Patent Publication Number: US-2022215962-A1

Title: Image diagnosis support device, operation method of image diagnosis support device, and operation program of image diagnosis support device

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of International Application No. PCT/JP2020/036274, filed on Sep. 25, 2020, which is incorporated herein by reference in its entirety. Further, this application claims priority from Japanese Patent Application No. 2019-173856, filed on Sep. 25, 2019, the disclosure of which is incorporated by reference herein in their entirety. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure relates to an image diagnosis support device, an operation method of an image diagnosis support device, and a non-transitory storage medium storing a program. 
     Related Art 
     In recent years, in addition to radiography apparatuses by using radiation, such as X-rays or gamma rays, due to advances in image capturing apparatuses, such as a computed tomography (CT) apparatus, an ultrasound (US) diagnostic apparatus, a magnetic resonance imaging (MM) apparatus, a positron emission tomography (PET) apparatus, and a single-photon emission computed tomography (SPECT) apparatus, it has been possible to make an image diagnosis by using a medical image having a high resolution that has a high quality. In addition, in the field of image diagnosis, the technology based on artificial intelligence (hereinafter referred to as AI) is advanced. 
     As the AI, for example, there is a computer-aided diagnosis (hereinafter referred to as CAD), which is a diagnosis support function by a computer. The medical image acquired by the image capturing apparatus is analyzed by the CAD, a region, a position, a volume, and the like of a lesion or the like included in the medical image are extracted, and these results are acquired as analysis results. The analysis results generated by analysis processing in this way are used for the image diagnosis by displaying the analysis results on the medical image, or storing the analysis results in a database in association with a patient name, a gender, an age, and examination information of the image capturing apparatus or the like that acquires the medical image. 
     In addition, based on the medical image acquired by the image capturing apparatus, a new medical image, which is an image diagnosis target, is also generated by using the AI technology. As an example, the technology has been proposed in which a slice thickness of a CT image acquired by a CT apparatus is virtually thinned by using the AI technology (see JP2008-110098A). This technology is the technology of virtually generating the CT image having the slice thickness of about 1 mm, for example, based on the CT image having the slice thickness of about 5 mm set at the time of imaging. By virtually thinning the slice thickness, it is possible to improve the visibility of bones or improve the image quality in a case in which the image is three-dimensionally displayed. 
     In this way, information more useful for the image diagnosis may be able to be obtained by applying the image analysis technology by using the AI technology and the image generation technology by using the AI technology to the medical image captured by the image capturing apparatus. 
     Here, a medical image to which the AI technology is applied to a medical image captured by an image capturing apparatus is referred to as an AI image. In addition, in the medical image captured by the image capturing apparatus, a medical image to which the AI technology is not applied is referred to as a non-AI image in comparison with the AI image. As described above, the AI image includes the medical image obtained by analyzing the non-AI image by the AI technology and adding the analysis results obtained by the analysis to the non-AI image, which is an analysis target, and the medical image, which is newly generated separately from the original non-AI image, by applying the AI technology to the non-AI image. 
     In a case in which the AI image is used, the information useful for diagnosis is obtained, the number of situations in which the AI image is used has been increased in the medical field in which the medical image diagnosis is made. As the medical image used for the final definitive diagnosis of the patient, the AI image and the non-AI image are mixed. On the other hand, since the AI technology has insufficient accumulation of reliability at least at a current stage as compared to a determination of a doctor, it is currently unacceptable to rely on the AI image for all diagnosis evidence. 
     Under these circumstances, it is important to clearly distinguish whether the medical image used as the evidence of diagnosis is the AI image or the non-AI image. However, it may be difficult to distinguish between the AI image and the non-AI image only by looking at the image. Therefore, in a case in which the medical image is displayed on an image display terminal used by the doctor, it has been required to be able to easily distinguish whether or not the displayed medical image is the AI image. 
     SUMMARY 
     The present disclosure has been made in view of the circumstances described above, and is to provide an image diagnosis support device, an operation method of an image diagnosis support device, and a non-transitory storage medium storing a program for an image diagnosis support processing that can easily distinguish whether or not a medical image displayed on a display unit is an AI image. 
     A first aspect of the present disclosure relates to an image diagnosis support device comprising a display control unit that displays a medical image acquired by imaging a subject on a display unit, and a notification unit that gives, in a case in which an AI image, which is a medical image to which AI technology which is technology using artificial intelligence is applied, is displayed on the display unit as the medical image, a notification that the medical image displayed on the display unit is the AI image. 
     Note that, in the image diagnosis support device according to the present aspect, the AI image may be a medical image newly generated separately from the medical image by applying the AI technology to the medical image. 
     In addition, in the image diagnosis support device according to the present aspect, the AI image may be a medical image obtained by adding, to the medical image, an image analysis result obtained by performing an image analysis by using the AI technology based on the medical image. 
     In addition, in the image diagnosis support device according to the present aspect, the notification unit may display an AI flag indicating that the AI technology is applied to the AI image. 
     In addition, the image diagnosis support device according to the present aspect may further comprise the determination unit that determines whether or not the medical image displayed on the display unit is the AI image. 
     In addition, in the image diagnosis support device according to the present aspect, accessory information of the medical image may include information indicating whether or not the AI technology is applied, and the determination unit may determine whether or not the medical image is the AI image based on the accessory information. 
     In addition, the image diagnosis support device according to the present aspect may further comprise a browsing detection unit that detects whether or not a user browses the AI image, and a recording control unit that performs a control of recording a browsing history indicating that the AI image is browsed, based on a detection result of the browsing detection unit. 
     In addition, in the image diagnosis support device according to the present aspect, the browsing detection unit may detect that the AI image is browsed in a case in which the AI image, which is not displayed on the display unit, is displayed on the display unit. 
     In addition, in the image diagnosis support device according to the present aspect, the browsing detection unit may detect that the AI image is browsed in a case in which a display instruction for displaying the AI image, which is not displayed, on the display unit is input. 
