JP6625155B2 - Information processing apparatus, method of operating information processing apparatus, and program - Google Patents

Description

  The present invention relates to a technique for providing information that supports medical diagnosis.

  In the medical field, a doctor displays a medical image of a patient on a monitor, interprets the medical image displayed on the monitor, and observes the state of the lesion and changes over time. In image diagnosis using a medical image, a doctor finds an abnormal shadow or the like from a medical image to be diagnosed and diagnoses what the abnormal shadow or the like is. The interpretation report created by the doctor at the time of the image diagnosis describes the result of determining what the abnormal shadow or the like found is (estimated disease name) and the interpretation findings that are the basis for the determination. At this time, the interpretation findings and estimated disease names written by the doctor were not always correct in the following cases. For example, when a doctor who is very busy and cannot spend much time on one case or who has little experience in image interpretation observes abnormal shadows that are difficult to find on images and abnormal features that are difficult to capture features, abnormal shadows There was a possibility of overlooking, and misjudgment of interpretation findings and estimated disease names. In the following description, the estimated disease name is considered to be one of the "findings (interpretation findings)" in a broad sense.

  Therefore, for the purpose of providing information for improving the accuracy of image diagnosis by a doctor, medical image processing for automatically detecting a diseased part or the like by digitizing and analyzing a medical image and performing computer-aided diagnosis Equipment is being developed. Hereinafter, the computer-aided diagnosis is referred to as CAD (Computer-Aided Diagnosis). In such CAD, an abnormal shadow candidate is automatically detected as a disease site. In this abnormal shadow detection process, an abnormal swelling shadow or a high density microcalcification shadow due to cancer or the like is detected by computer processing of medical image data. By presenting the detection result, the burden on the doctor for image interpretation can be reduced, and the accuracy of the image interpretation result can be improved.

  For example, in the device described in Patent Literature 1 below, after detecting an abnormal shadow by CAD, (part of) a finding corresponding to a finding used by a doctor for diagnosis is automatically generated from the detected abnormal shadow. Then, the automatically generated finding is automatically compared with the interpretation finding written by the doctor as the first impression, and when the comparison result is different, warning information is presented. Here, the findings automatically generated using CAD will be referred to as “CAD findings”. According to this technique, when the interpretation finding and the CAD finding written by the doctor as the first impression are different, the doctor can be encouraged to review the interpretation finding, and a highly accurate diagnosis can be realized.

Japanese Patent No. 3085724

  However, in Patent Literature 1, when the CAD findings are always correct, a warning is always presented if there is an error in the interpretation findings written by the doctor, but actually, the CAD findings are not necessarily correct. Therefore, actually, even if the CAD finding is wrong and the doctor's interpretation finding is correct, a warning is presented, and just because the warning is presented, the doctor judges that the CAD finding at that time is correct. I couldn't do it. In other words, the presented CAD findings were not always useful for a doctor's diagnosis.

  The present invention has been made in view of such a problem, and an object of the present invention is to enable a physician to present to what extent CAD findings are useful for diagnosis as information that can be appropriately determined.

The information processing apparatus of the present invention, by analyzing test data of the subject, generating means for generating information indicating the state of the subject regarding each of a plurality of items for diagnosis of the subject as a generation finding, An information acquisition unit configured to acquire information regarding a difference regarding the item in which the change has been performed in response to a change made to the generation finding; and Display control means for displaying, on a display unit, an item in which the information on the difference is obtained and an item in which the information on the difference is not obtained in an identifiable state.
In another aspect of the information processing apparatus of the present invention, information indicating a state of the subject regarding each of a plurality of items for diagnosis of the subject is analyzed by analyzing test data of the subject as a generation finding. Generating means for generating, an information obtaining means for obtaining information indicating that the change has been made for the item in which a change has been made to the generation finding, and a plurality of at least some of the plurality of items. Display control means for displaying, on a display unit, items in which information indicating that the change has been obtained and items in which information indicating that the change has not been obtained among the items have been obtained. And
According to another aspect of the information processing apparatus of the present invention, by analyzing test data of a subject, any one of a plurality of states that the subject can take with respect to an item for diagnosing the subject is provided. Generating means for generating information indicating a state as a generating finding; information obtaining means for obtaining information indicating that the change has been made for the item in which the change has been made to the generating finding; Display control means for displaying the generation finding on the display unit in a state where the information indicating that the change has been obtained and the case where the information indicating that the change has been performed has not been obtained can be identified, Having.
Another aspect of the information processing apparatus of the present invention is information indicating a state of the subject regarding each of a plurality of items for diagnosis of the subject obtained by analyzing test data of the subject. Finding obtaining means for obtaining a generated finding; and identifying, among at least some of the plurality of items, at least a portion of the plurality of items that have been changed with respect to the generated finding and those at which the change has not been made. Display control means for displaying the state on the display unit.
According to another aspect of the information processing apparatus of the present invention, any one of a plurality of states that the subject can take with respect to an item for diagnosing the subject obtained by analyzing test data of the subject. Finding acquisition means for acquiring a generation finding that is information indicating a state of the state, and displaying the generation finding in a state where the generation finding can be distinguished from a case where a change has been made to the generation finding. Display control means for causing the display unit to display.
The present invention also includes a method for operating the information processing apparatus described above, and a program for causing a computer to execute each step of the method for operating the information processing apparatus .

  ADVANTAGE OF THE INVENTION According to this invention, it can be shown as information which a doctor can judge appropriately how much an automatically generated finding (CAD finding) becomes a reference of diagnosis. This makes it possible to improve the accuracy of a doctor's diagnosis.

FIG. 2 is a block diagram illustrating an example of a functional configuration of the medical diagnosis support device according to the first embodiment. FIG. 2 is a block diagram illustrating an example of a hardware configuration of the medical diagnosis support apparatus according to the first embodiment. 5 is a flowchart illustrating an example of a processing procedure of a medical diagnosis support method in the medical diagnosis support device according to the first embodiment. FIG. 4 is a schematic diagram illustrating the first embodiment and illustrating a relationship between CAD findings and interpretation findings. FIG. 4 shows the first embodiment, and is a schematic diagram illustrating an example of a finding input screen in step S305 of FIG. 3. FIG. 4 is a schematic diagram illustrating the first embodiment and showing an example of a finding input screen after a doctor inputs a diagnostic finding in step S309 of FIG. 3. 4 is a flowchart illustrating an example of a detailed processing procedure in step S306 in FIG. 3. FIG. 6 shows the first embodiment and is a diagram summarizing the reliability information of CAD findings for each finding item. FIG. 11 is a characteristic diagram illustrating the second embodiment, in which the feature values S are plotted in time series. FIG. 4 is a schematic diagram showing the first embodiment and showing an example of a finding input screen in step S308 of FIG. 3.

