Cognitive dysfunction diagnosis device and cognitive dysfunction diagnosis program recording medium

Abstract

A cognitive dysfunction diagnosis device (1) includes: an acquisition unit (41) that acquires a distribution map that shows a distribution of a viewpoint of a subject with respect to a diagnosis image; and a diagnosis unit (39) that diagnoses the diagnosis image that includes a first image and an evaluation image in chronological order, the first image has a first region that includes a memorization image that is visually highlighted and a plurality of second regions that include a general image that is not highlighted, the evaluation image has a third region and the plurality of second regions, the third region includes an evaluation image, the evaluation image is an image that is similar to the memorization image and is displayed without visual highlighting at the same position as the memorization image, the diagnosis unit (39) calculates a fixation rate of the third region in the distribution map with respect to the evaluation image, and in a case where the fixation rate is lower than a threshold value, it is diagnosed that there is a suspicion of memory impairment.

Description

TECHNICAL FIELD

[0001] The present application relates to a cognitive dysfunction diagnosis device for diagnosing cognitive dysfunction and a cognitive dysfunction diagnosis program recording medium. BACKGROUND

[0002] In the past, there has been a technology related to a device for diagnosing cognitive dysfunction.

[0003] In Patent Literature 1 and Patent Literature 2, a medical diagnosis device using gaze detection is proposed, the gaze detection being detection of the movement of the head and the gaze of the subject, and being capable of objectively diagnosing a disease related to brain function.

[0004] In Patent Literature 3, a system for detecting the eye movement of a subject for the purpose of diagnosing neurological disorders is proposed.

[0005] In Patent Literature 4 and Patent Literature 5, a visual chart for examining optic nerve disorders is proposed, the optic nerve disorders occurring when a subject has eye diseases such as retinal and optic nerve diseases or intracranial diseases.

[0006] In Patent Literature 6 to Patent Literature 9, an autism diagnosis support system for diagnosing autism of a subject using a gaze detection unit having at least a camera section is proposed.

[0007] In Patent Literature 10, a brain function disease diagnosis support device for detecting the gaze and the pupil of a subject and judging the likelihood of brain disease of the subject is proposed.

[0008] In Patent Literature 11, a diagnosis support device for effectively supporting the diagnosis of visual recognition disorders is proposed.

[0009] In Patent Literature 12, an eye movement measuring device and a measuring method capable of detecting the initial movement time of the eye movement even from a rough image are proposed.

[0010] In Patent Literature 13, a cognitive dysfunction diagnosis device having simplicity, low cost, objectivity, quantitativeness, and universality is proposed.

[0011] (Prior Art Documents)

[0012] (Patent Literature)

[0013] Patent Literature 1: Japanese Patent Application Laid-Open No. 6-70884

[0014] Patent Literature 2: Japanese Patent Application Laid-Open No. 6-70885

[0015] Patent Literature 3: Japanese Patent Application Laid-Open No. 2016-523112

[0016] Patent Literature 4: Japanese Patent No. 4560801

[0017] Patent Literature 5: Japanese Patent No. 4116354

[0018] Patent Literature 6: Japanese Patent No. 5926210

[0019] Patent Literature 7: Japanese Patent No. 5912351

[0020] Patent Literature 8: Japanese Patent No. 5761048

[0021] Patent Literature 9: Japanese Patent No. 5761049

[0022] Patent Literature 10: Japanese Patent No. 5817582

[0023] Patent Literature 11: Japanese Patent Application Publication No. 2017-158866

[0024] Patent Literature 12: Japanese Patent Application Publication No. 2017-176302

[0025] Patent Literature 13: International Publication No. 2019 / 098173 SUMMARY

[0026] PROBLEMS TO BE SOLVED BY THE INVENTION

[0027] Conventionally, in the diagnosis of cognitive dysfunction and dementia, a cognitive function test (neuropsychological test) in the form of an interview has been used. The cognitive function test is a method in which a tester asks a testee a plurality of questions for evaluating cognitive function, scores the content of the answers to the questions by speech and writing or drawing, and calculates a cognitive function score. In this method, there is a problem that the test takes time. For example, even the simplest Mini-Mental State Exam (MMSE) takes about 15 to 30 minutes. According to the nature of the interview form, there is also a problem that the testee is under a great psychological stress. Furthermore, since the ability of the tester who conducts the interview varies and the interpretation of the answers varies depending on the tester, there is a problem that the score greatly varies.

[0028] In order to solve these problems, the present inventors have proposed the cognitive dysfunction diagnosis device of Patent Literature 13. However, a new cognitive dysfunction diagnosis device is still desired.

[0029] The present application provides a cognitive dysfunction diagnosis device and a cognitive dysfunction diagnosis program recording medium that are simple, low-cost, objective, quantitative, and general-purpose, and that use a new diagnosis method.

[0030] Means for solving the problem

[0031] One aspect of the present application relates to a cognitive dysfunction diagnosis device including an acquisition unit that acquires a distribution map showing a distribution of a subject's gaze point with respect to a diagnosis image, and a diagnosis unit that diagnoses cognitive dysfunction based on the distribution map, the diagnosis image including, in chronological order, a first image having a first region including a memorized image that is visually emphasized and a plurality of second regions including a normal image that is not emphasized, and an evaluation image having a third region including an evaluation image that is an image similar to the memorized image and displayed without visual emphasis at the same position as the memorized image, and the diagnosis unit calculating a fixation rate of the third region in the distribution map with respect to the evaluation image, and diagnosing as having a memory disorder if the fixation rate is lower than a threshold value.

