[0015] The communication device 105 is a device that enables the diagnosis assistance device 100 to communicate with other devices. The other devices may be computers connected to a network (e.g., the Internet or a local area network). For example, the communication device 105 may be used to acquire medical data from the other devices.

[0016] The storage device 106 is a device that stores data used in the operation of the diagnostic support device 100. The storage device 106 is configured by a storage medium such as a hard disk drive (HDD), a solid state drive (SSD), or a DVD (digital versatile disc). At least a portion of the data stored in the storage device 106 may be stored in a device external to the diagnostic support device 100, such as a cloud environment, and may be read out during processing by the diagnostic support device 100.

[0017] [Example of functional configuration of diagnostic support device] An example of the functional configuration of the diagnostic support device 100 will be described with reference to Fig. 2. The diagnostic support device 100 may not include some of the functions shown in Fig. 2, or may include functions not shown in Fig. 2. Each function of the diagnostic support device 100 may be realized by the processor 101 executing a program loaded into the memory 102. At least some of the functions of the diagnostic support device 100 may be realized by a dedicated circuit such as an ASIC or FPGA.

[0018] The data acquisition unit 201 acquires data used in the diagnosis support device 100. For example, the data acquisition unit 201 acquires medical data related to a subject. The data acquisition unit 201 may acquire medical data input by a user to the input device 103. The data acquisition unit 201 may acquire medical data from a device external to the diagnosis support device 100 (e.g., a file server or an examination device) using the communication device 105. If the diagnosis support device 100 includes an examination device, the data acquisition unit 201 may acquire medical data from the examination device. The medical data acquired by the data acquisition unit 201 may be stored in the memory 102 or the storage device 106 for subsequent processing.

[0019] The data acquisition unit 201 also acquires verification medical data. The verification medical data is medical data labeled with whether the provider of the verification medical data is positive or negative. The provider of the verification medical data is the person who created the medical data. For example, the medical data may include data about the provider that is similar to the medical data about the test subject described above.

[0020] The verification medical data is used to calculate a likelihood ratio of the AI ​​model 300 (described later) used by the diagnosis support device 100. The verification medical data has the same data structure as the medical data used to diagnose the test subject, except that it is labeled. The data acquisition unit 201 may acquire medical data from an external device (e.g., a file server) of the diagnosis support device 100 using the communication device 105. The data acquisition unit 201 acquires multiple pieces of verification medical data. The multiple pieces of verification medical data may include medical data acquired from the same provider at different times, or may include medical data acquired from different providers. The verification medical data acquired by the data acquisition unit 201 may be stored in the memory 102 or the storage device 106 for subsequent processing.

[0021] The score calculation unit 202 calculates a risk score that indicates the possibility that the person of the medical data has an injury or disease (i.e., is positive) using the AI ​​model 300. The person of the medical data may be the test subject or the provider of the verification medical data.

[0022] As shown in Figure 3, the AI ​​model 300 outputs a risk score for the input medical data. In the following description, the risk score can take a value in the range of 0 to 1. Alternatively, the risk score may take a value in another range. The higher the risk score, the more likely the person involved in the medical data is positive, and the lower the risk score, the more likely the person involved in the medical data is negative.

[0023] The AI ​​model 300 is created by machine learning using training medical data. Machine learning is a technique in which a computer independently finds patterns potentially contained in data and uses those patterns to perform regression, classification, and the like. In this specification, machine learning includes deep learning. Machine learning may also be referred to as AI learning. The AI ​​model 300 may have any structure. For example, the AI ​​model 300 may be configured as a neural network, a convolutional neural network, or a transformer.

[0024] The teacher medical data is medical data that has been labeled as positive or negative by the provider of the teacher medical data. The teacher medical data has the same data structure as the verification medical data described above. The teacher medical data is used to train the AI ​​model 300 through machine learning. The score calculation unit 202 calculates a risk score using the trained AI model 300. The machine learning of the AI ​​model 300 may be performed by the diagnosis support device 100 or by another device.

[0025] In one example, the medical data is electrocardiogram data, and the risk score represents the likelihood that the person of the electrocardiogram data has paroxysmal atrial fibrillation (PAF). In this case, the teacher medical data includes electrocardiogram data of a patient with paroxysmal atrial fibrillation (i.e., electrocardiogram data labeled as being positive for paroxysmal atrial fibrillation) and electrocardiogram data of a donor who does not have paroxysmal atrial fibrillation (i.e., electrocardiogram data labeled as being negative for paroxysmal atrial fibrillation).

[0026] Diseases such as PAF, which manifest spontaneously or suddenly, are known to be difficult to detect because symptoms often do not appear at the time of testing. For example, depending on the timing of the electrocardiogram data acquisition of a PAF patient, the electrocardiogram data may show sinus rhythm, making PAF difficult to detect. This type of PAF is sometimes called "hidden atrial fibrillation." Using the AI ​​model 300, it is possible to determine with a certain degree of accuracy whether a test subject is positive for PAF.

[0027] The class determination unit 203 determines, from three or more classification classes, the classification class to which the risk score calculated by the AI ​​model 300 belongs. The three or more classification classes divide the range that the risk score can take (i.e., the range from 0 to 1). Below, a case will be described in which the class determination unit 203 determines, from four classification classes, the classification class to which the risk score belongs. The same explanation applies when the class determination unit 203 determines, from three or five or more classification classes, the classification class to which the risk score belongs.

[0028] The four classification classes will be described with reference to Figure 4. The range that the risk score can take (i.e., the range from 0 to 1) is divided into four ranges by three boundary values ​​BL, BM, and BH. The boundary value BL (first boundary value) is greater than 0 and less than the boundary value BM. The boundary value BM (third boundary value) is greater than the boundary value BL and less than the boundary value BH. The boundary value BH (second boundary value) is greater than the boundary value BM and less than 1. The method for determining the boundary values ​​BL, BM, and BH will be described later.

[0029] The class determination unit 203 determines that a risk score belongs to the classification class "low" when the risk score is within a range equal to or greater than 0 and less than the boundary value BL. The class determination unit 203 determines that a risk score belongs to the classification class "middle-low" when the risk score is within a range equal to or greater than the boundary value BL and less than the boundary value BM. The class determination unit 203 determines that a risk score belongs to the classification class "middle-high" when the risk score is within a range equal to or greater than the boundary value BM and less than the boundary value BH. The class determination unit 203 determines that a risk score belongs to the classification class "high" when the risk score is within a range equal to or greater than the boundary value BH and less than 1. In the above example, when a risk score is equal to a boundary value, the risk score is determined to belong to a class whose lower limit is the boundary value. Alternatively, when a risk score is equal to a boundary value, the risk score may be determined to belong to a class whose upper limit is the boundary value. As shown in FIG. 4, the range of possible risk scores is unequally divided by the four classification classes. Alternatively, the range of possible risk scores may be evenly divided.

[0030] The class presentation unit 204 presents the classification class determined by the class determination unit 203 to the user. A specific example of a method for presenting the classification class to the user will be described later. The action presentation unit 205 presents a recommended action to the user based on the classification class determined by the class determination unit 203. For example, if the risk score is determined to belong to the classification class "high," the action presentation unit 205 recommends the user to consider conducting a detailed examination on the test subject. If the risk score is determined to belong to the classification class "middle-high," the action presentation unit 205 recommends the user to consider conducting regular examinations on the test subject. If the risk score is determined to belong to the classification class "middle-low," the action presentation unit 205 recommends the user to consider conducting a simple examination that is less burdensome on the test subject. If the risk score is determined to belong to the classification class "low," the action presentation unit 205 recommends the user not to conduct additional examinations on the test subject. A specific example of a method for presenting recommended actions to the user will be described later.