     In addition, the image diagnosis support device according to the present aspect may further comprise a gaze detection unit that detects a gaze of the user, in which the browsing detection unit detects that the AI image is browsed in a case in which the gaze detection unit detects that the gaze of the user is directed to the AI image displayed on the display unit. 
     In addition, in the image diagnosis support device according to the present aspect, the recording control unit may further perform a control of recording a use history indicating that the AI image is used for an image diagnosis, based on an operation of the user. 
     In addition, the image diagnosis support device according to the present aspect may further comprise a warning unit that issues, in a case in which a report related to the image diagnosis is created in a state in which the use history is not present even though the browsing history is present, a warning that the use history is not present at least before creation of the report is terminated. 
     A second aspect of the present disclosure relates to an operation method of an image diagnosis support device, the method comprising displaying a medical image acquired by imaging a subject on a display unit, and giving, in a case in which an AI image, which is a medical image to which AI technology which is technology using artificial intelligence is applied, is displayed on the display unit as the medical image, a notification that the medical image displayed on the display unit is the AI image. 
     A third aspect of the present disclosure relates to a non-transitory storage medium storing a program that causes a computer to perform an image diagnosis support processing, the image diagnosis support processing includes displaying a medical image acquired by imaging a subject on a display unit, and giving, in a case in which an AI image, which is a medical image to which AI technology which is technology using artificial intelligence is applied, is displayed on the display unit as the medical image, a notification that the medical image displayed on the display unit is the AI image. 
     Note that a fourth aspect of the present disclosure relates to an image diagnosis support device comprising a memory that stores a command to be executed by a computer, and a processor configured to execute the stored command, in which the processor is configured to display a medical image acquired by imaging a subject on a display unit, and to give, in a case in which an AI image, which is a medical image to which AI technology which is technology using artificial intelligence is applied, is displayed on the display unit as the medical image, a notification that the medical image displayed on the display unit is the AI image. 
     According to the aspects of the present disclosure, it is possible to easily distinguish whether or not the medical image displayed on the display unit is the AI image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram showing a schematic configuration of a diagnosis support system to which an image diagnosis support device of one embodiment of the present disclosure is applied. 
         FIG. 2  is a diagram for describing an AI image and a non-AI image. 
         FIG. 3  is a diagram for describing the AI image. 
         FIG. 4  is a schematic block diagram showing a configuration of the image diagnosis support device according to the embodiment of the present disclosure. 
         FIG. 5  is a functional block diagram of the image diagnosis support device according to a first embodiment. 
         FIG. 6  is a diagram showing an example of display on a display screen of a display unit according to the first embodiment. 
         FIG. 7  is a flowchart showing processing performed in the first embodiment. 
         FIG. 8  is a functional block diagram of an image diagnosis support device according to a second embodiment. 
         FIG. 9  is a diagram showing an example of display (AI image non-display) on the display screen of the display unit according to the second embodiment. 
         FIG. 10  is a diagram showing an example of display (AI image display) of the display screen of the display unit according to the second embodiment. 
         FIG. 11  is a functional block diagram of an image diagnosis support device according to a third embodiment. 
         FIG. 12  is a diagram for describing a gaze detection unit. 
         FIG. 13  is a functional block diagram of an image diagnosis support device according to a fourth embodiment. 
         FIG. 14  is a diagram showing an example of the display screen of the display unit according to the fourth embodiment. 
         FIG. 15  is a diagram showing an example of display on the second display screen of the display unit according to the fourth embodiment. 
         FIG. 16  is a flowchart showing processing performed in the fourth embodiment (part 1). 
         FIG. 17  is a flowchart showing processing performed in the fourth embodiment (part 2). 
     
    
    
     DETAILED DESCRIPTION 
     In the following, a first embodiment of the present disclosure will be described with reference to the drawings.  FIG. 1  is a diagram showing a schematic configuration of a diagnosis support system to which an image diagnosis support device of the embodiment of the present disclosure is applied. As shown in  FIG. 1 , in the diagnosis support system, an image diagnosis support device  1  according to the present embodiment, an image capturing apparatus  2 , an image storage server  3 , and an image processing unit  5  are connected via a network  4  in a communicable state. 
     The image capturing apparatus  2  is an apparatus that images a site, which is a diagnosis target, of a patient, which is an example of a subject, and generates an image representing the site. Specifically, in addition to a radiography apparatus using radiation, such as X-rays, a CT apparatus, an ultrasound diagnostic apparatus, an MRI apparatus, a PET apparatus, and an SPECT apparatus. A medical image, such as a two-dimensional image and a three-dimensional image, captured by the image capturing apparatus  2  is transmitted to and stored in the image storage server  3 . 
     In the present disclosure, the three-dimensional image is a set of a plurality of slice images (tomographic images) output by a tomography apparatus, such as the CT apparatus or the MRI apparatus, and is also called volume data. In addition, in the present disclosure, the volume data acquired by one imaging is referred to as an “image group”. In addition, in the present disclosure, the two-dimensional image is each slice image included in the image group, an X-ray image acquired by simple X-ray imaging using, for example, the radiography apparatus, and the like. In the present disclosure, the three-dimensional image and the two-dimensional image are examples of a medical image. 
     The image processing unit  5  performs various pieces of processing on the medical image captured by the image capturing apparatus  2  by using the AI technology, which is the technology using artificial intelligence. Note that in the technology of the present disclosure, the medical image, which is obtained by performing various pieces of processing using the AI technology by the image processing unit  5  to the medical image captured by the image capturing apparatus  2 , is referred to as an AI image  51 . In addition, in the medical image captured by the image capturing apparatus  2 , the medical image to which the AI technology is not applied is referred to as a non-AI image  50  in comparison with the AI image.  FIG. 2  is a diagram for describing the AI image  51  and the non-AI image  50 . 