[0032] One aspect of the present application relates to a cognitive dysfunction diagnosis program recording medium that records a cognitive dysfunction diagnosis program causing a computer to execute a process of acquiring a distribution map showing a distribution of a subject's gaze point with respect to a diagnosis image, and diagnosing cognitive dysfunction based on the distribution map, the diagnosis image including, in chronological order, a first image having a first region including a memorized image that is visually emphasized and a plurality of second regions including a normal image that is not emphasized, and an evaluation image having a third region including an evaluation image that is an image similar to the memorized image and displayed without visual emphasis at the same position as the memorized image, and the cognitive dysfunction diagnosis program causing the computer to execute a process of calculating a fixation rate of the third region in the distribution map with respect to the evaluation image, and diagnosing as having a memory disorder if the fixation rate is lower than a threshold value.

[0033] Effects of the Invention

[0034] One aspect of the present application relates to a cognitive dysfunction diagnosis device and a cognitive dysfunction diagnosis program recording medium that are simple, low-cost, objective, quantitative, and general-purpose. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1A is a block diagram showing a configuration example of the cognitive dysfunction diagnosis device in the embodiment.

[0036] Figure 1B is a block diagram showing another configuration example of the cognitive dysfunction diagnosis device in the embodiment.

[0037] Figure 2A is a diagram showing an appearance example of the cognitive dysfunction diagnosis device in the embodiment.

[0038] Figure 2B is a diagram showing another appearance example of the cognitive dysfunction diagnosis device in the embodiment.

[0039] Figure 3 is a diagram showing an example of the storage contents of the storage section in the embodiment.

[0040] Figure 4 is a diagram showing an example of the case feature data in the embodiment.

[0041] Figure 5 is a flowchart showing a diagnosis processing example of the cognitive dysfunction diagnosis device in the embodiment.

[0042] Figure 6 is a flowchart showing an example of the first diagnosis processing in the embodiment.

[0043] Figure 7 is a diagram showing an example of the diagnosis image or the like in the first diagnosis processing of Figure 6

[0044] Figure 8 is a diagram showing an example of the distribution map obtained in the first diagnosis processing for a subject without memory disorder in the embodiment.

[0045] Figure 9 is a diagram showing an example of the distribution map obtained in the first diagnosis processing for a subject with memory disorder in the embodiment.

[0046] Figure 10 is a flowchart showing an example of the second diagnosis processing in the embodiment.

[0047] Figure 11 is a diagram showing an example of the diagnosis image using language indication in the second diagnosis processing of Figure 10

[0048] Figure 12 is a diagram showing another example of the diagnosis image using language indication in the second diagnosis processing of Figure 10

[0049] Figure 13 ​​​is a diagram showing an example of a distribution map obtained in the second diagnosis process using Figure 11 for a subject without aphasia.

[0050] Figure 14 is a diagram showing an example of a distribution map obtained in the second diagnosis process using Figure 11 for a subject with aphasia.

[0051] Figure 15 is a diagram showing a first example of a diagnostic image in which a language instruction is not used in the second diagnosis process of Figure 10 .

[0052] Figure 16 is a diagram showing an example of an evaluation image corresponding to Figure 15 .

[0053] Figure 17 is a diagram showing an example of a distribution map obtained in the second diagnosis process using Figure 15 for a subject without aphasia.

[0054] Figure 18 is a diagram showing an example of a distribution map obtained in the second diagnosis process using Figure 15 for a subject with aphasia.

[0055] Figure 19 is a diagram showing a second example of a diagnostic image in which a language instruction is not used in the second diagnosis process of Figure 10 .

[0056] Figure 20 is a diagram showing an example of an evaluation image corresponding to Figure 19 .

[0057] Figure 21 is a diagram showing an example of a distribution map obtained in the second diagnosis process using Figure 20 for a subject without aphasia.

[0058] Figure 22 is a diagram showing an example of a distribution map obtained in the second diagnosis process using Figure 20 for a subject with aphasia.

[0059] Figure 23 is a flowchart showing an example of the third diagnosis process in the embodiment.

[0060] Figure 24 is a diagram showing an example of a diagnostic image in the third diagnosis process of Figure 23 .

[0061] Figure 25 is a diagram showing an example of an evaluation image corresponding to Figure 23Fig. 3 is a diagram showing an example of an evaluation image of the 3rd diagnosis processing.

[0062] Figure 26 Fig. 4 is a diagram showing an example of a distribution map in the 3rd diagnosis processing for a subject without cognitive impairment. Figure 24

[0063] Figure 27 Fig. 5 is a diagram showing a correlation between a fixation rate of an instruction sentence area of the 3rd diagnosis processing and an MMSE score. Figure 25

[0064] Figure 28 Fig. 6 is a diagram showing a correlation between a fixation rate of an off-schedule incorrect answer image of the 3rd diagnosis processing and an MMSE score. Figure 25 DETAILED DESCRIPTION

[0065] Hereinafter, the embodiments will be specifically described with reference to the accompanying drawings.