  Hereinafter, an embodiment (embodiment) for carrying out the present invention will be described with reference to the drawings.

[First Embodiment]
In the medical diagnosis support apparatus according to the first embodiment, first, a medical image (first inspection data) including an abnormal shadow of a subject is image-analyzed to automatically generate a CAD finding (first automatically generated finding). . The means of the medical diagnosis support apparatus that generates the first automatically generated finding constitutes “first generating means”. Further, in the medical diagnosis support apparatus according to the first embodiment, a doctor's interpretation finding (first interpretation finding) for the first examination data is acquired. The means of the medical diagnosis support apparatus that acquires this first interpretation finding constitutes “first acquisition means”.

  Subsequently, in the medical diagnosis support apparatus according to the first embodiment, the CAD finding (first automatically generated finding) is presented as an initial value of the interpretation finding (first interpretation finding). The means of the medical diagnosis support apparatus that presents the CAD findings (first automatically generated findings) constitutes “first presentation means”. Then, in the medical diagnosis support apparatus according to the first embodiment, the doctor who referred to the CAD finding (first automatic generation finding) uses the CAD finding (first automatic generation finding) as the interpretation finding (first interpretation finding). Is acquired and recorded as information on the difference between the first automatically generated finding and the first interpretation finding. The means of the medical diagnosis support apparatus that acquires the information on the difference constitutes “difference information acquisition means”.

  Further, in the medical diagnosis support apparatus according to the first embodiment, when a new medical image (second inspection data) of the same abnormal shadow of the same subject is to be diagnosed, the second inspection data Is image-analyzed to automatically generate a CAD finding (second automatic generating finding). The means of the medical diagnosis support apparatus that generates this second automatically generated finding constitutes “second generating means”. In the medical diagnosis support apparatus according to the first embodiment, a doctor's interpretation finding (second interpretation finding) for the second examination data is acquired. The means of the medical diagnosis support apparatus that acquires the second interpretation finding constitutes “second acquisition means”.

  In the medical diagnosis support apparatus according to the first embodiment, when a new medical image (second test data) of the same abnormal shadow of the same subject is to be diagnosed, the past first test is performed. The reliability of the CAD finding (first automatically generated finding) is evaluated based on information on the difference between the CAD finding (first automatically generated finding) and the interpretation finding (first interpretation finding) in the data. Then, in the medical diagnosis support apparatus according to the first embodiment, information on the reliability of the CAD findings (first automatically generated findings) in the evaluated first test data is stored in the CAD findings (second 2 automatically generated findings). The means of the medical diagnosis support apparatus that makes this presentation constitutes “second presentation means”. Thereby, the medical diagnosis support apparatus according to the first embodiment allows the doctor to grasp the reliability of the currently presented CAD findings (second automatically generated findings), which is useful for diagnosis. Provide information.

FIG. 1 is a block diagram illustrating an example of a functional configuration of the medical diagnosis support apparatus 10 according to the first embodiment.
The medical diagnosis support apparatus 10 shown in FIG. 1 includes a medical information acquisition unit 100, an abnormal shadow detection unit 101, a CAD finding generation unit 102, a finding management unit 103, a finding presentation unit 104, an interpretation finding acquisition unit 105, and a reliability evaluation. Each function of the unit 106 is provided.

  The medical information acquisition unit 100 acquires medical information (such as information on medical images and electronic medical records) regarding a case to be diagnosed from a server (not shown). Alternatively, the medical information acquisition unit 100 may connect an external storage device, for example, an FDD, an HDD, a CD drive, a DVD drive, an MO drive, a ZIP drive, or the like, and acquire medical information from these drives. Then, the medical information acquisition unit 100 outputs the acquired medical information to the abnormal shadow detection unit 101.

  The abnormal shadow detection unit 101 detects an abnormal shadow by performing image analysis on a medical image to be diagnosed acquired from the medical information acquisition unit 100, and generates a CAD finding based on a detection result (for example, information on a region of interest surrounding the abnormal shadow). Output to the unit 102.

  The CAD finding generation unit 102 generates a CAD finding from the detection result of the abnormal shadow acquired from the abnormal shadow detecting unit 101, and outputs the CAD finding to the finding managing unit 103.

  The finding management unit 103 includes information on CAD findings acquired from the CAD finding generation unit 102 and information on interpretation findings acquired from the interpretation finding acquisition unit 105 described later (specifically, CAD finding change information (hereinafter, “finding change”). Then, the finding management unit 103 stores the obtained information on the magnetic disk as finding information forming a part of the interpretation report on the abnormal shadow. Outputs such information to the finding presentation unit 104 and the reliability evaluation unit 106.

  The finding presentation unit 104 displays and presents the CAD findings acquired from the finding management unit 103. In addition, when the finding presentation unit 104 acquires the CAD finding reliability information for each finding item from the reliability evaluating unit 106, the information is displayed in association with the CAD finding and presented.

  The interpretation finding acquisition unit 105 acquires information on interpretation findings (specifically, in this embodiment, CAD finding change information (finding change information)) written by the doctor who referred to the information displayed by the finding presentation unit 104. Then, it outputs it to the finding management unit 103.

  If there is an interpretation report that has previously read an image of the same abnormal shadow as the abnormal shadow to be diagnosed, the reliability evaluation unit 106 determines whether or not the image of the abnormal shadow stored in the past radiology report is present. Obtain finding information from the finding managing unit 103. Then, the reliability evaluation unit 106 evaluates the reliability of the CAD findings for each finding item based on the acquired abnormal shadow finding information, and obtains reliability information (details will be described later). Then, the reliability evaluation unit 106 outputs the evaluated reliability information to the finding presentation unit 104.

  Note that at least some of the functional components of the medical diagnosis support apparatus 10 illustrated in FIG. 1 may be realized as independent apparatuses. Further, each functional component of the medical diagnosis support apparatus 10 may be realized as software that realizes each function. In the present embodiment, it is assumed that each functional component of the medical diagnosis support apparatus 10 shown in FIG. 1 is realized by software.