     As shown in  FIG. 2 , in a case in which the non-AI image  50  is input, the image processing unit  5  performs various pieces of processing by using the AI technology on the input non-AI image, and outputs the AI image  51  to which the AI technology is applied. For example, the plurality of slice images output by the tomography apparatus, such as the CT apparatus and the Mill apparatus, are input to the image processing unit  5  as the non-AI images. The image processing unit  5  performs virtual generation processing on the input non-AI image  50 , that is, the plurality of slice images, virtually generates the AI image  51  which is a slice image having a slice thickness t 2  thinner than a slice thickness tl of the input slice image, and outputs the generated AI image  51 . 
     Here, the virtual generation processing will be described. In the present embodiment, in the virtual generation processing, a first discriminator which is machine-learned by using learning information including a plurality of data sets of a pair of the plurality of slice images (hereinafter also referred to as a first image group Pt 1 ) having the slice thickness t 1  and the plurality of slice images (second image group Pt 2 ) having the slice thickness t 2 , which are actually captured by the tomography apparatus, such as the CT apparatus and MRI apparatus is used. The first discriminator is learned such that the second image group Pt 2  is output in a case in which the first image group Pt 1  is input. By using the first discriminator learned in this way, the image processing unit  5  can virtually generate the second image group Pt 2  (AI image  51 ) having the slice thickness t 2  from the first image group Pt 1  (non-AI image  50 ) having the slice thickness t 1 . 
     In addition, for example, one of a plurality of CT tomographic images Pct output by the CT apparatus is input to the image processing unit  5  as the non-AI image  50 . The image processing unit  5  performs image conversion processing on the input non-AI image  50 , that is, the CT tomographic image Pct, and converts the CT tomographic image Pct into a virtual MR tomographic image Pdmr just like an MR tomographic image Pmr captured by the MRI apparatus. 
     In the present embodiment, in the image conversion processing, a second discriminator which is machine-learned by using learning information including a plurality of data sets of a pair of the CT tomographic image Pct output by the CT apparatus and the MR tomographic image Pmr output by the MRI apparatus is used. The second discriminator is learned to output the MR tomographic image Pmr in a case in which the CT tomographic image Pct is input. By using the second discriminator learned in this way, the image processing unit  5  can convert the CT tomographic image Pct (non-AI image  50 ) into the virtual MR tomographic image Pdmr (AI image  51 ). 
     As described above, the image processing executed by the image processing unit  5  includes image processing of newly generating the AI image  51 , which is the medical image separately from the original non-AI image  50 , by applying the AI technology to the non-AI image  50 . 
     In addition, the AI image  51  includes the following medical images, as well as the image processing of generating the new AI image  51  based on the non-AI image  50 . For example, a breast image Pm acquired by simple imaging performed by a mammography apparatus, which is an example of the radiography apparatus, is input to the image processing unit  5  as the non-AI image  50 . The image processing unit  5  analyzes the input non-AI image  50 , that is, the breast image Pm by CAD, extracts a size, a position, a volume, and the like of a region of interest, such as a lesion, included in the breast image Pm, and acquires the extracted results as the analysis results. The AI technology using a machine learning model, such as a neural network, is applied to the CAD analysis processing in the present embodiment. The image processing unit  5  generates a marked breast image Pmc having a frame surrounding the region of interest on the breast image Pm based on the analysis results generated by the CAD analysis processing. 
     In this way, the image processing unit  5  analyzes the non-AI image  50  by the AI technology, and generates the image obtained by adding the analysis results obtained by the CAD analysis processing to the non-AI image  50 , which is the analysis target, as the AI image  51 . 
     Summarizing the above description, in the technology of the present disclosure, the medical image newly generated separately from the original non-AI image  50  by applying the AI technology to the non-AI image  50  is also the AI image  51 , and the medical image obtained by adding the image analysis results obtained by performing the image analysis by using the AI technology based on the non-AI image  50  to the non-AI image  50 , which is the analysis target, is also the AI image  51 . Note that in the present disclosure, the image generated by performing the analysis on the AI image  51  newly generated separately from the original non-AI image  50 , and adding the analysis results obtained by the analysis to the AI image  51 , which is the analysis target, is also the AI image  51 . 
     The image storage server  3  is a computer that stores and manages various data, and comprises a large capacity external storage device and software for database management. The image storage server  3  performs communication with other devices via the wired or wireless network  4  to transmit and receive image data. Specifically, the image storage server  3  acquires various data including the image data of an examination image generated by the image capturing apparatus  2  via the network, and stores and manages the data in a recording medium, such as the large capacity external storage device. Note that a storage format of the image data and the communication between the devices via the network  4  are based on a protocol, such as digital imaging and communication in medicine (DICOM). 
     In the present embodiment, the image storage server  3  stores the examination image for each patient. As the examination image stored for each patient, for example, there are a plurality of the examination images acquired by a plurality of examinations performed on the same patient. These examination images are stored for each examination. In addition, even in one examination for the same patient, there are usually the plurality of examination images. As the plurality of examination images acquired in one examination, for example, in a case of the breast examination, there are the examination images having different imaging conditions, such as an MLO image obtained by MLO imaging and a CC image obtained by CC imaging. In addition, the same type of examination may be performed a plurality of times on different examination dates, such as follow-up. The plurality of examinations having different examination dates are treated as different examinations, for example, and the plurality of examination images having different examination dates are stored for each examination date. In this way, the image storage server  3  stores the latest (current) examination images and the past examination images for the same type of examination, in addition to the different types of examination images performed on the same patient. 
     In the present embodiment, the examination image immediately after being acquired by the examination will be described as the non-AI image  50  to which the AI technology is not applied. In addition, the image storage server  3  also stores, in addition to the examination image which is the non-AI image  50 , the AI image  51  generated by the image processing unit  5  performing the various pieces of processing described above on the examination image. That is, the non-AI image  50  and the AI image  51 , which are examples of the medical image, are stored in the image storage server  3 . 