[0066] In addition, the embodiments described below are examples showing generality or specificity of the present disclosure. The numerical values, shapes, materials, arrangement positions of constituent elements, connection modes, steps, order of steps, and the like shown in the following embodiments are one example, and the gist is not limited to the technical solutions. Also, the constituent elements of the following embodiments that are not described in the constituent elements of the most general concept of the independent technical solution are described as arbitrary constituent elements.

[0067] Further, each figure is a schematic diagram, and is not a strict illustration. Further, the same symbol is given to the same structural member in each figure.

[0068] (Embodiment)

[0069] Hereinafter, the cognitive impairment diagnosis device and the cognitive impairment diagnosis program in the embodiments will be described with reference to the accompanying drawings.

[0070] [1. Structure of cognitive impairment diagnosis device]

[0071] Figure 1A Fig. 1 is a block diagram showing an example of the structure of the cognitive impairment diagnosis device in the embodiments. Further, Figure 2A Fig. 2 is a diagram showing an example of the appearance of the cognitive impairment diagnosis device in the embodiments.

[0072] As shown in Fig. 1, the cognitive impairment diagnosis device 1 includes a test image creation unit 10, a test image display unit 20, a test image evaluation unit 30, a test image evaluation result output unit 40, a test image evaluation result storage unit 50, a test image evaluation result analysis unit 60, and a test image evaluation result analysis result output unit 70. Figure 1A ​​​The illustrated cognitive dysfunction diagnosis apparatus 1 is provided with a display section 10, a camera 20, and a PC (Personal Computer) 30. This cognitive dysfunction diagnosis apparatus 1 shows an example of a structure in which a general PC 30 sold on the market is used as a main control apparatus, and the display section 10 and the camera 20 are attached to the PC 30.

[0073] The display section 10 is a flat panel type display having a display surface 11, and displays a diagnosis image of cognitive dysfunction on the display surface 11. As shown in the figure, in order to display the diagnosis image to a subject, the display section 10 is a large liquid crystal display or an organic electroluminescence display that is easy to see for elderly people. In addition, the display section 10 can be a display for a personal computer, or a large television set sold on the market can be used as the display. Furthermore, the display section 10 can be constituted by a screen as the display surface 11 and a projector instead of the flat panel type display. Figure 2A

[0074] The camera 20 is a module that can be attached to the display section 10, and the camera 20 is provided with at least a camera section 21 for photographing the eyes of a subject and a light source section 24.

[0075] The camera section 21 is a stereo camera having a camera 22 and a camera 23. The cameras 22 and 23 can be infrared cameras, for example. In other examples, the cameras 22 and 23 can be visible light cameras. Furthermore, the camera section 21 can not be a stereo camera, and can be a single camera or three or more cameras.

[0076] The light source section 24 is provided with a light source 25 and a light source 26, and irradiates a subject with infrared light as illumination light. The light sources 25 and 26 can be constituted by one or a plurality of infrared LEDs (Light Emitting Diodes), for example. In other examples, the light sources 25 and 26 can be one or a plurality of white LEDs (Light Emitting Diodes). In addition, in the case where the illumination environment of a subject is sufficiently bright, the camera 20 can not be provided with the light source section 24. Furthermore, the camera 20 can be attached to the upper portion of the display section 10, or can be attached separately to the left and right.

[0077] The PC 30 is provided with a processor 31, a storage section 32, an input section 33, an output section 34, a display section 35, an interface section 36, a detection section 37, a production section 38, and a diagnosis section 39. In the PC 30, the processor 31 is connected to the storage section 32, the input section 33, the output section 34, the display section 35, the interface section 36, the detection section 37, the production section 38, and the diagnosis section 39. Figure 1A ​Among the illustrated functional blocks, the processor 31, the storage section 32, the input section 33, the output section 34, the display section 35, and the interface section 36 are constituted by general hardware and software of a computer sold on the market. The other functional blocks, in other words, the detection section 37, the production section 38, and the diagnosis section 39 mainly illustrate constituent elements realized by the processor 31 executing the cognitive impairment diagnosis program in the present embodiment.

[0078] The processor 31 is a CPU (Central Processing Unit) that executes a program stored in the storage section 32.

[0079] The storage section 32 stores a program executed by the processor 31 and data processed by the processor 31. Further, the storage section 32 constitutes a database 324. The program stored in the storage section 32 includes various firmware, an OS (Operating System), and a driver software and the like, and also includes the cognitive impairment diagnosis program in the present embodiment. In addition, the data stored in the storage section 32 includes diagnosis-use image data, case feature data, viewpoint data, and distribution map data and the like. The diagnosis-use image data is a still image or a moving image produced for diagnosis of cognitive impairment. The case feature data is data showing a feature of a viewpoint distribution corresponding to a typical example of cognitive impairment. The viewpoint data is data showing a time series of positions and time of a viewpoint detected by the detection section 37. The distribution map is produced by the production section 38, and is a map in which viewpoints having a time series according to the viewpoint data are sequentially and in real time drawn on a two-dimensional plane, showing a two-dimensional distribution of the viewpoints.