[0031] The diagnostic performance index calculation unit 206 calculates the diagnostic performance index of the AI ​​model 300 using the verification medical data. The diagnostic performance index is an index that represents the diagnostic ability of the AI ​​model 300. Specifically, the diagnostic performance index calculation unit 206 calculates the sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of the AI ​​model 300 as diagnostic performance indices. Sensitivity is a diagnostic performance index that represents the ability of the AI ​​model 300 to determine a positive individual as positive. Specificity is a diagnostic performance index that represents the ability of the AI ​​model 300 to determine a negative individual as negative. The positive likelihood ratio is a diagnostic performance index that represents how likely a positive individual is to be positive compared to a negative individual. The positive likelihood ratio is sometimes expressed as "LR+". The negative likelihood ratio is a diagnostic performance index that represents how likely a positive individual is to be negative compared to a negative individual. The negative likelihood ratio is sometimes expressed as "LR-". The positive likelihood ratio and negative likelihood ratio of the AI ​​model 300 are calculated based on the sensitivity and specificity using the following formulas, respectively. Positive likelihood ratio = sensitivity / (1-specificity), Negative likelihood ratio = (1 - sensitivity) / specificity).

[0032] The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of the AI ​​model 300 vary depending on the cutoff value. Therefore, the diagnostic performance index calculation unit 206 calculates the sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of the AI ​​model 300 for each of multiple cutoff values ​​based on the risk score. The specificity, positive likelihood ratio, and negative likelihood ratio monotonically increase as the cutoff value increases. The sensitivity monotonically decreases as the cutoff value increases.

[0033] The boundary value determination unit 207 determines boundary values ​​of three or more classification classes for classifying risk scores. For example, the boundary value determination unit 207 determines the three boundary values ​​BL, BM, and BH in Fig. 4. The boundary value determination unit 207 may determine the boundary values ​​BL, BM, and BH based on the diagnostic performance index (specifically, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio) of the AI ​​model 300 calculated by the diagnostic performance index calculation unit 206.

[0034] For example, the boundary value determination unit 207 sets the largest cutoff value among the multiple cutoff values ​​used to calculate the diagnostic performance index of the AI ​​model 300, at which the negative likelihood ratio is equal to or less than a first predetermined value A, as the boundary value BL. The first predetermined value A is a likelihood ratio that is clinically useful for determining a negative result. The boundary value determination unit 207 may also set another value based on the negative likelihood ratio (for example, the cutoff value among the multiple cutoff values ​​at which the negative likelihood ratio is closest to the above-mentioned first predetermined value A) as the boundary value BL.

[0035] The boundary value determination unit 207 determines the smallest cutoff value among the multiple cutoff values ​​used to calculate the diagnostic performance index of the AI ​​model 300, at which the positive likelihood ratio is equal to or greater than the second predetermined value B, as the boundary value BH. The second predetermined value B is a likelihood ratio that is clinically useful for determining a positive result. The boundary value determination unit 207 may also determine another value based on the positive likelihood ratio (for example, the cutoff value among the multiple cutoff values ​​at which the positive likelihood ratio is closest to the above-mentioned second predetermined value) as the boundary value BH.

[0036] The boundary value determination unit 207 determines the boundary value BM as an optimal cutoff value that maximizes the sum of, for example, sensitivity and specificity, among the multiple cutoff values ​​used to calculate the diagnostic performance index of the AI ​​model 300. The boundary value determination unit 207 may also determine another value between the boundary values ​​BL and BH (for example, the average value of the boundary values ​​BL and BH) as the boundary value BM. For example, the boundary value determination unit 207 may determine the boundary value BM based on a value that is based on sensitivity and specificity and is different from the likelihood ratio, among the multiple cutoff values ​​used to calculate the diagnostic performance index of the AI ​​model 300.

[0037] When the class determination unit 203 determines the classification class to which a risk score belongs from three classification classes, any one of the three boundary values ​​BL, BM, and BH described above does not need to be used. For example, when the classification class "Low" and the classification class "Middle-Low" are combined, the boundary value BL is not used. When the classification class "Middle-Low" and the classification class "Middle-High" are combined, the boundary value BM is not used. When the classification class "Middle-High" and the classification class "High" are combined, the boundary value BH is not used.

[0038] As described above, the boundary value determination unit 207 determines multiple boundary values ​​based on the diagnostic performance index of the AI ​​model 300. The boundary values ​​divide the range that the risk score can take into three or more classification classes. Therefore, the range that the risk score can take is generally divided unevenly by the three or more classification classes.

[0039] The boundary value determined based on the diagnostic performance index of the AI ​​model 300 may differ depending on the performance of the AI ​​model 300. For example, suppose that the boundary value BH is determined for an AI model with relatively high performance (hereinafter referred to as a high-performance AI model) and an AI model with relatively low performance (hereinafter referred to as a low-performance AI model) using the same N pieces of validation medical data. In this case, the boundary value BH determined for the high-performance AI model will be lower than the boundary value BH determined for the low-performance AI model. As a result, even if the high-performance AI model and the low-performance AI model output the same risk score, the high-performance AI model is more likely to belong to the classification class "high." As such, in this embodiment, by determining the boundary value based on the diagnostic performance index of the AI ​​model 300, the risk score to which the classification class belongs can be accurately determined.

[0040] The reliability of the risk score output by the AI ​​model 300 varies depending on the performance of the AI ​​model 300. For example, suppose that both an AI model with relatively high performance and an AI model with relatively low performance output a risk score of "0.8." In this case, even if the same risk score is output, referring to the AI ​​model with relatively high performance increases the likelihood of correctly diagnosing the test subject. According to this embodiment, multiple boundary values ​​are determined based on the diagnostic performance index of the AI ​​model 300. Therefore, the values ​​of the multiple boundary values ​​may differ depending on the performance of the AI ​​model 300.

[0041] The probability value acquiring unit 208 acquires a prior probability value of an injury or disease based on the user's findings on the test subject. The prior probability value is a probability value that the test subject will test positive, determined by the user based on the test subject's findings before or after the diagnosis assistance device 100 presents the user with a risk score. The probability value acquiring unit 208 may acquire the prior probability value from the user using, for example, the input device 103. The probability value acquiring unit 208 may determine whether to acquire the prior probability value of an injury or disease, and acquire the prior probability value of an injury or disease from the user if it is determined that the prior probability value of an injury or disease should be acquired. The probability value acquiring unit 208 may determine whether to acquire the prior probability value of an injury or disease based on, for example, attribute information of the test subject (age, gender, medical history, test history), whether the test subject has a pacemaker, whether noise was mixed into the test subject's waveform during the test, and, if noise was mixed into the waveform, the type of noise.

[0042] The action presenting unit 205 may present a recommended action to the user based on the classification class determined by the class determining unit 203 and the prior probability value acquired by the probability value acquiring unit 208. For example, the action presenting unit 205 may determine an action by referring to table 500 in FIG. 5. Table 500 is generated by the manufacturer of the diagnosis support device 100 and stored in the memory 102 or the storage device 106 of the diagnosis support device 100.

[0043] The rows of table 500 represent the classification classes determined by class determination unit 203. The columns of table 500 represent whether the prior probability value acquired by probability value acquisition unit 208 is higher or lower than a predetermined threshold (e.g., 50%), and may be composed of one column or two or more columns. "Detailed Examination" in table 500 indicates that the recommended action is to consider conducting a detailed examination for the test subject. "Regular Examination" in table 500 indicates that the recommended action is to consider conducting a regular examination for the test subject. "Simple Examination" in table 500 indicates that the recommended action is to consider conducting a simple examination that is less burdensome for the test subject. "No Examination" in table 500 indicates that the recommended action is not to conduct an additional examination for the test subject. The recommended actions may include other actions not shown in table 500.