     In addition, each medical image includes accessory information, such as a DICOM tag, in addition to an image main body. Examples of the accessory information include information, such as an image identification (ID) for identifying an individual image, a patient ID for identifying the subject, an examination ID for identifying the examination, the examination date when the examination image, which is the original image before the AI technology is applied, is generated, an examination time point, a type of the image capturing apparatus  2  used in the examination to acquire the examination image, patient information, such as the patient name, the age, and the gender, examination site (imaging site), and an imaging condition (whether or not a contrast medium is used or the radiation dose). In addition, the accessory information also includes information, such as a CAD result in a case in which the CAD processing is performed. 
     In the present embodiment, it is premised that the accessory information included in the AI image  51  includes identification information indicating that the AI image is the AI image.  FIG. 3  is a diagram for describing the AI image. As shown in  FIG. 3 , the AI image  51  is configured by an AI image main body  51   a  and accessory information  51   b.  As an example, the accessory information  51   b  includes information, such as “Hanako Yamada” as the patient name, “female” as the gender, “ 25  years old” as the age, “it is the AI image” as whether or not it is the AI image, and “conversion of the CT image” as an image processing method. The information described as “it is the AI image” in  FIG. 3  is the identification information indicating that it is the AI image. The identification information of the AI image may be text information, but is actually recorded in the form of, for example, a flag or a code. 
     Next, a configuration of the image diagnosis support device  1  will be described.  FIG. 4  is a block diagram showing the configuration of the image diagnosis support device  1  according to the embodiment of the present disclosure, and  FIG. 5  is a functional block diagram of the image diagnosis support device  1  according to the first embodiment. 
     The image diagnosis support device  1  is configured by a computer comprising a central processing unit (CPU)  11 , a primary storage unit  12 , a secondary storage unit  13 , an external interface (I/F)  14 , and the like. The CPU  11  controls the whole image diagnosis support device  1 . The primary storage unit  12  is a volatile memory used as a work area or the like when various programs are executed. Examples of the primary storage unit  12  include a random access memory (RAM). The secondary storage unit  13  is a non-volatile memory in which various programs, various parameters, and the like are stored in advance, and one embodiment of an operation program  15  of the image diagnosis support device  1  of the present disclosure is installed. Examples of the secondary storage unit  13  include a hard disk drive, a solid state drive, and a flash memory. 
     The operation program  15  is distributed in a state of being recorded on a storage medium, such as a digital versatile disc (DVD) or a compact disc read only memory (CD-ROM), and is installed in the computer from the storage medium. Alternatively, the operation program  15  may be stored in a storage device or a network storage of a server computer connected to the network in a state of being accessible from the outside, downloaded to the computer in response to an external request, and then installed. 
     By executing the operation program  15  by the CPU  11 , the CPU  11  functions as an image acquisition unit  21 , a determination unit  22 , a notification unit  23 , and a display control unit  24  shown in  FIG. 5 . 
     The external I/F  14  controls the transmission and reception of various pieces of information between the image diagnosis support device  1  and the image storage server  3 . The CPU  11 , the primary storage unit  12 , the secondary storage unit  13 , and the external I/F  14  are connected to a bus line  16 , which is a common route for exchanging data. 
     In addition, a display unit  30  and an input unit  40  are also connected to the bus line  16 . The display unit  30  is configured by, for example, a liquid crystal display. As will be described below, the display unit  30  displays a display screen (see reference numeral  31  in  FIG. 6 ) on which various regions including an image display region are displayed. Note that the display unit  30  may be configured by a touch panel and may also be used as the input unit  40 . The input unit  40  comprises a mouse, a keyboard, and the like, and inputs various settings by the user. The input unit  40  according to the present embodiment functions as a mouse for inputting a selection operation of the medical image to be displayed on a display screen  31  and a mouse for inputting various operations on the medical image displayed on the display screen. 
     The image acquisition unit  21  acquires the medical image from the image storage server  3  via the external I/F  14 . The image acquisition unit  21  acquires the medical image selected by the user by operating the input unit  40 . In the present embodiment, for example, as shown in  FIG. 5 , the image acquisition unit  21  acquires the examination images acquired by the image capturing apparatus  2 , which are the non-AI image  50  to which the AI technology is not applied and the AI image  51  obtained by applying the AI technology to the non-AI image  50 . The medical image acquired by the image acquisition unit  21  is displayed on the display screen  31  of the display unit  30 . 
     In the following, the functions of the image diagnosis support device  1  will be described based on the functional blocks shown in  FIG. 5  and a display screen example shown in  FIG. 6 .  FIG. 6  is a diagram showing an example of the display of the display screen  31  of the display unit  30  according to the present embodiment. The display screen  31  is an example of a graphical user interface (GUI) that functions as an operation screen on which the examination image and various operation units are displayed. 
     As shown in  FIG. 6 , a thumbnail image display region  34   a  in which a thumbnail image obtained by reducing the medical image is displayed is provided in the upper right of the display screen  31 . In addition, in the upper left of the display screen  31 , although shown briefly, a selection region  34   b  is provided in which a patient list on which the patient ID is displayed and an examination list of examinations performed on each patient are displayed in a selectable manner. In addition, below the thumbnail image display region  34   a  and the selection region  34   b,  an image display region  34   c  on which the medical image is displayed is provided. 
     For example, in a case in which the user selects the patient ID of the patient to be interpreted from the patient list, the examination list of the selected patient is displayed. In a case in which the user selects the examination including the examination image to be displayed from the displayed examination list, the examination image acquired by the selected examination, that is, the thumbnail image of the non-AI image  50  is displayed in the thumbnail image display region  34   a.  In addition, in the examination selected by the user, various pieces of processing are performed on the examination image by the image processing unit  5 , and in a case in which the AI image  51  to which the AI technology is applied is present, the thumbnail image of the AI image  51  is also displayed in the thumbnail image display region  34   a.  That is, in the thumbnail image display region  34   a,  the thumbnail image of the medical image including at least one of the non-AI image  50  or the AI image  51  is displayed. 
     In a case in which the user selects the thumbnail image corresponding to the medical image to be interpreted from a plurality of the thumbnail images displayed in the thumbnail image display region  34   a,  the image acquisition unit  21  acquires the medical image corresponding to the selected thumbnail image as the medical image selected by the user. 