[0080] In addition, the storage section 32 includes a main memory or a primary storage constituted by a DRAM (Dynamic Random Access Memory) and the like, a secondary storage or a secondary storage constituted by a HDD (Hard Disc Drive) device and an SSD (Solid State Drive) device, and a cache memory. In other words, the storage section 32 is used as a general term of constituent elements having a function of storing a program and data in the present specification.

[0081] The input section 33 includes, for example, a keyboard, a mouse, a trackpad, and the like, and accepts an operation of an operator.

[0082] The output section 34 is, for example, a speaker, and is used for outputting audio.

[0083] The display section 35 is, for example, a liquid crystal display, and displays diagnosis-use images and the like on which a distribution map is superimposed for monitoring by a user (here, a person who performs detection).

[0084] The interface section 36 has a function of connecting and communicating with the display section 10 and the camera 20 via a cable. The interface section 36 has, for example, an HDMI (registered trademark) (High-Definition Multimedia Interface) port and a USB (Universal Serial Bus) port. In this case, the interface section 36 is connected to the display section 10 via an HDMI (registered trademark) cable and to the camera section 21 and the light source section 24 via a USB cable.

[0085] The detection section 37 detects the point of view of the subject on the display surface 11 in time series from an image captured by the camera section 21. For example, the detection section 37 detects the line of sight of the subject from the image captured by the camera section 21, and detects the coordinates of the point at which the line of sight intersects the display surface 11 as the position of the point of view of the subject in the display surface 11. The detection of the position of the point of view is performed periodically. The period can be prescribed to be between several tens of mS and several hundreds of mS, for example, 100 mS. The detection section 37, for example, generates a set of coordinate data (x, y, t) including the time as point of view data representing the position of the point of view in time series in real time. Here, x and y are coordinates of a plane (for example, the display surface 11 or the diagnostic image), and t is the time.

[0086] The production section 38 produces a distribution map showing the distribution of the point of view detected by the detection section 37. The distribution map is, for example, a map in which marks (for example, colored points) corresponding to the coordinate data (x, y, t) are plotted on a two-dimensional plane, and is superimposed on the diagnostic image displayed on the display section 35 of the PC 30 in real time. The marks can be displayed more brightly the more recent the point of view, for example.

[0087] The diagnosis section 39 diagnoses the cognitive function of the subject by judging whether the distribution map has a feature of the case feature data.

[0088] As described above, the cognitive function disorder diagnosis apparatus 1 includes the display section 10 that displays a diagnostic image of a cognitive function disorder on the display surface 11, the camera section 21 that captures the eyes of a subject, the detection section 37 that detects the point of view of the subject in the display surface 11 in time series from an image captured by the camera section 21, the production section 38 that produces a distribution map showing the distribution of the point of view detected by the detection section 37, the storage section 32 that stores case feature data 310 showing a feature of a point of view distribution corresponding to a typical example of a cognitive function disorder, and the diagnosis section 39 that diagnoses a cognitive function disorder of the subject by judging whether the distribution map has a feature of the case feature data 310.

[0089] With this structure, the diagnosis of cognitive function by the cognitive dysfunction diagnosis apparatus 1 can be made simple, low cost, objective, quantitative, and universal.

[0090] In addition, Figure 1A and Figure 2A The PC 30 shown is a notebook type computer, but can also be a tablet computer or a desktop computer.

[0091] Next, other structural examples of the cognitive dysfunction diagnosis apparatus 1 will be described.

[0092] Figure 1B is a block diagram showing another structural example of the cognitive dysfunction diagnosis apparatus 1 in the embodiment. Figure 1B When compared with Figure 1A , the difference is that the display section 10, the imaging section 20, the detection section 37, and the production section 38 are deleted, and the acquisition section 41 is added. Next, the same parts will not be described repeatedly, and the different parts will be described focusing on them.

[0093] Figure 1A The display section 10, the imaging section 20, the detection section 37, and the production section 38 in Figure 1B correspond to structures for producing distribution map data, which is data showing the distribution of the detected person's point of view with respect to the diagnosis-use video. In Figure 1A , the structure for producing the distribution map data is divided from the structure in , and is a structure centered on diagnosing cognitive dysfunction.

[0094] The acquisition section 41 acquires the distribution map data produced by an external apparatus and stores it in the storage section 32. This distribution map data becomes the diagnosis target of cognitive dysfunction.

[0095] In addition, the acquisition section 41 can acquire the distribution map data, the diagnosis-use video data, the case characteristic data, and the point of view data from an external apparatus. For example, when the diagnosis-use video data, the case characteristic data, and the like corresponding to the distribution map data are already stored in the storage section 32, the acquisition section 41 can acquire only the distribution map data. Further, for example, the acquisition section 41 acquires the distribution map data, the diagnosis-use video data, the case characteristic data, and the point of view data corresponding to the first detected person, and acquires only the distribution map data for the second and subsequent detected persons.