[0044] [Method for determining classification class boundary values] An example of a method for determining boundary values ​​of classification classes will be described with reference to Fig. 6. The method of Fig. 6 is executed by the diagnostic support device 100. The method of Fig. 6 may be started in response to an instruction to execute the method of Fig. 6 from a user of the diagnostic support device 100.

[0045] In S601, the data acquisition unit 201 of the diagnosis support device 100 acquires verification medical data as described with reference to FIG.

[0046] In S602, the diagnostic performance index calculation unit 206 of the diagnosis support device 100 calculates diagnostic performance indexes (e.g., sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio) of the AI ​​model 300 using the verification medical data acquired in S601, as described with reference to Fig. 2. The AI ​​model 300 may be stored in the memory 102 or the storage device 106 at the time of executing S602, or may be acquired from an external device at the time of executing S602.

[0047] In S603, the boundary value determination unit 207 of the diagnosis support device 100 determines a plurality of boundary values ​​(e.g., boundary values ​​BL, BM, and BH) of the classification classes based on the diagnostic performance index calculated in S602, as described with reference to Fig. 2. The boundary value determination unit 207 stores the determined plurality of boundary values ​​in the memory 102 or the storage device 106 for subsequent processing.

[0048] When multiple AI models 300 are available, the diagnostic support device 100 may determine multiple boundary values ​​by executing S602 and S603 for each AI model 300. In this case, the diagnostic support device 100 stores multiple corresponding boundary values ​​in association with the AI ​​model 300.

[0049] [Methods to aid in diagnosis] An example of a method for assisting diagnosis will be described with reference to Fig. 7. The method of Fig. 7 is executed by the diagnosis assistance device 100. The method of Fig. 7 may be started in response to an instruction to execute the method of Fig. 7 from a user of the diagnosis assistance device 100.

[0050] An example of a screen 800 for obtaining instructions from a user to execute the method of Fig. 7 will be described with reference to Fig. 8. The screen 800 is displayed on, for example, the output device 104 of the diagnosis support device 100. An input field 801 is a graphic object for obtaining designation of medical data from a user. The medical data may be designated by, for example, the address of a file storing the medical data.

[0051] The input field 802 is a graphic object for obtaining a prior probability value from the user. The prior probability value is specified, for example, as a value between 0 and 1. If the prior probability value is not used to determine the recommended action, the screen 800 does not need to include the input field 802.

[0052] The button 803 is a graphic object for obtaining an instruction from the user to execute the method of Fig. 7. The diagnosis assistance device 100 starts the method of Fig. 7 in response to the button 803 being pressed by the user.

[0053] In S701, the data acquisition unit 201 of the diagnosis support device 100 acquires medical data of the test subject. The address of the medical data of the test subject is specified in the input field 802. In S702, the score calculation unit 202 of the diagnosis support device 100 calculates a risk score for the medical data acquired in S701, as described with reference to FIG. 2. The AI ​​model 300 may be stored in the memory 102 or the storage device 106 at the time of executing S702, or may be acquired from an external device at the time of executing S702. If the diagnosis support device 100 is capable of using multiple AI models 300, the diagnosis support device 100 may acquire, from the user, designation of the AI ​​model 300 to be used.

[0054] In S703, the class determination unit 203 of the diagnosis support device 100 determines the classification class to which the risk score calculated in S702 belongs, as described with reference to Fig. 2. In S703, the boundary value determined by the method of Fig. 6 and corresponding to the AI ​​model 300 used in S702 is used.

[0055] In S704, the class presenting unit 204 of the diagnosis support device 100 presents the classification class determined in S703 to the user. Furthermore, the action presenting unit 205 of the diagnosis support device 100 presents recommended actions to the user based on the classification class determined in S703 (and the prior probability value if entered in the input field 802).

[0056] Referring again to Figure 8, an example of a screen 810 used to present classification classes and recommended actions to a user will be described. Screen 810 includes bar-shaped regions 811 representing multiple classification classes (four in the example of Figure 8). Region 811 is divided into a portion 811L corresponding to the classification class "Low," a portion 811ML corresponding to the classification class "Middle-Low," a portion 811MH corresponding to the classification class "Middle-High," and a portion 811H corresponding to the classification class "High." These four portions are arranged horizontally in the order shown in Figure 8.

[0057] Screen 810 includes indicator 812 that indicates the classification class determined in S703. In the example of FIG. 8, indicator 812 has a triangular shape. Indicator 812 may have other shapes. In the example of FIG. 8, indicator 812 is displayed in a position that points to portion 811MH from above. This indicates to the user that the risk score was determined to belong to the classification class "middle-high" in S703.

[0058] The indicator 812 may present to the user the relative position of the risk score calculated for the test subject relative to the boundary values ​​of the four classification classes (the above-mentioned boundary values ​​BL, BM, and BH). Specifically, the left end of portion 811L represents the position where the risk score is 0. The boundary line between portion 811L and portion 811ML represents the position where the risk score is boundary value BL. The boundary line between portion 811ML and portion 811MH represents the position where the risk score is boundary value BM. The boundary line between portion 811MH and portion 811H represents the position where the risk score is boundary value BH. The right end of portion 811H represents the position where the risk score is 1.

[0059] As shown in Figure 8, it is assumed that the risk score is determined to belong to the classification class "middle-high," and the risk score divides the lower limit (i.e., boundary value BM) and upper limit (i.e., boundary value BH) of the classification class "middle-high" internally at a:b. In this case, indicator 812 is positioned to point to the position where the left end and right end of portion 811MH are divided internally at a:b. By positioning indicator 812 in this way, the user can recognize how close the risk score is to other classification classes, making it even easier for the user to make a diagnosis.

[0060] The screen 810 may include an indicator 813 that indicates the relative position of a predetermined positive likelihood ratio (1, 2, 3.25, 5, and 10 in the example of FIG. 8 ) of the AI ​​model 300 relative to the boundary values ​​of the four classification classes (the above-mentioned boundary values ​​BL, BM, and BH). In the example of FIG. 8 , the indicator 813 has a bar shape. The indicator 813 is added to the top of the region 811. "LR+" on the screen 810 indicates that the indicator 813 added to the top of the region 811 represents a positive likelihood ratio. There is a one-to-one correspondence between the positive likelihood ratio and the cutoff value. The indicator 813 is placed at a position that indicates the cutoff value corresponding to the predetermined positive likelihood ratio. As described above for the indicator 812, the position indicated by the cutoff value is the position that divides both ends of the classification class that includes this cutoff value internally.

[0061] The screen 810 may include an indicator 814 that indicates the relative position of a predetermined negative likelihood ratio (0, 0.1, 0.2, 0.36, 0.5, and 1 in the example of FIG. 8 ) of the AI ​​model 300 relative to the boundary values ​​of the four classification classes (the above-mentioned boundary values ​​BL, BM, and BH). In the example of FIG. 8 , the indicator 814 has a bar shape. The indicator 814 is added to the bottom of the region 811. "LR-" on the screen 810 indicates that the indicator 814 added to the bottom of the region 811 represents a negative likelihood ratio. There is a one-to-one correspondence between the negative likelihood ratio and the cutoff value. The indicator 814 is placed at a position that indicates the cutoff value corresponding to the predetermined negative likelihood ratio. As described above for the indicator 812, the position indicated by the cutoff value is the position that divides both ends of the classification class that includes this cutoff value internally.