  FIG. 2 is a block diagram illustrating an example of a hardware configuration of the medical diagnosis support apparatus 10 according to the first embodiment. That is, FIG. 2 is a diagram illustrating a basic configuration (hardware configuration) of a computer for realizing each function of each functional configuration unit illustrated in FIG. 1 by software.

  The medical diagnosis support apparatus 10 illustrated in FIG. 2 has a hardware configuration of a CPU 200, a main memory 201, a magnetic disk 202, a display memory 203, a monitor 204, a mouse 205, a keyboard 206, and a common bus 207.

  The CPU 200 mainly controls the operation of each component, and totally controls the operation of the medical diagnosis support apparatus 10.

  The main memory 201 stores a control program executed by the CPU 200 and provides a work area when the CPU 200 executes the program.

  The magnetic disk 202 stores an operating system (OS), a device drive of a peripheral device, various application software including a program for performing processing described later, and the like.

  The display memory 203 temporarily stores display data generated by the finding presenting unit 104 shown in FIG.

  The monitor 204 is, for example, a CRT monitor or a liquid crystal monitor, and displays an image, text, or the like based on display data from the display memory 203.

  The mouse 205 and the keyboard 206 are used by the user to perform pointing input and input of characters and the like.

  The common bus 207 connects the components (200 to 206) so as to be able to communicate with each other.

  Here, for example, the medical information acquisition unit 100, the CAD finding generation unit 102, and the reliability evaluation unit 106 illustrated in FIG. 1 are stored in the CPU 200 and the main memory 201 and / or the magnetic disk 202 illustrated in FIG. It consists of programs that are. Further, for example, the abnormal shadow detection unit 101 and the finding management unit 103 illustrated in FIG. 1 are configured by the CPU 200, the main memory 201, and / or the programs stored in the magnetic disk 202 and the magnetic disk 202 illustrated in FIG. Have been. Further, for example, the finding presentation unit 104 illustrated in FIG. 1 includes the CPU 200, the program stored in the main memory 201 and / or the magnetic disk 202, the display memory, and the monitor illustrated in FIG. Further, for example, the interpretation finding acquisition unit 105 illustrated in FIG. 1 includes a CPU 200, a program stored in the main memory 201 and / or the magnetic disk 202, a mouse 205, and a keyboard 206 illustrated in FIG. 2.

  Next, the overall processing performed by the medical diagnosis support apparatus 10 will be described using the flowchart of FIG.

  FIG. 3 is a flowchart illustrating an example of a processing procedure of a medical diagnosis support method in the medical diagnosis support apparatus 10 according to the first embodiment. In the present embodiment, the flowchart of FIG. 3 is realized by the CPU 200 executing a program for realizing the function of each unit stored in the main memory 201.

  First, in step S301 in FIG. 3, the medical information acquisition unit 100 acquires medical information (eg, information on medical images and electronic medical records) input to the medical diagnosis support apparatus 10 from a server or the like (not shown). Then, the medical information acquisition unit 100 outputs the acquired medical information to the abnormal shadow detection unit 101.

  Subsequently, in step S302, the abnormal shadow detection unit 101 analyzes the medical image of the diagnosis target included in the medical information acquired from the medical information acquisition unit 100, and detects an abnormal shadow. Here, in the present embodiment, the medical image is a chest CT image, and an abnormal shadow (tumor shadow) of the lung is detected therefrom. Then, for example, the abnormal shadow detection unit 101 automatically detects the existence position of the abnormal shadow by performing image analysis on the medical image by the CAD technology described in Patent Literature 1, and further surrounds the abnormal shadow. Automatically set the region of interest. Then, the abnormal shadow detection unit 101 outputs the automatically set image of the region of interest to the CAD finding generation unit 102. Further, the abnormal shadow detection unit 101 stores information indicating the automatically set range of the region of interest on the magnetic disk 202. The doctor may perform the detection of the abnormal shadow and the setting of the region of interest. Further, when an abnormal shadow is detected in advance and stored in a server or the like (not shown) together with the medical information, the configuration may be such that it is acquired together with the medical information.

  In step S303, the CAD finding generation unit 102 extracts an image feature amount from the region of interest of the abnormal shadow acquired from the abnormal shadow detecting unit 101, and generates a finding that a doctor can use for diagnosis based on the extracted feature amount. Is a CAD finding. Note that the CAD finding generation processing may be performed based on any method that can be implemented in this field. For example, the CAD finding generation unit 102 generates a finding with a finding item name: {size} from the size (maximum diameter) of the tumor, which is one of the image feature amounts. Further, the CAD finding generation unit 102 generates a finding of the finding item name: {overall shape} using the degree of unevenness of the edge, which is one of the image feature amounts. Further, the CAD finding generation unit 102 generates a finding of the finding item name: {cut} based on the distance of the edge which is one of the image feature amounts. In addition, the CAD finding generation unit 102 can perform processing such as selecting a finding of the finding item name: {radial} based on the variance of the distance between the edges, which is one of the image feature amounts. Since these processes do not represent the features of the present embodiment, description thereof will be omitted.

  Further, the CAD finding generation unit 102 infers the finding of the finding item name: {estimated disease name} using the image feature amount set extracted above. In the present embodiment, classification is made into three disease names, {estimated disease name-primary lung cancer}, {estimated disease name-metastatic lung tumor}, {estimated disease name-others}. As an inference unit for estimating a disease name, for example, a support vector machine, a neural network, or a Bayesian network can be used. However, the inference device is not limited to this method.

  Then, the CAD finding generating unit 102 outputs the CAD finding generated above to the finding managing unit 103.

  In step S <b> 304, the finding management unit 103 determines whether or not a past interpretation report on the same abnormal shadow as the diagnosis target is stored in the magnetic disk 202. Here, the association between the abnormal shadow to be diagnosed and the abnormal shadow in the past interpretation report is performed by a known method based on, for example, the position information of the abnormal shadow in the lung field.

  Then, as a result of the determination in step S304, if there is a past interpretation report regarding the same abnormal shadow as the abnormal shadow to be diagnosed, the finding information of the abnormal shadow is acquired from the magnetic disk 202 and the reliability evaluation unit Then, the process proceeds to step S306. On the other hand, if there is no previous interpretation report regarding the abnormal shadow that is the same as the abnormal shadow to be diagnosed, the process proceeds to step S305.

  When proceeding to step S305 (when there is no past interpretation report), the finding presenting unit 104 displays the CAD findings acquired from the finding managing unit 103 together with the abnormal shadow detection result stored on the magnetic disk 202 in S302. The image is displayed on the monitor 204 in a form that can be read. Then, the process proceeds to step S309.