[0096] Figure 1A In addition, Figure 1B The cognitive dysfunction diagnosis apparatus 1 in may have the structure of the acquisition section 41 in

[0097] Further, the external device can have only a function of making a distribution map showing the distribution of the time series of the visual point of the subject by eye tracking, and there is no limitation on the method of eye tracking and the structure of making the distribution map. Further, the external device and the cognitive impairment diagnosis device 1 can be connected via the Internet, a telephone line, or a dedicated line.

[0098] In addition, the cognitive impairment diagnosis device 1 can be configured as a smart device without having a large display unit 10 like the appearance example of Figure 2A

[0099] Figure 2B This figure is a diagram showing another appearance example of the cognitive impairment diagnosis device 1. The cognitive impairment diagnosis device 1 of this figure is a smart device of a tablet type, and has a display unit 10 and a camera 22 and the like, which have the same functions as those of the cognitive impairment diagnosis device 1 of the appearance example of Figure 1A

[0100] The display unit 10 of the cognitive impairment diagnosis device 1 of this figure is used as both the display unit 10 and the display unit 35 of the PC 30. A touch screen is formed on the surface of the display surface 11. Figure 2B Figure 2A The camera 22 is a camera on the front surface of the smart device. The camera 22 detects the position and movement of the eyes, for example, by a face authentication technique, and is used for eye tracking. In this case, the light source unit 24 can not be provided. The image display for the subject can be performed on the display unit 10. Further, according to the touch screen operation of the subject, the processor inside the tablet device executes the cognitive impairment diagnosis program. For example, the smart device is alternately used by the subject and the examiner, or the examiner shows the display unit 10 to the subject, and thus the cognitive impairment is diagnosed.

[0101] The cognitive impairment diagnosis device 1 as a smart device can be configured by a tablet type terminal, a smart phone, a notebook computer, or the like. Further, the cognitive impairment diagnosis program can be made as an application of these devices. In addition, the cognitive impairment diagnosis device 1 of the appearance example of

[0102] The cognitive impairment diagnosis device 1 of the appearance example of Figure 2B Figure 1B The cognitive impairment diagnosis device 1 of the appearance example of

[0103] [1.1 Programs and data stored in the storage unit 32]

[0104] Next, the programs and data stored in the storage unit 32 will be described.

[0105] Figure 3 ​​​is a diagram showing an example of the storage contents of the storage section 32 in the embodiment. The storage section 32 in this diagram stores the diagnostic image data 300, the case feature data 310, the program 320, the viewpoint data 322, and the distribution map data 323. The program 320 includes the cognitive impairment diagnosis program 321. Further, the storage section 32 includes the database 324.

[0106] The diagnostic image data 300 is a collection of a plurality of image data including the 1st image data 301 to the 3rd image data 303. The plurality of image data are respectively images made for diagnosing the presence or absence or the degree of cognitive impairment, or images made for identifying a case of cognitive impairment.

[0107] The case feature data 310 is data showing the features of the viewpoint distribution corresponding to a typical case of cognitive impairment, and is a collection of a plurality of feature data including the 1st feature data 311 to the 3rd feature data 313. The 1st feature data 311 to the 3rd feature data 313 respectively correspond to the 1st image data 301 to the 3rd image data 304.

[0108] The program 320 includes various software such as firmware, OS (Operating System), driver software, and the cognitive impairment diagnosis program 321. The cognitive impairment diagnosis program 321 is a program executed by the computer, that is, the PC 30, to cause the computer to perform the following: display the diagnostic image of cognitive impairment on the display surface 11, photograph the eyes of the subject by the imaging section 21, detect the viewpoint of the subject in the display surface 11 in time series from the image photographed by the imaging section 21, make a distribution map showing the distribution of the detected viewpoint, judge whether the distribution map has the features of the case feature data, and thereby diagnose the cognitive impairment of the subject. Here, the PC 30 detects the viewpoint of the subject in the display surface 11 in time series from the image photographed by the imaging section 21, which is the function of the detection section 37. The PC 30 makes the distribution map showing the distribution of the detected viewpoint, which is the function of the making section 38. The PC 30 judges whether the distribution map has the features of the case feature data, and thereby diagnoses the cognitive impairment of the subject, which is the function of the diagnosis section 39.

[0109] The viewpoint data 322 is data in time series showing the position of the viewpoint detected by the detection section 37 and the time, and is, for example, a collection of coordinate data (x, y, t) of the time already explained.

[0110] The distribution map data 323 is data showing the distribution map already explained.

[0111] The database 324 accumulates various data and the like. In addition, the database 324 can not be a part of the storage section 32, and can be provided outside the storage section 32. Further, the database 324 can be connected via a network such as the Internet.

[0112] In addition, the storage section 32 stores, in addition to the programs and data shown, Figure 3 The diagnosis data showing the diagnosis result of the examinee is stored, and data in which the viewpoint data 322, the distribution map data 323, and the diagnosis data of each examinee are associated is stored.

[0113] Next, a specific example of the case feature data 310 will be described.

[0114] Figure 4 is a diagram showing an example of the case feature data 310 in the embodiment. The case feature data 310 of this diagram includes the first feature data 311 to the third feature data 313. In this diagram, cases of cognitive impairment corresponding to the features of the first feature data 311 to the third feature data 313 are described.