[0062] On the screen 810, of the four parts included in the area 811, only the part corresponding to the classification class to which the risk score is determined to belong is displayed, and the other three parts may not be displayed. For example, in the example of Fig. 8, the part 811MH and the indicators 812 to 814 pointing to it may be displayed, and the parts 811L, 811ML, and 811H and the indicators 813 to 814 pointing to them may not be displayed.

[0063] The screen 810 includes a field 815 for displaying a recommended action. The action presenting unit 205 of the diagnosis support device 100 may display a sentence indicating the determined action in the field 815, as described with reference to Fig. 2. When the diagnosis support device 100 does not present a recommended action to the user, the screen 810 does not need to include the field 815.

[0064] The information displayed on screen 810 may be presented to the user in other formats. For example, the information displayed on screen 810 may be presented to the user in a printed form, such as a report. Furthermore, the information displayed on screen 810 may be used by other programs in addition to or instead of being presented to the user.

[0065] In the above-described embodiment, the method of FIG. 6 and the method of FIG. 7 are executed by the same diagnostic support device 100. Alternatively, the method of FIG. 6 and the method of FIG. 7 may be executed by different devices. For example, a device other than the diagnostic support device 100 may execute the method of FIG. 6, and the diagnostic support device 100 may execute the method of FIG. 7 using the result of the method of FIG. 6 (i.e., the boundary value). Also, in the method of FIG. 7, instead of the diagnostic support device 100 calculating the risk score using the AI ​​model 300, the diagnostic support device 100 may calculate the risk score using an AI model 300 other than the diagnostic support device 100, and the diagnostic support device 100 may acquire this risk score. Furthermore, the method of FIG. 6 may be executed by multiple devices working together, and the method of FIG. 7 may be executed by multiple devices working together.

[0066] [Example of diagnostic support system configuration] An example configuration of a diagnostic support system 900 according to the first embodiment will be described with reference to Fig. 9. The diagnostic support system 900 includes a user terminal 901 and a server 902. Each of the user terminal 901 and the server 902 may be configured by a computer including the same hardware components as those of the diagnostic support device 100 described with reference to Fig. 1.

[0067] The user terminal 901 is a device used by a user. The server 902 is a device that executes specific processing in response to a request from the user terminal 901. The server 902 may be located in an on-premise environment or in a cloud environment.

[0068] The user terminal 901 and the server 902 can communicate with each other through a network 903. The network 903 may be a local area network, the Internet, a cellular network, or any combination thereof.

[0069] 2 are distributed between a user terminal 901 and a server 902. For example, the user terminal 901 may include a data acquisition unit 201, a class determination unit 203, a class presentation unit 204, an action presentation unit 205, and a probability value acquisition unit 208. The server 902 may include the data acquisition unit 201, a score calculation unit 202, a diagnostic performance index calculation unit 206, and a boundary value determination unit 207.

[0070] The data acquisition unit 201 of the user terminal 901 may acquire medical data of the subject from the user of the user terminal 901. The data acquisition unit 201 of the server 902 may acquire the medical data acquired by the user terminal 901 from the user terminal 901. The above-described operations by the diagnosis support device 100 (for example, the methods in FIGS. 6 and 7) are executed by the user terminal 901 and the server 902 in cooperation with each other.

[0071] <Second embodiment> A second embodiment of the present invention relates to a diagnostic support device. A diagnostic support device is a device that supports diagnoses performed by medical professionals such as doctors, dentists, and veterinarians. The person to be diagnosed (hereinafter referred to as the test subject) may be a human being or another animal (for example, livestock or pets). A person diagnosed with an injury or illness may also be called a patient.

[0072] The purpose of the test is to determine whether the test subject has an illness or disease. For example, it may be determined whether the test subject has a specific illness or disease, or it may be determined whether the test subject has some illness or disease even if the name of the illness or disease cannot be identified. In the following description, the fact that the test subject has an illness or disease is referred to as "the test subject is positive," and the fact that the test subject does not have an illness or disease is referred to as "the test subject is negative."

[0073] The diagnosis support device assists in diagnosis using medical data related to the subject. Medical data is data that can serve as the basis for a diagnosis. For example, the medical data may include data collected from the subject using a testing device (e.g., electrocardiogram data, X-rays, blood test results, height, weight). The medical data may also include findings on the subject by a medical professional. The medical data may also include attributes of the subject (e.g., age, sex, lifestyle, medical history). The medical data may also include attributes of the subject's blood relatives (e.g., parents, brothers, sisters).

[0074] [Example of hardware configuration for diagnostic support device] An example of the hardware configuration of a diagnosis support device 1000 according to the second embodiment will be described with reference to FIG. 10. The diagnosis support device 1000 includes the components shown in FIG. 10. The diagnosis support device 1000 may include only one of each component shown in FIG. 10, or may include multiple components. The diagnosis support device 1000 may not include some of the components shown in FIG. 10, or may include components not shown in FIG. 10. For example, the diagnosis support device 1000 may include an inspection device for collecting medical data from a subject. In this case, the diagnosis support device 1000 may be called an inspection device having a diagnosis support function.

[0075] The processor 1001 is a device that controls the overall operation of the diagnostic support device 1000. The processor 1001 is configured, for example, by a CPU (Central Processing Unit). The memory 1002 is a device that stores programs and trained AI models required for the operation of the diagnostic support device 1000. The memory 1002 is configured, for example, by a RAM (Random Access Memory) or a ROM (Read Only Memory). The operation of the diagnostic support device 1000 may be performed, for example, by the processor 1001 executing a program stored in the memory 1002. Alternatively, part or all of the operation of the diagnostic support device 1000 may be performed by a dedicated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). Furthermore, the processor 1001 may include a GPU (Graphical Processing Unit). A device having the processor 1001 and the memory 1002, such as the diagnostic support device 1000, may also be called a computer.

[0076] The input device 1003 is a device for acquiring input from a user of the diagnostic support device 1000 (e.g., a medical professional; hereinafter, the user of the diagnostic support device 1000 will be simply referred to as the user). The input device 1003 is configured, for example, by inputting waveform data, images, etc. recorded from electrodes, sensors, cameras, etc., using a keyboard, touch panel, or mouse. The output device 1004 is a device for outputting to the user. The output device 1004 is configured, for example, by a display or a printer. In the example of FIG. 10 , the input device 1003 is described as constituting a part of the diagnostic support device 1000, but the input device 1003 may be a device separate from the diagnostic support device 1000. In this case, the diagnostic support device 1000 includes an input interface for connecting to the input device 1003. Similarly, the output device 1004 may be a part of the diagnostic support device 1000 or a device separate from the diagnostic support device 1000.

[0077] The communication device 1005 is a device that enables the diagnostic assistance device 1000 to communicate with other devices. The other devices may be computers connected to a network (e.g., the Internet or a local area network). For example, the communication device 1005 may be used to acquire medical data from the other devices.

[0078] The storage device 1006 is a device that stores data used for the operation of the diagnostic assistance device 1000. The storage device 1006 is configured by a storage medium such as a hard disk drive (HDD), a solid state drive (SSD), or a DVD (digital versatile disc).

[0079] [Example of processing flow in which the functions of the diagnostic support device are executed by a processor] An example of the flow of processing in which the functions of the diagnostic support device 1000 are executed by a processor will be described with reference to Fig. 11. The diagnostic support device 1000 may not include some of the functions shown in Fig. 11, or may include functions not shown in Fig. 11. Each function of the diagnostic support device 1000 is realized by the processor 1001 executing a program or a trained AI model loaded into a memory 1002. At least some of the functions of the diagnostic support device 1000 may be realized by a dedicated circuit such as an ASIC or FPGA.