FIG. 5 is a schematic diagram showing the first embodiment and showing an example of the finding input screen 500 in step S305 of FIG.
The finding input screen 500 shown in FIG. 5 includes a medical image 501 to be diagnosed, a region of interest 502 surrounding an abnormal shadow detected by CAD, a region for inputting finding information (finding input region) 503, and an instruction to complete the input of interpretation findings. An input completion button 504 for inputting is provided. The doctor interprets the image while referring to the medical image 501 displayed on the finding input screen 500, and refers to the region of interest 502 set by the CAD. At this time, when the doctor specifies the region of interest 502, the finding is displayed in the finding input region 503 as an initial value for the doctor to input the interpretation finding using the template-based input support method. In this way, by displaying the CAD findings as the initial values for the doctor to input the interpretation findings, it is possible to save the trouble of the doctor to input the interpretation findings.

Here, the description returns to FIG.
When the process in step S305 ends, the process proceeds to step S309.
In step S309, the interpretation finding acquisition unit 105 acquires the interpretation finding after the doctor changes the CAD finding with reference to the medical image and the CAD finding displayed on the monitor 204. In the present embodiment, the doctor inputs interpretation findings of the medical image displayed on the monitor 204 using an operation unit such as the mouse 205 and the keyboard 206. This processing is realized by providing a function that can be selected by a GUI using, for example, a template-based interpretation finding input support method.

FIG. 6 is a schematic diagram showing the first embodiment, and showing an example of a finding input screen 600 after the doctor inputs the interpretation findings in step S309 of FIG. 6, 600 to 604 represent the same as 500 to 504 in FIG. 5, respectively.
In FIG. 6, the interpretation findings 605, 606, and 607 respectively represent the interpretation findings after the CAD findings are changed by the doctor. In the example of FIG. 6, regarding the interpretation finding 605, the doctor has changed the CAD finding {size-small} to {size-medium}. Regarding the interpretation finding 606, the doctor has changed the CAD finding {radial-very strong} to {radial-medium}. Regarding the interpretation finding 607, the doctor has changed the CAD finding {presumed disease name-primary lung cancer} to {presumed disease name-metastatic lung tumor}. The doctor presses the input completion button 604 to complete the input of the interpretation finding.

  Then, when the input completion button 604 is pressed by the doctor, the interpretation finding acquisition unit 105 acquires the interpretation finding after the CAD finding is changed in the finding input area 603 and outputs the acquired interpretation finding to the finding management unit 103.

Here, the description returns to FIG.
When the process in step S309 ends, the process proceeds to step S310.
In step S310, the finding management unit 103 compares the interpretation finding input via the operation unit acquired in step S309 with the CAD finding, thereby finding finding change information (changing the finding of what item from what to what). (Information indicating whether the operation has been performed). Then, the CAD finding, the interpretation finding, and the finding change information acquired in the processing so far are stored and recorded on the magnetic disk 202 as finding information forming a part of the interpretation report on the abnormal shadow. It should be noted that the finding change information records information of "no change found" even when there is no actually changed finding. In this case, since the finding change information itself exists, if the judgment processing in step S304 is performed in a state where this information is recorded in the past abnormal shadow, it is determined that a past interpretation report exists. After the determination is made, the process proceeds to step S306.

The above is the description of the processing (the processing after step S305) in the case where there is no past interpretation report regarding the same abnormal shadow as the abnormal shadow to be diagnosed.
Next, a description will be given of a process (step S306 and subsequent processes) in the case where there is a past interpretation report on the same abnormal shadow as the abnormal shadow to be diagnosed.

  In step S306 (when there is a past image reading report on the same abnormal shadow as the abnormal shadow to be diagnosed), the reliability evaluation unit 106 determines each of the finding items based on the past finding information acquired in step S304. Evaluate the reliability of CAD findings.

  In this step, if the CAD finding is changed for a certain finding item, it is considered that the doctor has determined that the CAD finding of the finding item is incorrect, and it is determined that "the reliability of the CAD finding is not reliable". I do. Conversely, if the CAD finding has not been changed for a certain finding item, it is assumed that the physician has determined that the CAD finding for that finding item is correct, and that "the reliability of the CAD finding is reliable".

Next, the determination processing (the reliability evaluation processing of CAD findings in step S306 in FIG. 3) performed by the reliability evaluation unit 106 will be described with reference to the flowchart in FIG.
FIG. 7 is a flowchart illustrating an example of a detailed processing procedure in step S306 in FIG.

  When the process of step S306 in FIG. 3 starts, first, in step S701 in FIG. 7, the reliability evaluation unit 106 sets the finding number i = 1 of the CAD finding to be processed and acquires the finding number n. .

  Subsequently, in step S702, the reliability evaluation unit 106 acquires the i-th CAD finding (CAD finding i) from the finding change information, and determines whether there is a record changed by the doctor. As a result of this determination, if the CAD finding i has been changed, the process proceeds to step S703. If the CAD finding i has not been changed, the process proceeds to step S704.

  Considering the case where the example shown in FIG. 6 is a past abnormal shadow corresponding to the abnormal shadow to be diagnosed, regarding finding 1 {size}, finding 5 {radial}, and finding 20 {estimated disease name}, , Physician has changed CAD findings. Therefore, when i = 1, 5, 20, the process proceeds to step S703. On the other hand, regarding the finding 2 {overall shape}, the finding 3 {bronchial transparent image}, and the finding 4 {cut}, the doctor did not change the CAD findings. Therefore, when i = 2, 3, and 4, the process proceeds to step S704.

  In step S703, the reliability evaluation unit 106 receives the determination result that the CAD finding i has been changed in the past by the doctor, and determines that “the CAD finding i is not reliable”. Then, the reliability evaluation unit 106 calculates the reliability indicating the degree of reliability of the CAD finding i, and then proceeds to step S705.

In the present embodiment, it is considered that the smaller the degree to which the physician changes the CAD finding, the higher the reliability of the CAD finding is, and conversely, the larger the degree to which the CAD finding is changed, the lower the reliability of the CAD finding is. I reckon. Here, when the value of the CAD finding i, which is a finding item, is expressed by a degree or a probability, the maximum value of the value is Vi _MAX , the value of the CAD finding i is Vi _PRE , and the interpretation finding after the change is obtained. Let the value of i be Vi _POST . In this case, reliability Ri _CAD of CAD findings i is expressed by the following equation.
_{Ri CAD = (Vi MAX - |} Vi POST -Vi PRE |) / Vi MAX ··· (1)
However, if the CAD finding i itself cannot be expressed in terms of the degree or the probability, the degree of the degree of the interpretation finding changed by the doctor cannot be evaluated, and thus the reliability is not calculated.