[0115] In the first feature data 311, the first feature is associated with memory disorder. The first feature is a typical feature exhibited by a person with memory disorder. Specifically, the first feature shows a feature in which, in a distribution map of a viewpoint, a fixation rate to a memory recall image is low when an image for recalling the memory is prompted after a person with memory disorder is prompted with a learning image. A premise of the first feature is that the examinee is shown a diagnosis image based on the first image data 301, that is, the learning image, and further prompted with an image for recalling the memory.

[0116] In the second feature data 312, the second feature is associated with aphasia. That is, the second feature is a typical feature exhibited by a person with aphasia. Specifically, the second feature shows a feature in which, in a distribution map of a viewpoint for an image having a plurality of selection items including a correct answer image and an incorrect answer image, a fixation rate to the correct answer is small. A premise of this second feature is that the examinee is shown a diagnosis image based on the second image data 302, that is, the second image.

[0117] The third feature data 313 associates the third feature with cognitive impairment. That is, the third feature is a typical feature exhibited by a person with cognitive impairment. Specifically, the third feature shows a feature in which, in a distribution map of a viewpoint for an image having a plurality of selection items including a correct answer image and an incorrect answer image, a fixation rate to the incorrect answer image is not small. A premise of this third feature is that the examinee is shown a diagnosis image based on the third image data 303, that is, the third image.

[0118] [1.2 Processing Example of Cognitive Impairment Diagnosis Apparatus 1]

[0119] The processing example of the cognitive impairment diagnosis device 1 in the embodiment configured as described above will be described.

[0120] Figure 5 is a flowchart showing a diagnosis processing example of the cognitive impairment diagnosis device 1 in the embodiment.

[0121] As shown in Figure 5 , the cognitive impairment diagnosis device 1 sequentially executes the 1st diagnosis processing (S10) to the 3rd diagnosis processing (S30). Figure 5 The diagnosis processing example of the cognitive impairment diagnosis device 1 is mainly processing realized by the PC 30 executing the cognitive impairment diagnosis program 321.

[0122] The 1st diagnosis processing is diagnosis processing using the 1st image data 301 and the 1st feature data 311. The 2nd diagnosis processing is diagnosis processing using the 2nd image data 302 and the 2nd feature data 312. The 3rd diagnosis processing is diagnosis processing using the 3rd image data 303 and the 3rd feature data 313.

[0123] The time of each processing from the 1st diagnosis processing to the 3rd diagnosis processing is about 0.5 minutes to several minutes, respectively.

[0124] In addition, the order of the 1st diagnosis processing to the 3rd diagnosis processing can be different from Figure 5 .

[0125] Further, in Figure 5 , at least one of the 1st diagnosis processing to the 3rd diagnosis processing can be selected and executed.

[0126] In addition, before the start of the diagnosis processing example of Figure 5 , calibration processing of the viewpoint detection can be performed.

[0127] [2.1 1st Diagnosis Processing]

[0128] Next, the 1st diagnosis processing in step S10 of Figure 5 will be described in detail. In the 1st diagnosis processing, the memory disorder is diagnosed using the 1st feature.

[0129] Figure 5 is a flowchart showing an example of the 1st diagnosis processing (S10) in the embodiment. Figure 6 is a diagram showing an example of the diagnosis image or the like in the 1st diagnosis processing of Figure 7 .

[0130] As shown in Figure 6The PC 30 first starts the detection of the viewpoint by the detection section 37 (S61), and executes a cycle 1 (S62-S65) which is a process for causing the examinee to memorize a specific image or a region of a character string. The cycle 1 is repeated N times (S66).

[0131] In the cycle 1, the PC 30 displays a diagnosis image for memorization (S63), and immediately thereafter displays another image (S64). The number of repetitions N of the cycle 1 can be two or more.

[0132] In step S63, a memorization image is presented which causes the examinee's line of sight to be concentrated on a specific figure or region on the screen, involuntarily, in other words, without being instructed by language. In the example of Fig. 10, the diagnosis image includes a memorization image bll and a memorization image bl2 in time series. The memorization image bll is displayed for a time Ta, and immediately thereafter the memorization image bl2 is displayed for a time Tb. For example, the time Ta can be about 5 seconds, and the time Tb can be about 3 seconds. Figure 6

[0133] In the memorization image bll, an image of a total of 20 clocks including 10 clocks in the upper portion and 10 clocks in the lower portion of the display face 11 is included. Of the 20 clock images, only the second clock image Ml from the right in the lowermost layer is rotating. The rotation can be a high-speed rotation of the needle of the clock, or a rotation of the clock image itself. The other 19 clock images are stationary. This is to cause the examinee's attention to be concentrated on the clock image Ml.

[0134] In the memorization image bl2, a dotted line frame which emphasizes only the rotating clock image Ml is added, and the other clock images disappear. Thus, the examinee's attention can be concentrated on the clock image Ml without using language instruction.

[0135] In step S64, another image is displayed for a time Tc, and an interval time for memorization is set. The Tc can be about 60 seconds, for example.

[0136] Figure 7 The display times Td, Te, and Tf in the second repetition in Fig. 10 can be the same as Ta, Tb, and Tc in the first repetition, or can be different times.

[0137] By repeating such a cycle 1, the examinee's attention is involuntarily concentrated on the clock image Ml on the screen, and thus the memory is reinforced.