[0080] The data acquisition unit 1101 acquires data to be used in the diagnosis support device 1000. For example, the data acquisition unit 1101 acquires medical data related to a subject. The data acquisition unit 1101 may acquire medical data input by a user to the input device 1003. The data acquisition unit 1101 may acquire medical data from a device external to the diagnosis support device 1000 (e.g., a file server or an examination device) using the communication device 1005. If the diagnosis support device 1000 includes an examination device, the data acquisition unit 1101 may acquire medical data from the examination device. The medical data acquired by the data acquisition unit 1101 may be stored in the memory 1002 or the storage device 1006 for subsequent processing.

[0081] The trained AI model unit 1102 is created by AI learning using training medical data. The trained AI model unit 1102 may have any structure. For example, the trained AI model unit 1102 may be configured as a neural network such as deep learning, or as a support vector machine such as machine learning.

[0082] The teacher medical data is medical data labeled as whether the provider of the teacher medical data is positive or negative. The risk score calculation unit 1103 calculates a risk score that the person in the medical data has an injury or illness using the trained AI model unit 1102. The AI ​​learning of the trained AI model unit 1102 may be performed by the diagnosis support device 1000 or by another device.

[0083] The risk score calculation unit 1103 calculates a risk score that indicates the possibility that the person involved in the medical data has an illness or injury (i.e., is positive) using the trained AI model unit 1102. The person involved in the medical data may be the test subject or the provider of the verification medical data.

[0084] As shown in Figure 11, the trained AI model unit 1102 calculates a risk score for the input medical data. In the following description, the risk score can take a value in the range of 0 to 1. Alternatively, the risk score may take a value in another range. The higher the risk score, the more likely the person involved in the medical data is positive, and the lower the risk score, the more likely the person involved in the medical data is negative.

[0085] In one example, the medical data is electrocardiogram data, and the risk score represents the likelihood that the person of the electrocardiogram data has paroxysmal atrial fibrillation (PAF). In this case, the teacher medical data includes electrocardiogram data of a patient with paroxysmal atrial fibrillation (i.e., electrocardiogram data labeled as being positive for paroxysmal atrial fibrillation) and electrocardiogram data of a donor who does not have paroxysmal atrial fibrillation (i.e., electrocardiogram data labeled as being negative for paroxysmal atrial fibrillation).

[0086] Diseases such as PAF, which manifest spontaneously or suddenly, are known to be difficult to detect because symptoms often do not appear at the time of testing. For example, depending on the timing of the electrocardiogram data acquisition of a PAF patient, the electrocardiogram data may show sinus rhythm, making PAF difficult to detect. This type of PAF is sometimes called "hidden atrial fibrillation." The trained AI model unit 1102 quantitatively determines the risk of a test subject having PAF, enabling support for diagnosis.

[0087] The classification class determination unit 1104 determines, from three or more classification classes, the classification class to which the risk score calculated by the trained AI model unit 1102 belongs. The three or more classification classes divide the range that the risk score can take (i.e., the range from 0 to 1). Below, as an example, a case will be described in which the classification class determination unit 1104 determines, from four classification classes, the classification class to which the risk score belongs. The same explanation applies when the classification class determination unit 1104 determines, from three or five or more classification classes, the classification class to which the risk score belongs.

[0088] The four classification classes will be described with reference to Figure 12. The range that the risk score can take (i.e., the range from 0 to 1) is divided into four ranges by three boundary values ​​BL, BM, and BH. The boundary value BL is greater than 0 and less than the boundary value BM. The boundary value BM is greater than the boundary value BL and less than the boundary value BH. The boundary value BH is greater than the boundary value BM and less than 1. The method for determining the boundary values ​​BL, BM, and BH will be described later.

[0089] The classification class determination unit 1104 determines that a risk score belongs to the classification class "low" when the risk score is included in a range equal to or greater than 0 and less than the boundary value BL. The classification class determination unit 1104 determines that a risk score belongs to the classification class "middle-low" when the risk score is included in a range equal to or greater than the boundary value BL and less than the boundary value BM. The classification class determination unit 1104 determines that a risk score belongs to the classification class "middle-high" when the risk score is included in a range equal to or greater than the boundary value BM and less than the boundary value BH. The classification class determination unit 1104 determines that a risk score belongs to the classification class "high" when the risk score is included in a range equal to or greater than the boundary value BH and less than 1. In the above example, when a risk score is equal to a boundary value, the risk score is determined to belong to a class whose lower limit is the boundary value. Alternatively, when a risk score is equal to a boundary value, the risk score may be determined to belong to a class whose upper limit is the boundary value. As shown in FIG. 12, the range of possible risk scores is divided into four classification classes by predetermined values ​​of a plurality of diagnostic performance indices (described later).

[0090] The method for determining the boundary value is based on indicators (specifically, sensitivity, specificity, positive likelihood ratio, negative likelihood ratio) that represent the diagnostic ability of the AI ​​model corresponding to the output risk score of the trained AI model unit 1102.

[0091] Sensitivity is an index representing the ability to determine a subject with an illness or injury as positive when the risk score output by the trained AI model unit 1102 is equal to or greater than the cutoff value of a certain test using the cutoff value of a certain test. Specificity is an index representing the ability to determine a subject with an illness or injury as negative when the risk score output by the trained AI model unit 1102 is equal to or less than the cutoff value of a certain test using the cutoff value of a certain test using the cutoff value of a certain test using the cutoff value of a certain test using the cutoff value of a certain test using the cutoff value of a certain test using the cutoff value of a certain test using the cutoff value of a certain test using the risk score ... risk score of a certain test using the cutoff value of the trained AI model unit 1102.

[0092] The likelihood ratio is the ratio of the odds before the test to the odds after the test, and is an index that shows how much the probability of having an illness changes before and after the test when the test is positive or negative. The positive likelihood ratio of a test indicates how many times more likely a positive person is to be positive than a negative person, and the negative likelihood ratio indicates how many times more likely a positive person is to be negative than a negative person. It can be calculated from the sensitivity and specificity using the following formula. Positive likelihood ratio (LR+) = sensitivity / (1-specificity), Negative likelihood ratio (LR-) = (1-sensitivity) / specificity.

[0093] From the calculated positive and negative likelihood ratios, the posterior probability of the test subject having the disease before the test can be calculated using a nomogram, referring to the prior probability 1106 of the test subject having the disease before the test. The prior probability is a value that a clinician estimates the probability that the test subject has the disease before the test is conducted, based on interviews and other clinical information. It can be estimated based on literature from the test subject's medical history and physical examination findings, or it can be comprehensively judged based on the clinician's experience. In addition, the season, region, and characteristics of the facility can also affect the prior probability judgment.

[0094] By referring to the prior probability value 1106 estimated by the clinician, the classification class determination unit 1104 of the AI ​​model and the AI ​​output display unit 1105 (described later) use a nomogram to quantitatively determine the posterior probability of the test subject after the AI ​​test based on the positive likelihood ratio or negative likelihood ratio of the test corresponding to the determined class. This helps doctors make accurate decisions to make a definitive diagnosis.

[0095] A method for determining the boundary values ​​of the four classification classes will be described using an example. For example, the boundary value BL is set to the maximum cutoff value (e.g., 0.1) at which the negative likelihood ratio is equal to or less than a first predetermined value A. When the negative likelihood ratio of a certain test is equal to or less than A, the possibility that the test subject has an illness can be more strongly denied. The boundary value BL may also be set to another value based on the negative likelihood ratio (e.g., a cutoff value among multiple cutoff values ​​at which the negative likelihood ratio is closest to or even lower than the first predetermined value).