FIG. 4 is a schematic diagram illustrating the first embodiment and illustrating a relationship between CAD findings and interpretation findings. In the example of FIG. 4A, for the finding 1 {size}, the doctor changes the CAD finding {size-small} to the interpretation finding {size-medium}. Therefore, the reliability is obtained as R1 _CAD = (4- | 2-1 |) /4=0.75. In the example of FIG. 4B, regarding the finding 5 {radial}, the doctor has changed the CAD finding {radial-very strong} to the interpretation finding {radial-medium}. Accordingly, reliability, R5 _CAD = determined with /4=0.5 (4- | | 4-2). That is, in the present embodiment, as shown in FIG. 4, after the CAD finding is presented, the reliability evaluation unit 106 determines the reliability of the CAD finding based on the degree to which the doctor has changed the CAD finding as a radiological finding. I try to evaluate the degree. For the finding 20 {estimated disease name}, the reliability is not calculated because the state of the finding cannot be represented by the degree or the probability.

Here, the description returns to FIG.
In step S704 in FIG. 7, the reliability evaluation unit 106 determines that “CAD finding i has reliable reliability” in response to the determination result that the CAD finding has not been changed in the past by the doctor. Then, the reliability of the CAD findings, after calculating the Ri _CAD = 1.0, the process proceeds to step S705. Here, in the example shown in FIG. 6, the reliability of the CAD finding i is 1.0 (100%) for the finding 2 {overall shape}, the finding 3 {bronchial transparent image}, and the finding 4 {cut}. Become.

  In step S705, the reliability evaluation unit 106 adds 1 to the finding number i.

  Subsequently, in step S706, the reliability evaluation unit 106 compares the finding number i with the finding number n, and terminates the processing of the flowchart in FIG. 7 if the finding number i is larger than the finding number n. On the other hand, if the finding number i is equal to or less than the finding number n, the process proceeds to step S702. After that, the processes after step S702 are performed again.

This is the end of the description of the flowchart in FIG.
As described above, the reliability of a CAD finding in a past image (past medical image) can be evaluated for each finding item.

  FIG. 8 shows the first embodiment, and is a diagram summarizing information on the reliability of CAD findings for each finding item. The reliability evaluation unit 106 outputs the CAD finding reliability information for each finding item described in FIG. 8 to the finding presentation unit 104.

Here, the description returns to FIG.
When the process of step S306 in FIG. 3 ends, the process proceeds to step S307.
In step S307, the finding presenting unit 104 associates the CAD finding reliability information in the past image acquired from the reliability evaluating unit 106 with the CAD finding in the diagnosis target image acquired from the finding managing unit 103.

  In this step, if the reliability of the CAD finding in the past image is high, it is determined that the CAD finding is reliable even in the diagnosis target image for the same finding item.

  Therefore, in this step, the CAD findings for the diagnosis target image are associated with the reliability information of the CAD findings obtained from the past images for each finding item.

  For example, as for the finding item 1 {size}, information of “reliability of CAD finding: not certain (reliability 75%)” is associated with the CAD finding in the diagnosis target image. Similarly, regarding the finding item 4 {cut}, "CAD finding reliability: certainty (reliability 100%)" is associated with the CAD finding of the diagnosis target image.

  Subsequently, in step S308, the finding presenting unit 104 displays, on the monitor 204, the CAD finding of the diagnosis target image on which the process of associating the CAD finding reliability information in the past image has been performed in step S307.

  FIG. 10 is a schematic diagram showing the first embodiment and showing an example of the finding input screen 1000 in step S308 of FIG. 10, 1001 to 1004 denote the same components as 601 to 604 in FIG. 6, respectively. Specifically, the finding input area 1003 shows CAD findings in the diagnosis target image. Further, a finding input screen 1000 shown in FIG. 10 shows a warning message 1005 warning the possibility of an error in the CAD finding, and finding items 1006, 1007 and 1008 of the CAD finding warned by the warning message 1005. . Further, on the finding input screen 1000 shown in FIG. 10, messages 1009 and 1010 indicating the reliability of the CAD finding corresponding to the finding items 1006 and 1007 warned by the warning message 1005 are shown. At this time, as for the finding item 1008 warned by the warning message 1005, the finding item is the estimated disease name, and the reliability is not calculated. Therefore, a message indicating the reliability of the CAD finding is not displayed.

  In FIG. 10, the finding items 1006, 1007, and 1008 indicate that the CAD findings have been corrected (changed) in the past image, and the reliability of the CAD findings has been determined to be unreliable even in the diagnosis target image. Be warned. On the other hand, for the finding items 2, 3, and 4, the CAD findings were not corrected (changed) in the past image, and the CAD findings were judged to be reliable even in the diagnosis target image, so a warning was presented. Absent. As described above, by giving a warning only to the finding item determined to be unreliable, the doctor can determine how much the CAD finding is helpful.

Here, the description returns to FIG.
When the process of step S308 in FIG. 3 ends, the process proceeds to step S309.
In step S309, the interpretation finding acquisition unit 105 acquires an interpretation finding after changing the CAD finding by referring to the medical image and the CAD finding displayed on the monitor 204 by the doctor. Then, when the input completion button 1004 is pressed by the doctor, the interpretation finding acquisition unit 105 acquires the interpretation finding after changing the CAD finding in the finding input area 1003 and outputs the acquired interpretation finding to the finding management unit 103.

  Subsequently, in step S310, the finding management unit 103 compares the interpretation finding acquired in step S309 with the CAD finding, thereby obtaining the finding change information (information indicating which finding has been changed from what to what). Extract. Then, the CAD finding, the interpretation finding, and the finding change information acquired in the processing so far are stored and recorded on the magnetic disk 202 as finding information forming a part of the interpretation report on the abnormal shadow. It should be noted that the finding change information records information of "no change found" even when there is no actually changed finding.

  This is the end of the description of the flowchart in FIG.

  In the present embodiment, whether or not to present a warning is changed depending on whether or not the reliability of the CAD finding is reliable, but the display method is not limited to this. For example, information on the reliability associated with the CAD findings may be always presented in step S306 in FIG. At this time, if the reliability is determined to be reliable, "reliability: certain" is always displayed, so that the doctor can recognize that the CAD finding of the finding item is helpful.