     The determination unit  22  determines whether the medical image acquired by the image acquisition unit  21  is the non-AI image  50  or the AI image  51 . As a determination method, as described above, a determination is made based on each medical image, that is, the accessory information  50   b  and  51   b  included in the non-AI image  50  and the AI image  51 , respectively. Specifically, the determination is made based on the information on whether or not the AI image, which is included in the accessory information  50   b  and  51   b.  The determination unit  22  determines that the medical image is the AI image  51  in a case in which information on “it is the AI image” is included in the accessory information  50   b  and  51   b.    
     In a case in which the determination unit  22  determines that the medical image acquired by the image acquisition unit  21  is the AI image  51 , the notification unit  23  gives a notification that the medical image displayed on the display unit is the AI image  51  in a case in which the AI image  51  is displayed on the display screen  31  of the display unit  30 . Specifically, as a flag indicating that the AI technology is applied to the AI image  51  to be displayed, in  FIG. 6 , as an example, an AI flag  52  indicated by text information, such as “AI image”, is displayed on the display control unit  24 . 
     The display control unit  24  displays the medical image acquired by the image acquisition unit  21  on the display screen  31 . In addition, in the present embodiment, in a case in which the display control unit  24  further displays the AI image  51  on the display screen  31  based on the instruction from the notification unit  23 , the AI flag  52  is displayed on the AI image  51  as shown in  FIG. 6 . 
     Then, processing performed in the present embodiment will be described.  FIG. 7  is a flowchart showing the processing performed in the first embodiment of the present disclosure. 
     First, the image acquisition unit  21  acquires the medical image (step ST 1 ). Specifically, as described above, the user selects the patient name to be interpreted from the patient list by using the input unit  40 , and selects a desired examination from the examination list of the selected patient. As a result, the thumbnail image of the medical image acquired by the selected examination is displayed in the thumbnail image display region  34   a.  In the present embodiment, as an example, the thumbnail image includes the thumbnail images of the non-AI image  50  and the AI image  51 . 
     In a case in which the user selects the thumbnail image to be displayed from the plurality of thumbnail images displayed in the thumbnail image display region  34   a,  the image acquisition unit  21  searches for the medical image corresponding to the selected thumbnail image in the image storage server  3 , and acquires the searched medical image. In the present embodiment, an example will be described in which the thumbnail image of the AI image  51  is selected as the thumbnail image selected by the user. The image acquisition unit  21  acquires the AI image corresponding to the selected thumbnail image as the medical image. 
     Then, the determination unit  22  determines whether or not the medical image acquired by the image acquisition unit  21  is the AI image  51  (step ST 2 ). Specifically, the determination unit  22  examines the accessory information (see  FIG. 3 ) added to the medical image, and determines whether or not the medical image is the AI image  51 . 
     In a case in which step ST 2  is denied (step ST 2 : NO), since the acquired medical image is not the AI image  51 , that is, the non-AI image  50 , the display control unit  24  displays the acquired medical image, that is, the non-AI image  50  on the display screen  31  (step ST 3 ), and the CPU  11  terminates the processing. 
     On the other hand, in a case in which step ST 2  is affirmed (step ST 2 : YES), the display control unit  24  displays the acquired medical image, that is, the AI image  51  on the display screen  31  (step ST 4 ). Then, the notification unit  23  causes the display control unit  24  to display the AI flag  52  (see  FIG. 6 ) indicating that the AI technology is applied to the displayed AI image  51  (step ST 5 ), and the CPU  11  terminates the processing. Note that, in the present embodiment, displaying the AI flag  52  is an example of giving the notification that it is the AI image. 
     In the field of the image diagnosis, information more useful for the image diagnosis may be able to be obtained by applying the image analysis technology by using the AI technology, the image generation technology, and the like by using the AI technology to the medical image captured by the image capturing apparatus  2 . In a case in which the AI image  51  is used, the information useful for diagnosis is obtained, the number of situations in which the AI image  51  is used has been increased in the medical field in which the image diagnosis is made. As the medical image used for the final definitive diagnosis of the patient, the AI image  51  and the non-AI image  50  are mixed. On the other hand, since the AI technology has insufficient accumulation of reliability at least at a current stage as compared to a determination of a doctor, it is currently unacceptable to rely on the AI image for all diagnosis evidence. Under these circumstances, it is important to clearly distinguish whether the medical image used as the evidence of diagnosis is the AI image or the non-AI image. 
     In the present embodiment, in a case in which the AI image  51  is displayed as the medical image on the display screen  31 , the notification that the displayed medical image is the AI image  51  is given. As a result, even in a case in which it is difficult to distinguish between the AI image  51  and the non-AI image  50  only by looking at the image, it is possible to easily distinguish whether or not the medical image displayed on the display screen  31  of the display unit  30  is the AI image. 
     Note that, in the first embodiment, in the flowchart of  FIG. 7 , the display control unit  24  displays the AI image  51  on the display screen  31  (step ST 4 ), and then the notification unit  23  gives the notification of the AI flag  52  (step ST 5 ), but the technology of the present disclosure is not limited to this. For example, the notification unit  23  may first give the notification of the AI flag  52  (step ST 5 ), and then the display control unit  24  may display the AI image  51 . 
     In addition, in the first embodiment, as shown in  FIG. 6 , the notification unit  23  displays the text information “AI image” as the AI flag  52  on the upper left of the AI image  51 , but the technology of the present disclosure is not limited to this. A display position of the AI flag  52  may be any position in the AI image  51 . In addition, the AI flag  52  may be displayed around the AI image  51  instead of in the AI image  51 . In addition, the display position of the AI flag  52  does not have to be around the AI image  51  as long as a correspondence between the AI image  51  and the AI flag  52  can be understood. For example, even in a case in which the AI image  51  and the AI flag  52  are positioned at positions separated from each other on the display screen  31 , the correspondence is shown by connecting the AI image  51  and the AI flag  52  with a leader line or the like. In addition, the AI flag  52  may be displayed at a position separated from the AI image  51 , and both an outer frame of the AI image  51  and the AI flag  52  may be turned on and off at the same timing. Also in this method, it is possible to indicate the correspondence between the AI image  51  and the AI flag  52 . 