[0096] The boundary value BH is set to the smallest cutoff value (e.g., 0.8) at which the positive likelihood ratio is equal to or greater than the second predetermined value B. When the positive likelihood ratio of a certain test is equal to or greater than B, it is more likely that the test subject has an illness. The boundary value BH may also be determined using another value based on the positive likelihood ratio (e.g., a cutoff value at which the positive likelihood ratio is closest to or even higher than the second predetermined value described above).

[0097] The boundary value BM is determined as an optimal cutoff value C (e.g., 0.5) that maximizes the sum of sensitivity and specificity. The boundary value BM may be determined as another value between the boundary values ​​BL and BH (e.g., the average value between the boundary values ​​BL and BH) or a third predetermined value of the positive and negative likelihood ratio.

[0098] When the classification class determination unit 1104 determines the classification class to which the risk score belongs from the four classification classes, any one of the three boundary values ​​BL, BM, and BH described above does not need to be used. For example, when the classification class "Low" and the classification class "Middle-Low" are combined, the boundary value BL is not used. When the classification class "Middle-Low" and the classification class "Middle-High" are combined, the boundary value BM is not used. When the classification class "Middle-High" and the classification class "High" are combined, the boundary value BH is not used.

[0099] As described above, the boundary values ​​of the classification classes are determined by a plurality of predetermined values ​​based on the diagnostic performance index of the trained AI model unit 1102. These boundary values ​​divide the range of possible risk scores into three or more classification classes. Therefore, in general, the range of possible risk scores is divided into three or more classification classes by the predetermined values ​​of the plurality of diagnostic performance indexes.

[0100] The boundary value determined based on the diagnostic performance index of the trained AI model unit 1102 may differ depending on the performance of the trained AI model unit 1102. For example, even if the same risk score is output for an AI model with relatively high performance (hereinafter referred to as a high-performance AI model) and an AI model with relatively low diagnostic performance (hereinafter referred to as a low-performance model), the boundary values ​​of the multiple classification classes determined will differ depending on the performance of the models, and the probability change (test effect) before and after the test corresponding to the determined class will also differ. In this way, in this embodiment, by determining the boundary value based on the diagnostic performance index of the trained AI model unit 1102, the positive and negative likelihood ratios of the test corresponding to the determined class can be presented to users, including doctors, by the AI ​​output display unit 1105 (described below).

[0101] FIG. 13 shows an example of four classification classes output by the AI ​​output display unit 1105. An example of a screen 1300 used to present the classification classes and recommended actions to the user will be described. The screen 1300 includes a bar-shaped region 1301 representing the four classification classes. The region 1301 is divided into a portion 1301L corresponding to the classification class "low," a portion 1301ML corresponding to the classification class "middle-low," a portion 1301MH corresponding to the classification class "middle-high," and a portion 1301H corresponding to the classification class "high." These four portions are arranged horizontally in the order shown in FIG. 13. The result of the classification class determination unit 1104 is displayed by displaying the portion 1301H with a weighted color or pattern.

[0102] FIG. 14 shows another example of four classification classes output by the AI ​​output display unit 1105. A screen 1400 includes an indicator that indicates the risk score output by the risk score calculation unit 1103 of FIG. 11. In the example of FIG. 14, the indicator 1402 has a triangular shape, but it may have another shape. The portion 1401MH to which the indicator 1402 belongs indicates the class determined by the classification class determination unit 1104 of FIG. 11. This indicates to the user that the risk score calculated by the trained AI model has been determined to belong to the classification class "middle-high."

[0103] Indicator 1402 may present to the user the relative position of the risk score calculated for the test subject with respect to the boundary values ​​of the four classification classes (the above-mentioned boundary values ​​BL, BM, and BH). Specifically, the left end of portion 1401L represents the position where the risk score is 0. The boundary line between portions 1401L and 1401ML represents the position where the risk score is boundary value BL. The boundary line between portions 1401ML and 1401MH represents the position where the risk score is boundary value BM. The boundary line between portions 1401MH and 1401H represents the position where the risk score is boundary value BH. The right end of portion 1401H represents the position where the risk score is 1.

[0104] As shown in FIG. 14, assume that the risk score calculated by the AI ​​model is determined to belong to the classification class "middle-high," and the risk score divides the lower limit (i.e., boundary value BM) and upper limit (i.e., boundary value BH) of the classification class "middle-high" internally at a:b. In this case, indicator 1402 is positioned to point to the position where the left and right ends of portion 1401MH are internally divided at a:b. By positioning indicator 1402 in this manner, the user can recognize how close the risk score is to other classification classes and the corresponding positive and negative likelihood ratios. This allows the user to quantitatively grasp the change in probability before and after testing with greater accuracy, making it easier to make a definitive diagnosis.

[0105] The screen 1400 may include an indicator 503 that indicates the relative position of a predetermined positive likelihood ratio (LR+) (1, 2, 3.4, 5, and 10 in the example of FIG. 14) of the model in which the risk score output from the trained AI model unit 1102, ranging from 0 to 1, is calculated as each cutoff value. In the example of FIG. 14, the indicator 503 has a bar shape. The indicator 503 is added to the top of the area 1401. The indicator 503 is placed at a position that indicates each cutoff value corresponding to the predetermined positive likelihood ratio. As described above for the indicator 1402, the position indicated by the cutoff value is the position that divides both ends of the classification class that includes this cutoff value internally.

[0106] The screen 1400 may include an indicator 504 that indicates the relative position of a predetermined negative likelihood ratio (LR-) (0, 0.1, 0.2, 0.36, 0.5, and 1 in the example of FIG. 14 ) of the model, in which the risk score output from the trained AI model unit 1102, ranging from 0 to 1, is calculated as each cutoff value. In the example of FIG. 14 , the indicator 504 has a bar shape. The indicator 504 is added to the bottom of the area 1401. The indicator 504 is placed at a position that indicates each cutoff value corresponding to the predetermined negative likelihood ratio. As described above for the indicator 1402, the position indicated by the cutoff value is the position that divides both ends of the classification class that includes this cutoff value internally.

[0107] On screen 1400, of the four portions included in area 1401, only the portion corresponding to the classification class to which the risk score has been determined to belong may be displayed, and the other three portions may not be displayed. For example, in the example of Fig. 14, portion 1401MH and indicators 1402 to 1404 pointing to it may be displayed, while portions 1401L, 1401ML, and 1401H and indicators 1403 to 1404 pointing to them may not be displayed. Fig. 15 shows an example of such an output display.

[0108] Screen 1500 indicates the risk score calculated by risk score calculation unit 1103 in Fig. 11 with a triangular indicator 1502. Bar-shaped area 1501MH displays the discrimination class to which the risk score indicated by indicator 1502 belongs. The lower and upper limits of the risk score of class 1501MH are represented by boundary values ​​BM and BH. Area 1501MH indicating the discriminated class is divided into five equal parts by vertical bars, and each equal part may be further divided into two equal parts by indicator 1503.

[0109] The area 1501MH indicating the determined class may include, above the area 1501MH, an indicator 1504 indicating the relative positions of three predetermined values ​​(3.4, 4.8, and 6.2 in the example of FIG. 15) including the lower limit, upper limit, and predetermined values ​​between the upper and lower limits of the positive likelihood ratio (LR+) of the model determined from the cutoff values ​​corresponding to the lower limit BM and upper limit BH of the risk score of this class. In this way, in the example of FIG. 15, a predetermined number of positive likelihood ratios and negative likelihood ratios distributed over the entire range of the classification class to which the risk score has been determined to belong are displayed.