  According to the configuration described above, by presenting the CAD finding reliability information in association with the CAD finding for the image to be diagnosed, the physician can refer to the determination of the CAD finding reliability, which is useful for diagnosis. be able to.

(Modification 1 of the first embodiment)
In the first embodiment described above, the CAD findings are presented as initial values for inputting the interpretation findings in a template format, and information on the difference between the CAD findings and the interpretation findings is determined by whether or not the doctor has changed the CAD findings. Was to get. However, a method of acquiring information on a difference between a CAD finding and a radiological finding is not limited to this. For example, a method may be employed in which a doctor compares a radiological finding created without using a CAD finding as an initial value with a CAD finding separately generated, and acquires information on a difference between the two. In this case, if only the interpretation findings created by the doctor are recorded in advance, the CAD findings can be generated by going back to the past medical images and can be compared with the recorded interpretation findings. Therefore, in the case of a past medical image, since an interpretation finding is usually given, information on a difference between a CAD finding and an interpretation finding can be acquired for any past case.

(Modification 2 of the first embodiment)
In the first embodiment described above, the finding change information of the past image is recorded in the finding management unit 103, and when a new image is to be diagnosed, the finding change information is read to evaluate the reliability. Thus, the evaluated reliability information is presented in association with the finding information. However, the flow up to the presentation of the reliability information is not limited to this method. For example, by recording the result of evaluating the reliability of a CAD finding from the finding change information of a past image, when a new image is to be diagnosed, the previously recorded reliability information is read and read. May be presented in association with the finding information.

  Thus, when a diagnosis target image is input, the trouble of evaluating the reliability of CAD findings can be omitted, and the processing can be made more efficient.

[Second embodiment]
In the first embodiment, the reliability of CAD findings is evaluated from one of the past images of the same subject (same subject). However, the method of evaluating the reliability of CAD findings is not limited to this, and may be another method. In the present embodiment, when there are a plurality of past images of the same subject (the same subject), a diagnosis is made from the plurality of past images (hereinafter, referred to as “time-series past images (time-series inspection data)”). A CAD finding of a past image related to the target image is selected. Then, the reliability of the CAD finding is evaluated based on only the selected CAD finding. For example, if the characteristics of the medical image of the subject (subject) have changed significantly due to an operation such as removal of a tumor in the past, the characteristics of the medical image are related to the diagnosis target image (postoperative). The information is selected so that only the finding information in the postoperative past image is selected. Thus, the reliability of the CAD findings is evaluated with high accuracy by excluding, from the evaluation target, pre-operational past images that have significantly different characteristics from the diagnosis target image.

  The functional configuration of the medical diagnosis support apparatus 10 according to the present embodiment is the same as that of the first embodiment shown in FIG. However, the CAD finding generation unit 102 stores the image feature amount extracted from the medical image, the finding management unit 103 manages the CAD findings in the time-series past image, and the reliability evaluation unit 106 diagnoses the time-series past image from the time-series past image. The difference from the first embodiment is that the reliability of the CAD finding is evaluated based only on the CAD finding in the past image related to the target image. The basic configuration (hardware configuration) of a computer for realizing each function of each functional configuration unit illustrated in FIG. 1 by software is the same as that in FIG. 2 in the first embodiment.

  The flowchart of the entire processing procedure performed by the medical diagnosis support apparatus 10 is the same as that in FIG. However, part of the processing in steps S303, S304, and S306 is different from the first embodiment. Hereinafter, only differences from the first embodiment will be described.

  The process of acquiring medical information in step S301 of FIG. 3 and the process of detecting an abnormal shadow in step S302 are the same as in the first embodiment.

  In the present embodiment, subsequently, in step S303, the CAD finding generation unit 102 extracts an image feature amount from the region of interest of the abnormal shadow acquired from the abnormal shadow detecting unit 101, and obtains a finding that a doctor can use for diagnosis. Generate and use this as CAD findings. The method of generating the CAD findings is the same as in the first embodiment, and a description thereof will not be repeated. In the present embodiment, the image feature amount used for generating the CAD findings is stored on the magnetic disk 202 in association with the ID of the medical image. This ID is assigned, for example, to medical images of the same subject (the same subject) as 1, 2, 3,.

Subsequently, in step S304, the finding management unit 103 first stores the CAD findings for the acquired diagnosis target image on the magnetic disk 202 in association with the ID of the diagnosis target image. Then, the finding management unit 103 outputs the CAD findings of the diagnosis target image to the finding presentation unit 104.
Next, the finding management unit 103 determines whether or not a past interpretation report on the same abnormal shadow as the abnormal shadow to be diagnosed is stored in the magnetic disk 202 and exists. Specifically, it is determined whether or not finding change information indicating whether or not a doctor has changed a CAD finding for the same subject (same subject) in the past is stored in the magnetic disk 202.
If the finding change information is stored on the magnetic disk 202 as a result of this determination, the finding management unit 103 performs a CAD finding on all the time-series past images in which the finding change information is recorded. It is acquired from the magnetic disk 202 together with the finding change information. Then, the finding management unit 103 outputs the CAD findings and the finding change information of the acquired time-series past image and the CAD findings of the diagnosis target image to the reliability evaluation unit 106, and proceeds to step S306.
On the other hand, if the finding change information is not stored on the magnetic disk 202, the process proceeds to step S305.

  In the present embodiment, after proceeding to step S306, the reliability evaluation unit 106 acquires the image feature amounts stored in the magnetic disk 202 by the CAD finding generation unit 102 in step S303 for all the time-series past images. Then, the reliability evaluation unit 106 selects a past image related to the diagnosis target image from the time-series past images based on the acquired image feature amounts, and extracts the CAD findings. Next, the reliability evaluation unit 106 evaluates the reliability of each CAD finding based on only the extracted CAD findings.

Here, first, a method of extracting a CAD finding of a past image related to a diagnosis target image based on an image feature amount will be specifically described.
In the present embodiment, a CAD finding of a past image related to an image to be diagnosed is extracted based on at least one type of image feature amount extracted to generate a CAD finding. In step S303, at least one type of image feature stored on the magnetic disk 202 is selected, and all image features of the corresponding type are acquired from the time-series past image and the image to be diagnosed. For example, the feature amount S of the size (maximum diameter) of the tumor extracted to generate the finding item 1: {size} is acquired for all the time-series past images and the diagnosis target image. Here, when the ID of a certain image included in the time-series past image is represented by i, the feature amount of the size of the tumor corresponding to the image ID: i is represented by Si. In addition, a feature amount of the size of the tumor corresponding to the diagnosis target image is represented by St. At this time, a set of feature amounts related to the feature amount St is defined as Sgr, and all feature amounts Si satisfying both of the following conditions 1 and 2 are selected.