     In addition, the text information is used as the AI flag  52 , a noun, such as the “AI image”, may be used, or a sentence, such as “this image is the AI image” may be used. In this way, any text information may be used as long as it can transmit that it is the AI image  51 . In addition, the AI flag  52  does not have to be the text, but may be a figure, a symbol, a pattern, or the like recognized as a flag indicating the AI. In addition, the means of notification is not limited to display. For example, a voice “this image is the AI image” may be output. 
     In addition, in the first embodiment, the determination unit  22  searches for the accessory information in a case in which it is determined whether or not the medical image acquired by the image acquisition unit  21 , that is, the medical image to be displayed is the AI image, but the technology of the present disclosure is not limited to this. For example, in a case in which the determination unit  22  can determine whether or not the AI technology is applied by performing the image analysis on the medical image, it may be determined whether or not the medical image is the AI image by the image analysis. In addition, in a case in which the analysis results are added to the medical image, in a case in which it is possible to determine whether or not the AI technology is applied by examining the added analysis results by the determination unit  22 , it may be determined whether or not the medical image is the AI image by the image analysis. 
     In addition, in the first embodiment, as shown in  FIG. 6 , the example has been described in which the medical image displayed on the display screen  31  is one, but the technology of the present disclosure is not limited to this, and a plurality of medical images may be displayed on the display screen  31 . In a case in which the plurality of medical images are displayed, for example, the display screen  31  is divided into a plurality of regions based on the number of medical images acquired by the image acquisition unit  21 , and the acquired medical images are displayed on the divided regions. In a case in which the AI image  51  and the non-AI image  50  are mixed in the acquired medical image, that is, the medical image to be displayed, the AI flag  52  is displayed only on the AI image  51  (see  FIG. 10 ). Note that a method of division (size, number, shape, and the like of each region) on the display screen  31  can be optionally set by the user. 
     Next, a second embodiment of the present disclosure will be described.  FIG. 8  is a functional block diagram of an image diagnosis support device  120  according to the second embodiment. In the image diagnosis support device  120  according to the second embodiment shown in  FIG. 8 , the CPU  11  of the image diagnosis support device  1  according to the first embodiment shown in  FIG. 5  further has the functions of a browsing detection unit  25  and a recording control unit  26 . 
     As shown in  FIG. 8 , the image diagnosis support device  120  according to the second embodiment comprises the browsing detection unit  25  and the recording control unit  26 . The browsing detection unit  25  detects whether or not the user browses the AI image  51 .  FIG. 9  is a diagram showing an example of display (AI image non-display) on the display screen of the display unit according to the second embodiment, and  FIG. 10  is a diagram showing an example of display (AI image display) of the display screen of the display unit according to the second embodiment. 
     In the present embodiment, as shown in  FIG. 9 , the display control unit  24  divides the display screen  31  into regions of three columns and two rows, and displays six medical images acquired by the image acquisition unit  21  in the divided regions. In the present embodiment, for example, the determination unit  22  determines that two of the six medical images are the AI images  51 . In this case, in a case in which the two medical images determined as the AI image  51  are displayed on the display screen  31 , the display control unit  24  displays the subject in the AI image  51  in an invisible manner, and displays the AI flag  52  in a visible manner. That is, the display control unit  24  does not display the AI image  51  while displaying the AI flag  52 . Specifically, as shown in  FIG. 10 , the display control unit  24  does not display an image content in the display region of the AI image  51  by using hatching or the like, and displays the AI flag  52  on the display region. Note that the same processing is performed to the thumbnail image corresponding to the AI image  51 . 
     The display control unit  24  displays the AI image  51  in a visible manner in a case in which the AI flag  52  of the AI image  51 , which is not displayed, is clicked by the user operating the input unit (mouse)  40 . Then, after the AI image  51  is displayed, the display control unit  24  displays the AI flag  52  on the displayed AI image  51  as shown in  FIG. 10 . 
     In the present embodiment, the browsing detection unit  25  detects that the AI image  51  is browsed in a case in which the AI flag  52  is clicked in a state in which the AI image  51  shown in  FIG. 9  is not displayed. Note that the click operation of the AI flag  52  by the user corresponds to the input of a display instruction for displaying the AI image  51 , which is not displayed, according to the present disclosure on the display screen  31 . 
     As shown in  FIG. 8 , the recording control unit  26  stores a browsing history  71  indicating that the AI image  51  is browsed in the secondary storage unit  13  based on the detection result of the browsing detection unit  25 . Specifically, the recording control unit  26  records the browsing history  71  in the secondary storage unit  13  in association with the browsed AI image  51 , that is, the image ID of the AI image  51  for which the display instruction is given. 
     In the present embodiment, the browsing detection unit  25  detects that the user browses the AI image  51  in a case in which the display instruction for displaying the AI image  51 , which is not displayed, on the display screen  31  of the display unit  30  is input (AI flag  52  is clicked). Further, the recording control unit  26  performs a control of recording the browsing history  71  based on the detection result of the browsing detection unit  25 , that is, in a case in which the AI flag  52  is clicked. As a result, it is possible to leave the evidence that the doctor looks at the AI image. 
     Note that, in the second embodiment, the click operation has been described as an example of inputting the display instruction for displaying the AI image  51 , which is not displayed, on the display screen  31 , but the technology of the present disclosure is not limited to this. For example, in a case in which the display unit  30  is configured by the touch panel, the user may tap the region of the AI image  51 , which is not displayed, or the AI flag  52 . 