[0110] The area 1501MH indicating the class to be determined may include, at the bottom, an indicator 1505 indicating the relative positions of three predetermined values ​​(0.36, 0.48, and 0.6 in the example of Figure 15) including the lower limit, upper limit, and predetermined values ​​between them of the negative likelihood ratio (LR-) of the model obtained from the cutoff values ​​corresponding to the lower limit BM and upper limit BH of the risk score of this class.

[0111] As described above, Figure 15, which is an example of the display of the AI ​​output display unit 1105, differs from the output display examples of Figures 13 and 14 in that it provides the user with a more accurate quantitative indication of the change in probability before and after the test based on indicator 1502 that indicates the relative position of the risk score calculated from the AI ​​model, and the three positive and negative likelihood ratios presented within the determined class area, thereby supporting a definitive diagnosis.

[0112] The recommended action presentation unit 1107 in Fig. 11 presents recommended actions to the user based on the determined class displayed by the AI ​​output display unit 1105 and the prior probability value 1106 of the test target obtained from the user. For example, the recommended action presentation unit 1107 may determine the action by referring to the table 1600 in Fig. 16. The table 1600 is generated by the manufacturer of the diagnostic support device 1000 and stored in the memory 1002 or the storage device 1006 of the diagnostic support device 1000. Along with the output display of the determined classification class, the presentation of recommended actions can also be displayed on the diagnostic screen.

[0113] The rows of table 1600 represent the classification classes determined by the classification class determination unit 1104. The columns of table 1600 represent whether the pre-test prior probability value 1106 in FIG. 11 is higher or lower than a predetermined threshold (e.g., 50%), and may be composed of two or more columns. The "Detailed Examination" in table 1600 recommends that the subject of the test be considered for detailed examination. The "Regular Examination" in table 1600 recommends that the subject of the test be considered for regular examination. The "Simple Examination" in table 1600 recommends that the subject of the test be considered for simple examination, which is less burdensome. The "No Examination" in table 1600 recommends that the subject of the test not be subjected to additional examination. Other descriptions may be used for the recommended actions.

[0114] Based on the positive and negative likelihood ratios of the test corresponding to the judgment class displayed by the AI ​​output display unit 1105 in Figure 11 and the prior probability value 1106 of the test subject obtained from the user, the posterior probability of the test subject having an injury or disease after the test can be quantitatively determined by referring to a nomogram. Once the posterior probability is determined, recommended actions such as those shown in table 1600 in Figure 16 can be presented to medical professionals, who can accurately support them in making a definitive diagnosis based on the quantitative posterior probability.

[0115] The rows of table 1700 in FIG. 17 represent the posterior probability of the estimated test subject having an injury or illness after the test. As an example, the posterior probability ranging from 0 to 100% is divided into four classes using three predetermined thresholds (e.g., 20%, 50%, and 80%), and the recommended action for each class is shown in the columns of table 1700. If the posterior probability value is lower than the first predetermined threshold, the recommended action in table 1700 is "No additional test." If the posterior probability value is higher than the first predetermined threshold and lower than the second predetermined threshold, the recommended action in table 1700 is "Simple test." If the posterior probability value is higher than the second predetermined threshold and lower than the third predetermined threshold, the recommended action in table 1700 is "Regular test." If the posterior probability value is equal to or greater than the third predetermined threshold, the recommended action in table 1700 is "Detailed test." If the posterior probability is equal to the predetermined threshold, the posterior probability is determined to belong to a class whose lower limit is this threshold. Alternatively, if the posterior probability is equal to a predetermined threshold, the posterior probability may be determined to belong to a class whose upper limit is the threshold.

[0116] A proposed action plan (Figure 16 or Figure 17) is displayed on the screen based on the test results and the AI ​​output display, the determined classification class, and the user's estimated prior probability of the test subject. This information is then saved to a recording device or printed by the test result report output unit 1108. This ensures that the test results and recommended actions are properly documented and accessible to medical professionals for future reference.

[0117] [Example of diagnostic support system configuration] An example configuration of a diagnostic support system 1800 according to the second embodiment will be described with reference to Fig. 18. The diagnostic support system 1800 includes a user terminal 1801 and a server 1802. Each of the user terminal 1801 and the server 1802 may be configured by a computer including the same hardware components as those of the diagnostic support device 1000 described with reference to Fig. 10.

[0118] The user terminal 1801 is a device used by a user. The server 1802 is a device that executes specific processing in response to a request from the user terminal 1801. The server 1802 may be located in an on-premise environment or in a cloud environment.

[0119] The user terminal 1801 and the server 1802 can communicate with each other through a network 1803. The network 1803 may be a local area network, the Internet, a cellular network, or any combination thereof.

[0120] 11 are distributed between a user terminal 1801 and a server 1802. For example, the user terminal 1801 may include a data acquisition unit 1101, a trained AI model unit 1102, a risk score calculation unit 1103, a classification class determination unit 1104, an AI output display unit 1105, a pre-test prior probability value 1106 of the test subject, a posterior probability calculation and recommended action presentation unit 1107, and a test result report output unit 1108. The server 1802 may include the data acquisition unit 1101, the trained AI model unit 1102, the risk score calculation unit 1103, the classification class determination unit 1104, and the AI ​​output display unit 1105.

[0121] The data acquisition unit 1101 of the user terminal 1801 may acquire medical data of the subject of examination from the user of the user terminal 1801. The data acquisition unit 1101 of the server 1802 may acquire the medical data acquired by the user terminal 1801 from the user terminal 1801. The above-described operation of the diagnosis support device 1000 (the flow of the execution process in FIG. 11 ) is executed by the user terminal 1801 and the server 1802 in cooperation with each other.