[0138] The PC 30 displays an evaluation image b3 for recalling the memory for a time Tg after the cycle 1 (S66). The time Tg can be about 5 seconds, for example. Figure 7 ​The evaluation-use image b3 is displayed instead of the clock image Ml, and has the clock image M3. In other words, in the evaluation-use image b3, all 20 clock images are stationary, and only a specific pattern is formed without being noticed originally.

[0139] The examinee without memory disorder leaves the content seen in the memorization-use image (the memorization images bl l and bl2) in memory, and the line of sight is directed toward a specific pattern or region in the memorization-use image without being conscious. The specific pattern or region is a pattern or region memorized in the memorization image, and the line of sight is directed toward the clock image M3 without being conscious in the case of the evaluation-use image b3. Figure 7 The clock image M3 in the evaluation-use image b3 is stationary at the same position as the clock image Ml.

[0140] In this case, the examinee with memory disorder does not maintain memory or does not memorize, and thus the line of sight is not directed toward the clock image M3 without being conscious, and there is a tendency to look at random.

[0141] Thus, the PC 30 sets a specific pattern or region in the evaluation-use image for which memory recall is to be evaluated as a region of interest, and calculates the fixation rate Vb in the region of interest as a score of memory (S67) as a quantitative evaluation of memory. Here, the fixation rate Vb can be a ratio (%) at which a fixation point exists in the region of the clock image M3, which is the region of interest, in a distribution pattern of fixation points at a viewpoint corresponding to the evaluation-use image.

[0142] Further, the PC 30 determines whether the calculated fixation rate Vb is greater than the threshold value thl (S68). In the case where the fixation rate Vb is determined to be greater than the threshold value thl, it is diagnosed that the examinee does not have memory disorder (S69).

[0143] Further, the PC 30 determines whether the calculated fixation rate Vb is less than the threshold value thl and greater than the threshold value th2 (S70). In the case where the fixation rate Vb is determined to be less than the threshold value thl and greater than the threshold value th2, it is diagnosed that the examinee is suspected of having memory disorder (S71).

[0144] Further, the PC 30 determines whether the calculated fixation rate Vb is less than the threshold value th2 (S72). In the case where the fixation rate Vb is determined to be less than the threshold value th2, it is diagnosed that the examinee has memory disorder (S73).

[0145] As for the threshold value thl and the threshold value th2, the fixation rate Vb can be calculated for a plurality of examinees for which memory disorder has been determined in advance, and thus the threshold values can be appropriately set.

[0146] In addition, in the case where the examinee is diagnosed to have memory disorder, the PC 30 can display a message indicating that the examinee has memory disorder. Figure 7Examples of the three diagnostic results of "no memory disorder", "suspected memory disorder", and "memory disorder" are shown in the middle, but the diagnostic results are not limited to these, and can be two categories indicating the presence or absence of memory disorder, or four or more categories indicating the presence or absence and degree of memory disorder.

[0147] In addition, in the flowchart of Figure 6 , instead of steps S61 to S66, a step of obtaining a distribution map indicating the distribution of the viewpoint of the subject with respect to the diagnostic image can be provided. The flowchart replaced with the obtaining step can be easily implemented in the PC 30 of Figure 6 , and can also be implemented in the PC 30 of Figure 1B . Next, an example of the distribution map obtained in the first diagnostic process will be described.

[0148] Figure 1A is a graph showing an example of a distribution map obtained in the first diagnostic process for a subject without memory disorder with respect to MMSE 30 points. Further Figure 8 is a graph showing an example of a distribution map obtained in the first diagnostic process for a subject with memory disorder with respect to MMSE 14 points. In addition, the score of MMSE is 30 points for full marks, and 30 points indicates a healthy person, and the lower the score, the more severe the cognitive dysfunction.

[0149] Figure 9 and Figure 8 shows an example in which the distribution map is overlaid on the learning image b11, the learning image b12, and the evaluation image b3 displayed on the display portion 35 of the PC 30.

[0150] As shown in Figure 9 and as shown in Figure 8 , in the learning image b11 and the learning image b12, the subject's viewpoint is concentrated on the clock image M1. The learning image b11 and the learning image b12 are diagnostic images having a first region containing a learning image visually emphasized, i.e., the clock image M1, and a plurality of second regions containing a normal image without emphasized display, i.e., a still clock image. As shown in Figure 9 and Figure 8 , the subject without memory disorder and the subject with memory disorder are substantially the same in that the viewpoint is concentrated on the clock image M1 of the first region.

[0151] In the evaluation image b3 of Figure 9 , the distribution of the subject's viewpoint without memory disorder, as a manifestation of the effect of learning, is very high at 81% in the fixation rate on the clock image M3 of the region of interest.

[0152] In this regard, in the evaluation image b3 of Figure 8The evaluation image b3, in which the person with memory disorder has a viewpoint distribution, does not show the effect of memorization, and the fixation rate on the clock image M3 of the region of interest is 0%, which is very low.

[0153] In the first diagnosis process, the fixation rate on the region of interest in the evaluation image in which the memory comes to mind is used as a score of attention, and the degree of memory of the subject is quantitatively evaluated to diagnose memory disorder.