     In addition, in the second embodiment, the browsing detection unit  25  detects that the AI image  51  is browsed when the AI flag  52  is clicked, but the technology of the present disclosure is not limited to this. For example, in a case in which the display control unit  24  displays the AI image  51  (see  FIG. 9 ), which is not displayed, on the display screen  31  of the display unit  30  (see  FIG. 10 ), the browsing detection unit  25  may detect that the AI image  51  is browsed. In this case, in  FIG. 8 , the browsing detection unit  25  does not need to input from the input unit  40 , and detects that the AI image  51  is browsed based on the input from the display control unit  24  surrounded by an alternate long and short dash line. That is, the trigger for displaying the AI image  51 , which is not displayed, is not necessarily limited to the display instruction from the input unit  40 . In a case in which the display control unit  24  performs a display control of the display screen  31 , the AI image  51  may be displayed regardless of the operation instruction of the user. In that case, the display control unit  24  transmits, to the browsing detection unit  25 , the fact that the processing of displaying the AI image  51 , which is not displayed, is executed. As a result, the browsing detection unit  25  detects that the AI image  51  is browsed. 
     Next, a third embodiment of the present disclosure will be described.  FIG. 11  is a functional block diagram of an image diagnosis support device  130  according to the third embodiment. In the image diagnosis support device  130  according to the third embodiment shown in  FIG. 11 , the CPU  11  of the image diagnosis support device  1  according to the first embodiment shown in  FIG. 5  further has the functions of the browsing detection unit  25 , the recording control unit  26 , and a gaze detection unit  27 . Note that the functions of the browsing detection unit  25  and the recording control unit  26  are the same as those in the second embodiment, and thus the description thereof is omitted here. 
     In the second embodiment, the browsing detection unit  25  detects that the AI image  51  is browsed in a case in which the AI flag  52  is displayed, but in the present embodiment, it is detected that the AI image  51  is browsed in a case in which the gaze detection unit  27  detects that the gaze of the user is directed to the AI image  51  displayed on the display screen  31  of the display unit  30 .  FIG. 12  is a diagram for describing the gaze detection unit  27 . 
     As shown in  FIG. 12 , the gaze detection unit  27  acquires a face image of a face of the user captured by a camera C provided on an upper part of the display unit  30 . The gaze detection unit  27  analyzes the acquired face image and detects the movement of a pupil E of the user to detect whether or not the gaze of the user is directed to the AI image  51  displayed on the display screen  31 . Note that for the detection of the gaze, commonly used known technology can be used. The browsing detection unit  25  detects that the AI image  51  is browsed, for example, in a case in which the gaze of the user is directed to the AI image  51  for a predetermined time or longer. The browsing history  71  is recorded in the secondary storage unit  13  by the recording control unit  26  in the same manner as in the second embodiment. 
     In the third embodiment, it is possible to easily detect whether or not the user browses the AI image  51  by detecting the gaze of the user without any input operation by the user. 
     Next, a fourth embodiment of the present disclosure will be described.  FIG. 13  is a functional block diagram of an image diagnosis support device  140  according to the fourth embodiment. In the image diagnosis support device  140  according to the fourth embodiment shown in  FIG. 13 , the CPU  11  of the image diagnosis support device  130  according to the third embodiment shown in  FIG. 11  further has a function of a warning unit  28 . Note that the function of the gaze detection unit  27  is the same as that of the third embodiment, the description thereof is omitted here. 
     In the fourth embodiment, the recording control unit  26  performs a control of recording a use history  72  indicating that the AI image  51  has been used for the image diagnosis based on the operation of the user, in addition to the browsing history  71 . First, in order to describe the operation of the user, the configuration of the display screen  31  of the display unit  30  according to the present embodiment will be described.  FIG. 14  is a diagram showing an example of the display screen of the display unit according to the fourth embodiment, and  FIG. 15  is a diagram showing an example of display on the second display screen of the display unit according to the fourth embodiment. 
     In the fourth embodiment, as shown in  FIG. 14 , the display unit  30  includes a first display screen  31 A and a second display screen  31 B. The display control unit  24  displays an interpretation report  32  in which the contents of the image diagnosis are recorded on the first display screen  31 A, and displays the medical image on the second display screen  31 B. The second display screen  31 B functions as an image viewer on which the medical image is displayed. In the present embodiment, the display control unit  24  displays the contents displayed on the display screen  31  shown in  FIG. 15  on the second display screen  31 B. 
     On the second display screen  31 B, a check box  60   a  is displayed below the image display region  34   c,  as shown in  FIG. 15 . The check box  60   a  is a use history input tool for inputting the use history  72  in a case in which the user uses the AI image  51  for the image diagnosis. Next to the check box  60   a,  text information  60 , such as “AI image has been used for image diagnosis” is displayed to indicate the meaning of the check box  60   a.    
     In the fourth embodiment, as shown in  FIG. 13 , in a case in which the user operates the mouse (input unit  40 ) to check the check box  60   a,  the recording control unit  26  stores the use history  72  indicating that the AI image  51  has been used for the image diagnosis in the secondary storage unit  13 . Specifically, in the use history  72 , the recording control unit  26  records the use history  72  in the secondary storage unit  13  in association with the image ID of the browsed AI image  51 , that is, the AI image  51  displayed on the second display screen  31 B. 
     In a case in which the interpretation report  32  related to the image diagnosis is created in a state in which the use history  72  is not present even though the browsing history  71  is present, the warning unit  28  issues a warning that the use history  72  is not present at least before creation of the interpretation report  32  is terminated. For example, the warning unit  28  causes the display control unit  24  to display warning information, such as “the use history of the AI image is not present”, on the first display screen  31 A. 
     Then, processing performed in the present embodiment will be described.  FIGS. 16  and  17  are flowcharts showing the processing performed in the fourth embodiment of the present disclosure. 
     First, the image acquisition unit  21  acquires the medical image in the same manner as in the first embodiment (step ST 21 ). Then, the determination unit  22  determines whether or not the medical image acquired by the image acquisition unit  21  is the AI image  51  in the same manner as in the first embodiment (step ST 22 ). 
     In a case in which step ST 22  is denied (step ST 22 : NO), since the acquired medical image is not the AI image  51 , that is, the non-AI image  50 , the display control unit  24  displays the acquired medical image, that is, the non-AI image  50  on the second display screen  31 B (step ST 23 ), and the CPU  11  shifts the processing to B of  FIG. 17  and terminates a series of the processing. 