Condition 1: All feature quantities Si whose difference from feature quantity St is within a predetermined range
Condition 2: When the time series is traced back from the feature amount St as a starting point, all feature quantities Si included before the magnitude of the rate of change between two time-series adjacent feature quantities first exceed a predetermined threshold value

  The above condition 1 is intended to select only past images having an image feature amount close to the image feature amount of the diagnosis target image. That is, Condition 1 above selects a past image related to the diagnosis target image from the plurality of time-series past images based on the difference between the characteristic of the diagnosis target image and the characteristics of the plurality of time-series past images. I do.

  Condition 2 described above is intended to select only the past images after the time when there is a time when the image feature amount changes abruptly between the time-series past images. That is, the condition 2 is to select a past image related to the diagnosis target image from the plurality of time-series past images based on a temporal change in the characteristics of an image sequence including the diagnosis target image and the plurality of time-series past images. I do.

FIG. 9 is a characteristic diagram illustrating the second embodiment, in which the feature amount S is plotted in time series.
A specific example of selecting a set Sgr satisfying the above conditions 1 and 2 will be described with reference to FIG.

  In FIG. 9, the time-series feature amounts S1 to S10 and the feature amount St of the diagnosis target image are plotted on a graph. The range D represents a range of the value of the predetermined feature amount around the feature amount St. At this time, the feature quantity Si satisfying the condition 1 is a point included in the range D with St as a center, and thus is six points {S2, S3, S4, S8, S9, S10}.

  Subsequently, when two adjacent feature amounts in time series are Sj and Sj + 1, a change rate between the feature amounts Sj and Sj + 1 is set to Gj. G5 in FIG. 9 represents the rate of change between the feature value S5 and the feature value S6. Here, a predetermined threshold value for the change rate Gj is defined as Gth (not shown). At this time, the feature quantities Si satisfying the condition 2 are all feature quantities included between Sj + 1 and St when a certain change rate Gj first satisfies Gj> Gth when the time series is traced back from St. Represents In the example of FIG. 9, the change rate G5 first satisfies G5> Gth, and the five feature amounts satisfying the condition 2 are {S6, S7, S8, S9, S10}.

  Therefore, a set Sgr of feature amounts satisfying both the condition 1 and the condition 2 is the three points {S8, S9, S10}. Then, as the time-series past images related to the diagnosis target image, the CAD findings assigned to the images of the image IDs: 8, 9, and 10 corresponding to the feature amount set Sgr are acquired (extracted).

  As described above, by selecting the set Sgr of the feature amounts related to the feature amount St using the conditions 1 and 2, when the image feature of the region of interest is significantly changed due to surgery or the like, the time series past Only images can be selected. Further, among them, it is possible to select only a time-series past image whose image feature value is close (similar) to the diagnosis target image.

  In the present embodiment, as a method of selecting a feature amount related to a diagnosis target image in consideration of a change in the feature amount, a feature amount is selected by setting a threshold value for a change rate between adjacent feature amounts in time series. The method of selecting the feature is not limited to this. For example, a specific function model may be fitted with the feature amount St of the diagnosis target image as a base point, and only the feature amount Si existing in a predetermined range may be selected from the fitted model. As this model, for example, a straight line model may be applied. This applies when it is assumed that the disease progresses spontaneously, or that changes during treatment with medication or the like can be approximated linearly. Alternatively, a non-linear functional model representing the progress of a disease or a change in treatment may be constructed based on medical statistical information and applied. In this way, by using the function model, whether the change between the feature amounts adjacent in time series is in line with the change when the disease progresses spontaneously or is treated by medication or the like, the diagnosis target image is determined. The feature amount Si of the related image can be selected.

  In the present embodiment, the method of selecting the past image related to the diagnosis target image using the image feature amount: the size of the tumor corresponding to the finding item 1 {size} has been described. Is not limited to this method. For example, past images respectively related to a plurality of other finding items may be selected in the same manner as described above. In addition, by extracting only common past images from the past images obtained for each of a plurality of finding items, the reliability is evaluated using the common past images for all finding items. Is also good. Alternatively, the reliability may be evaluated using the past images obtained for each of the plurality of finding items independently.

  Further, in the present embodiment, the past image related to the diagnosis target image is selected based on the image feature amount, but the judgment material for selecting the past image is not limited to this. For example, the selection may be made based on a CAD finding generated from the image feature amount or an interpretation finding written by a doctor. In this case, the vertical axis in FIG. 9 can be selected in the same manner as described above by replacing the feature quantity with the size of the finding item.

Next, a method of evaluating the reliability of the CAD findings based on the CAD findings of the past images acquired as described above will be described.
In the above example, the reliability is evaluated based on the CAD findings of the past images corresponding to the image IDs: 8, 9, and 10. First, the reliability of CAD findings is evaluated for each image. This method is the same as step S306 in FIG. 3 in the first embodiment, and a description thereof will be omitted. Then, the reliability information evaluated for each image is integrated to evaluate the final reliability information. In the present embodiment, final reliability information is obtained by taking the average value of the reliability information of the obtained individual images. In the example of FIG. 9, it is assumed that the evaluation results of the reliability of the image IDs: 8, 9, and 10 for the finding item 1: {size} are as follows.
・ ID: 8 Reliability: Not certain Reliability R1 _CAD : 75%
・ ID: 9 Reliability: Reliable Reliability R1 _CAD : 100%
・ ID: 10 Reliability: Reliable Reliability R1 _CAD : 100%
At this time, the reliability of the final CAD findings R1 _CAD, When _ALL, R1 _CAD, obtained as _{ALL = (1.0 + 1.0 + 0.75} ) /3≒0.92. Then, since the reliability is not 100%, the reliability evaluation unit 106 determines that the reliability is “uncertain”. Therefore, the reliability information of the final CAD findings is as follows.
・ Overall reliability: not reliable Reliability R1 _{CAD, ALL} : 92%

  However, in the present embodiment, the final reliability is obtained from the average value of the individual reliability, but the reliability evaluation method is not limited to this method. For example, weighting may be performed for each image ID depending on how close each image is taken to the image to be diagnosed (the older the image is, the smaller the weight is), and then an average value may be calculated. Thus, the older the image to be diagnosed is, the more likely the state is to be far apart, so that their influence on reliability can be reduced. Alternatively, the mode value of each reliability may be used. As a result, if the reliability of most data is “certain” but at least one of the data is “uncertain”, it is determined to be “unreliable” and the warning display Can be prevented.