[0122] <Summary of the embodiment> (Item 1) a data acquisition unit that acquires medical data related to the subject; a score calculation unit that calculates a risk score representing the possibility of having an injury or disease for the medical data using an AI model created by learning using teacher medical data; A diagnostic support device comprising: a determination unit that determines the classification class to which the risk score belongs from three or more classification classes using the diagnostic performance index of the AI ​​model. This item will enable medical professionals to more easily determine the probability that a test subject will have a post-test injury or disease. For example, the posterior probability will be quantitatively calculated based on the test subject's prior probability and the output of the AI ​​model, allowing medical professionals to more accurately determine the posterior probability of a post-test injury or disease. This quantitative information will be important for medical professionals to properly assess the patient's condition and recommend appropriate detailed examinations, treatments, and follow-up. (Item 2) Item 1. The diagnostic support device according to item 1, wherein two or more boundary values ​​of the three or more classification classes are determined based on different diagnostic performance indexes of the AI ​​model. This item allows for proper determination of boundary values ​​for three or more classification classes. (Item 3) 3. The diagnostic support device according to claim 1, wherein the first boundary values ​​of the three or more classification classes are determined based on a negative likelihood ratio of the AI ​​model. This item makes it possible to accurately determine the classification class to which test subjects who are unlikely to have an injury or illness belong. (Item 4) A diagnostic support device according to any one of items 1 to 3, wherein the second boundary values ​​of the three or more classification classes are determined based on a positive likelihood ratio of the AI ​​model. This item makes it possible to accurately determine the classification class to which test subjects who are likely to have an injury or illness belong. (Item 5) The diagnostic support device according to any one of items 1 to 4, wherein the third boundary value of the three or more classification classes is determined based on a value that is based on the sensitivity and specificity of the AI ​​model and is different from a likelihood ratio. This item makes it possible to accurately determine the classification class to which test subjects who are moderately likely to have an injury or illness belong. (Item 6) a first boundary value of the three or more classification classes is determined based on a negative likelihood ratio of the AI ​​model; a second boundary value of the three or more classification classes is determined based on a positive likelihood ratio of the AI ​​model; a third boundary value of the three or more classification classes is determined based on a value that is based on a sensitivity and a specificity of the AI ​​model and is different from a likelihood ratio; 6. The diagnostic support device according to any one of items 1 to 5, wherein the third boundary value is greater than the first boundary value and less than the second boundary value. This item makes it possible to accurately determine the classification class to which the test subject belongs based on the possibility of having an injury or illness. (Item 7) 7. The diagnostic support device according to any one of items 1 to 6, further comprising a display unit that displays the classification class to which the risk score is determined to belong. This item allows medical professionals to understand the classification class. (Item 8) 8. The diagnostic support device according to item 7, wherein the display unit further displays a predetermined plurality of positive likelihood ratios and negative likelihood ratios distributed over the entire range of the classification class to which the risk score is determined to belong. This item allows medical professionals to understand the relationship between classification classes and likelihood ratios. (Item 9) 9. The diagnostic support device according to item 7 or 8, wherein the display unit does not display any classification class to which the risk score is not determined to belong, among the three or more classification classes. This item makes it easier for medical professionals to understand information about the classification class to which the risk score is determined to belong. (Item 10) The display unit a first indicator representing the relative position of the risk score with respect to the upper and lower limits of the classification class to which the risk score is determined to belong; a second indicator representing the relative position of a predetermined positive likelihood ratio to the upper and lower limits of the classification class to which the risk score is determined to belong; a third indicator that indicates the relative position of a predetermined negative likelihood ratio to the upper and lower limits of the classification class to which the risk score is determined to belong; 10. The diagnostic aid device according to item 9, further displaying at least one of the following: This item makes it easier for medical professionals to understand the relationship between classification classes and likelihood ratios. (Item 11) 11. The diagnostic support device according to any one of items 1 to 10, further comprising an action presentation unit that presents recommended actions determined based on the classification class to which the risk score is determined to belong. According to this item, it is possible to efficiently classify the test subject's condition using an index with high diagnostic ability, and to present appropriate actions to medical professionals. (Item 12) Item 12. The diagnostic support device according to item 11, wherein the action presenter determines a posterior probability value based on a prior probability value and a positive likelihood ratio or a negative likelihood ratio corresponding to the risk score, and determines the recommended action based on the posterior probability value. According to this item, medical professionals can calculate the post-test probability of a subject having an injury or illness by using the likelihood ratio corresponding to the pre-test probability value and the calculated risk score, making it easier for them to select the corresponding recommended action. (Item 13) the medical data is electrocardiogram data, 13. The diagnostic support device according to any one of items 1 to 12, wherein the teacher medical data includes electrocardiogram data of a patient with paroxysmal atrial fibrillation. This item makes it easier for medical professionals to quantitatively determine the likelihood that a test subject has paroxysmal atrial fibrillation. (Item 14) On the computer, obtaining medical data relating to the test subject; Calculating a risk score representing the possibility of having an injury or disease for the medical data using an AI model created by learning using training medical data; A program for executing the steps of: determining the classification class to which the risk score belongs from three or more classification classes using the diagnostic performance index of the AI ​​model. This item allows medical personnel to more easily determine the probability that a test subject will have a post-test illness or injury. (Item 15) A diagnostic support system including a user terminal and a server, The server a data acquisition unit that acquires medical data related to the subject; a score calculation unit that calculates a risk score representing the possibility of having an injury or disease for the medical data using an AI model created by learning using teacher medical data, The user terminal A determination unit that determines a classification class to which the risk score belongs from three or more classification classes using the diagnostic performance index of the AI ​​model, Diagnostic support system. This item allows medical personnel to more easily determine the probability that a test subject will have a post-test illness or injury.

[0123] The invention is not limited to the above-described embodiment, and various modifications and variations are possible within the scope of the gist of the invention. [Explanation of symbols]

[0124] 100 diagnostic support devices, 300 AI models, 900 diagnostic support systems

Claims

1. a data acquisition unit that acquires medical data related to the subject; a score calculation unit that calculates a risk score representing the possibility of having an injury or illness for the medical data using an AI model created by learning using teacher medical data; A diagnostic support device comprising: a determination unit that determines the classification class to which the risk score belongs from three or more classification classes using the diagnostic performance index of the AI ​​model.

2. The diagnostic support device according to claim 1 , wherein two or more boundary values ​​of the three or more classification classes are determined based on different diagnostic performance indexes of the AI ​​model.

3. The diagnostic support device according to claim 1 , wherein the first boundary values ​​of the three or more classification classes are determined based on a negative likelihood ratio of the AI ​​model.

4. The diagnostic support device according to claim 1 , wherein the second boundary values ​​of the three or more classification classes are determined based on a positive likelihood ratio of the AI ​​model.

5. The diagnostic support device according to claim 1 , wherein the third boundary value of the three or more classification classes is determined based on a value that is based on the sensitivity and specificity of the AI ​​model and is different from a likelihood ratio.

6. a first boundary value of the three or more classification classes is determined based on a negative likelihood ratio of the AI ​​model; a second boundary value of the three or more classification classes is determined based on a positive likelihood ratio of the AI ​​model; a third boundary value of the three or more classification classes is determined based on a value that is based on a sensitivity and a specificity of the AI ​​model and is different from a likelihood ratio; The diagnostic support device according to claim 1 , wherein the third boundary value is greater than the first boundary value and less than the second boundary value.

7. The diagnostic support device according to claim 1 , further comprising a display unit that displays a classification class to which the risk score is determined to belong.

8. The diagnostic support device according to claim 7 , wherein the display unit further displays a predetermined number of positive likelihood ratios and negative likelihood ratios distributed over the entire range of the classification class to which the risk score is determined to belong.

9. The diagnostic support device according to claim 7 , wherein the display unit does not display any classification class to which the risk score is not determined to belong, among the three or more classification classes.

10. The display unit a first indicator representing the relative position of the risk score relative to the upper and lower limits of the classification class to which the risk score is determined to belong; a second indicator representing the relative position of a predetermined positive likelihood ratio to the upper and lower limits of the classification class to which the risk score is determined to belong; a third indicator representing the relative position of a predetermined negative likelihood ratio to the upper and lower limits of the classification class to which the risk score is determined to belong; 10. The diagnostic support device according to claim 9, further displaying at least one of the following:

11. The diagnostic assistance device according to claim 1 , further comprising an action presenting unit that presents a recommended action determined based on a classification class to which the risk score is determined to belong.

12. 12. The diagnostic assistance device according to claim 11, wherein the action presenter determines a posterior probability value based on a prior probability value and a positive likelihood ratio or a negative likelihood ratio corresponding to the risk score, and determines the recommended action based on the posterior probability value.

13. the medical data is electrocardiogram data, The diagnosis support device according to claim 1 , wherein the training medical data includes electrocardiogram data of a patient with paroxysmal atrial fibrillation.

14. On the computer, obtaining medical data relating to the test subject; Calculating a risk score representing the possibility of having an injury or illness for the medical data using an AI model created by learning using teacher medical data; A program for executing the following: determining the classification class to which the risk score belongs from three or more classification classes using the diagnostic performance index of the AI ​​model.

15. A diagnostic support system including a user terminal and a server, The server a data acquisition unit that acquires medical data related to the subject; a score calculation unit that calculates a risk score representing the possibility of having an injury or illness for the medical data using an AI model created by learning using teacher medical data, The user terminal A determination unit that determines a classification class to which the risk score belongs from three or more classification classes using the diagnostic performance index of the AI ​​model, Diagnostic support system.

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