     On the other hand, in a case in which step ST 22  is affirmed (step ST 22 : YES), the display control unit  24  does not display the image contents while showing the presence of the acquired medical image, that is, the AI image  51  on the second display screen  31 B (step ST 24 ). 
     Then, the notification unit  23  causes the display control unit  24  to display the AI flag  52  (see reference numeral  52  in  FIG. 9 ) indicating that the AI technology is applied to the AI image  51 , which is not displayed (step ST 25 ). Note that, in the present embodiment, displaying the AI flag  52  is an example of giving the notification that it is the AI image. 
     Then, the CPU  11  determines whether or not the AI flag  52  is clicked (step ST 26 ). In a case in which step ST 26  is denied (step ST 26 : NO), the CPU  11  shifts the processing to step ST 24  and performs the subsequent processing. On the other hand, in a case in which step ST 256  is affirmed (step ST 26 : YES), as shown in  FIG. 16 , the display control unit  24  displays the image contents of the AI image  51  (step ST 27 ), and further displays the check box  60   a  (use history input tool) which is the use history input tool on the second display screen  31 B (step ST 28 ). 
     Then, the browsing detection unit  25  detects that the AI image  51  is browsed (step ST 29 ). Then, as shown in  FIG. 17 , the recording control unit  26  records the browsing history  71  indicating that the AI image  51  is browsed in the secondary storage unit  13  based on the detection result of the browsing detection unit  25  (step ST 30 ). 
     Then, the recording control unit  26  determines whether or not the check box  60   a  is checked (step ST 31 ). In a case in which step ST 31  is denied (step ST 31 : NO), the CPU  11  shifts the processing to step ST 33 . On the other hand, in a case in which step ST 31  is affirmed (step ST 31 : YES), the recording control unit  26  records the use history  72  indicating that the AI image  51  has been used for the image diagnosis in the secondary storage unit  13  (step ST 32 ). 
     Then, the CPU  11  determines whether or not the creation of the interpretation report  32  is terminated (step ST 33 ). In a case in which step ST 33  is denied (step ST 33 : NO), the CPU  11  repeatedly performs the processing of step ST 33 . On the other hand, in a case in which step ST 33  is affirmed (step ST 33 : YES), the warning unit  28  determines whether or not the use history  72  is stored in the secondary storage unit  13  (step ST 34 ). Note that, in the present embodiment, it is determined that the creation of the interpretation report  32  is terminated in a case in which the operation of closing the interpretation report  32  shown in  FIG. 14  is performed. Specifically, the CPU  11  determines that the interpretation report  32  in closed in a case in which a cross mark (not shown) displayed on the upper right of the interpretation report  32  is clicked. 
     In a case in which step ST 34  is affirmed (step ST 34 : YES), the CPU  11  terminates a series of the processing. On the other hand, in a case in which step ST 34  is denied (step ST 34 : NO), the warning unit  28  issues the warning that the use history  72  is not present before the creation of the interpretation report  32  is terminated (step ST 35 ), and the CPU  11  returns to processing of step ST 31 . 
     In the fourth embodiment, it is possible to prevent the creation of the interpretation report  32  related to the image diagnosis from being terminated in a state in which the use history  72  is not present even though the browsing history  71  is present. 
     Note that, in the fourth embodiment, the warning unit  28  displays the warning information, such as “the use history of the AI image is not present”, on the first display screen  31 A, but the technology of the present disclosure is not limited to this. The warning unit  28  may display the warning information on the second display screen  31 B instead of the first display screen  31 A. In addition, the warning unit  28  may output the warning information by the voice. 
     In addition, in the fourth embodiment, in a case in which the creation of the interpretation report  32  is to be terminated in a state in which the use history  72  is not present even though the browsing history  71  is present, the warning unit  28  issues the warning that the use history  72  is not present, but the technology of the present disclosure is not limited to this. For example, the CPU  11  may perform processing in which the interpretation report  32  cannot be closed to prevent the creation of the interpretation report  32  from being terminated. 
     In addition, in the fourth embodiment, the first display screen  31 A and the second display screen  31 B are provided on the same display unit  30 , but the technology of the present disclosure is not limited to this. In a case in which there are two display units  30 , the display units  30  can display the first display screen  31 A and the second display screen  31 B, respectively. 
     In addition, in the embodiments described above, for example, as hardware structures of processing units that execute various pieces of processing, such as the image acquisition unit  21 , the determination unit  22 , the notification unit  23 , the display control unit  24 , the browsing detection unit  25 , the recording control unit  26 , the gaze detection unit  27 , and the warning unit  28 , it is possible to use various processors described below. As described above, various processors include, in addition to the CPU, which is a general-purpose processor that executes software (program) and functions as various processing units, a programmable logic device (PLD), which is a processor of which a circuit configuration can be changed after manufacture, such as a field programmable gate array (FPGA), and a dedicated electric circuit, which is a processor having a circuit configuration designed for exclusive use in order to execute specific processing, such as an application specific integrated circuit (ASIC). 
     One processing unit may be configured by one of various processors, or may be a combination of two or more processors of the same type or different types (for example, a combination of a plurality of FPGAs or a combination of the CPU and the FPGA). In addition, a plurality of the processing units may be configured by one processor. 
     As an example of configuring the plurality of processing units by one processor, first, as represented by a computer, such as a client and a server, there is a form in which one processor is configured by a combination of one or more CPUs and software and this processor functions as the plurality of processing units. Second, as represented by a system on chip (SoC) or the like, there is a form of using a processor that realizes the function of the whole system including the plurality of processing units with one integrated circuit (IC) chip. In this way, as the hardware structure, the various processing units are configured by using one or more of the various processors described above. 
     Further, as the hardware structure of these various processors, more specifically, it is possible to use an electrical circuit (circuitry) in which circuit elements such as semiconductor elements are combined.