  Then, the reliability evaluation unit 106 outputs the CAD finding reliability information for each finding item evaluated as described above to the finding presentation unit 104.

  According to the above-described configuration, by evaluating the reliability of the CAD findings based only on the CAD findings of the past images related to the diagnosis target images, the past images having significantly different features from the diagnosis target images due to the influence of surgery or the like can be evaluated. Can be removed from the subject. As a result, the reliability of the CAD findings can be evaluated with high accuracy.

[Other embodiments]
In the above-described embodiment, the interpretation finding is input via the operation unit such as the mouse 205 and the keyboard 206. However, the present invention is not limited to this, and a touch panel function of the display unit may be used. In addition, a gesture or voice input of a doctor or the like may be detected to acquire information on the interpretation finding. As in these examples, a user interface for inputting information from a person involved in diagnosis is also included in the operation unit of the present invention.
The present invention is also realized by executing the following processing.
That is, software (program) that realizes the functions of the above-described embodiments is supplied to a system or an apparatus via a network or various storage media, and a computer (or a CPU or an MPU or the like) of the system or the apparatus reads out the program. This is the process to be performed.
The program and a computer-readable recording medium storing the program are included in the present invention.

  It should be noted that the above-described embodiments of the present invention are merely examples of specific embodiments for carrying out the present invention, and the technical scope of the present invention should not be interpreted in a limited manner. It is. That is, the present invention can be implemented in various forms without departing from the technical idea or the main features.

Reference Signs List 10 medical diagnosis support device, 100 medical information acquisition unit, 101 abnormal shadow detection unit, 102 CAD finding generation unit, 103 finding management unit, 104 finding presentation unit, 105 interpretation finding acquisition unit, 106 reliability evaluation unit

Claims (15)

By analyzing test data of the subject, generating means for generating information indicating the state of the subject regarding each of a plurality of items for diagnosis of the subject as a generation finding,
Information acquisition means for acquiring information about a difference with respect to the item in which the change has been made, in accordance with a change made to the generation finding;
Display control means for displaying, on a display unit, an item for which information about the difference has been obtained and an item for which information about the difference has not been obtained among at least some of the plurality of items, ,
An information processing apparatus comprising:   The display control means displays the generated finding together with an image included in the inspection data and identification information indicating that the generated finding is a finding automatically generated by analyzing the image. The information processing apparatus according to claim 1, wherein the information is displayed on a unit.   The display control means causes the display unit to display the identification information for an item for which the information regarding the difference has not been obtained, and does not display the identification information on the display unit for an item for which the information regarding the difference has been obtained. And displaying, on the display unit, an item for which the information regarding the difference is obtained and an item for which the information regarding the difference is not obtained among at least some of the plurality of items. The information processing apparatus according to claim 2, wherein By analyzing test data of the subject, generating means for generating information indicating the state of the subject regarding each of a plurality of items for diagnosis of the subject as a generation finding,
Information acquisition means for acquiring information indicating that the change has been made, for the item that has been changed with respect to the generation finding,
At least some of the plurality of items can be distinguished from items for which information indicating that the change has been obtained and items for which information indicating that the change has been obtained have not been obtained. Display control means for displaying on the display unit in a state where
An information processing apparatus comprising: By analyzing the test data of the subject, for the item for diagnosis of the subject, generating means for generating information indicating any one of a plurality of states that the subject can take as a generation finding,
Information acquisition means for acquiring information indicating that the change has been made, for the item that has been changed with respect to the generation finding,
Display control for displaying the generation finding on a display unit in a state where the information indicating that the change has been obtained and the information indicating that the change has not been obtained can be identified. Means,
An information processing apparatus comprising:   The display control means may include at least one of the generated finding, an image included in the inspection data, and identification information indicating that the generated finding is a finding automatically generated by analyzing the image. The information processing apparatus according to claim 4, wherein the information is displayed on the display unit.   The display control means causes the display unit to display the identification information for an item for which information indicating that the change has been obtained has not been obtained, and for an item for which the information indicating that the change has been obtained has been obtained. By not displaying the identification information on the display unit, it is possible to identify an item from which information indicating that the change has been obtained and an item from which information indicating that the change has been obtained have not been obtained. The information processing apparatus according to claim 6, wherein the information is displayed on the display unit in a state. Findings acquisition means for acquiring a generation finding that is information indicating the state of the subject for each of a plurality of items for diagnosis of the subject obtained by analyzing the test data of the subject,
Display control means for displaying, on at least a part of the plurality of items, an item that has been changed with respect to the generation finding and an item that has not been changed on the display unit in an identifiable state When,
An information processing apparatus comprising: Obtaining a finding that obtains a generation finding, which is information indicating one of a plurality of possible states of the subject with respect to an item for diagnosis of the subject obtained by analyzing test data of the subject. Means,
Display control means for displaying the generated findings on a display unit in a state where the change is made to the generated findings and the case where the changes are not made, and
An information processing apparatus comprising:   The information processing apparatus according to claim 1, wherein the display control unit displays the generated findings and images included in the inspection data side by side on the display unit.   The information processing apparatus according to claim 10, wherein the display control unit causes the display unit to display a region of interest related to the generation finding on the image.   The information processing apparatus according to claim 1, wherein the display control unit causes the display unit to display an operation unit for giving an instruction to end the change to the generation finding.   13. The information processing apparatus according to claim 1, wherein the generation finding includes information indicating a disease name of the subject. An operation method of an information processing apparatus having a generation unit, an information acquisition unit, and a display control unit,
The generation unit, by analyzing test data of the subject, a generation step of generating information indicating the state of the subject regarding each of a plurality of items for diagnosis of the subject as a generation finding,
An information acquisition step of acquiring the information indicating that the change has been made, for the item in which the information acquisition means has changed the generation finding;
The display control unit is configured to obtain, from among at least some of the plurality of items, an item for which information indicating that the change has been obtained and information for indicating that the change has been performed. A display control step of displaying on the display unit an item that can not be identified,
An operation method of an information processing apparatus, comprising: A non-transitory computer-readable storage medium storing a program for causing a computer to execute each step of the method for operating an information processing apparatus according to claim 14.

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