[0154] As described above, the cognitive impairment diagnosis device that performs the first diagnosis process includes an acquisition unit 41 that acquires a distribution map showing a distribution of a subject's viewpoint with respect to a diagnosis image, and a diagnosis unit 39 that diagnoses cognitive impairment based on the distribution map, the diagnosis image including a first image and an evaluation image in chronological order, the first image having a first region and a plurality of second regions, the first region including a memorization image that is visually emphasized, and the second region including a normal image that is not emphasized, the evaluation image having a third region and a plurality of second regions, the third region including an evaluation image, the evaluation image being an image similar to the memorization image and displayed without visual emphasis at the same position as the memorization image, the diagnosis unit 39 calculating a fixation rate of the third region in the distribution map with respect to the evaluation image, and diagnosing as suspected of having memory disorder when the fixation rate is lower than a threshold value.

[0155] In addition, the cognitive impairment diagnosis device that performs the first diagnosis process includes a display unit 10 having a display surface, an imaging unit 21 that photographs the subject's eye, a detection unit 37 that detects the subject's viewpoint on the display surface in chronological order based on an image photographed by the imaging unit 21, a creation unit 38 that creates a distribution map showing a distribution of the viewpoint detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive impairment based on the distribution map, the diagnosis unit 39 causing the first image to be displayed on the display unit 10, the first image having a first region and a plurality of second regions, the first region including a memorization image that is visually emphasized, and the second region including a normal image that is not emphasized, and further causing the evaluation image to be displayed on the display unit 10, the evaluation image having a third region and a plurality of second regions, the third region including an evaluation image, the evaluation image being an image similar to the memorization image and displayed without visual emphasis at the same position as the memorization image, calculating a fixation rate of the third region in the distribution map with respect to the evaluation image, and diagnosing as suspected of having memory disorder when the fixation rate is lower than a threshold value.

[0156] By the above, it is possible to provide a cognitive dysfunction diagnosis device that is simple, low cost, objective, quantitative, and universal in diagnosing memory impairment in cognitive dysfunction. Moreover, it is possible to perform diagnosis without using verbal and audio instructions. It is possible to perform on multiple subjects who have different native languages. Furthermore, it is possible to diagnose memory impairment in only a few minutes.

[0157] Here, the first image can include a first learning image and a second learning image in chronological order, the first learning image can have a first area and a plurality of second areas, and the second learning image can have the first area and no second areas.

[0158] By the above, it is possible to improve the learning ability of the subject for the first area.

[0159] Here, the diagnosis image can include another image, and the other image can be an image displayed between the first image and the evaluation image.

[0160] By the above, the display period of the other image is provided between learning and evaluation, so it is possible to determine whether the memory of the subject can be maintained or not, and it is possible to improve the reliability of the diagnosis result.

[0161] Here, the diagnosis image can include an image in which the first image and the other image are repeated more than twice.

[0162] By the above, it is possible to improve the learning ability of the subject for the first area by repeating.

[0163] In addition, the cognitive dysfunction diagnosis program that performs the first diagnosis processing causes a computer to perform the following processing: obtaining a distribution map showing the distribution of the subject's viewpoint for a diagnosis image, diagnosing cognitive dysfunction based on the distribution map, the diagnosis image including a first image and an evaluation image in chronological order, the first image having a first area and a plurality of second areas, the first area containing a learning image displayed with visual emphasis, the second area containing a normal image without emphasis, the evaluation image having a third area and a plurality of second areas, the third area containing an evaluation image, the evaluation image being an image similar to the learning image and displayed without visual emphasis at the same position as the learning image, the cognitive dysfunction diagnosis program causing the computer to perform the following processing: calculating the fixation rate of the third area in the distribution map for the evaluation image, and diagnosing as suspected of having memory impairment if the fixation rate is lower than a threshold value.

[0164] Further, the cognitive impairment diagnosis program that performs the first diagnosis processing is a program that causes a computer to execute the following processing. The computer has a display section 10 having a display surface, an imaging section 21 that photographs an eye of a subject, a detection section 37 that detects a point of view of the subject on the display surface in a time series from an image photographed by the imaging section 21, a production section that produces a distribution map that shows a distribution of the point of view detected by the detection section 37, and a diagnosis section 39 that diagnoses a cognitive impairment from the distribution map. The computer is caused to execute the following processing. The diagnosis image is caused to be displayed on the display section 10. The diagnosis image has a first area that contains a memorization image that is visually emphasized and displayed and a plurality of second areas that contain normal images that are not emphasized and displayed. Further, the evaluation image is caused to be displayed on the display section 10. The evaluation image has a third area that contains an evaluation image and a plurality of second areas. The evaluation image is an image that is similar to the memorization image and is displayed without visual emphasis at the same position as the memorization image. A fixation rate of the third area in the distribution map for the evaluation image is calculated. In a case where the fixation rate is lower than a threshold value, it is diagnosed that there is a suspicion of memory impairment.

[0165] [2.2.1 Second diagnosis processing (example using language indication)]

[0166] Next, an example of the second diagnosis processing in step S20 in the embodiment will be described. In the second diagnosis processing, aphasia is diagnosed using the above-described second feature. Figure 9

[0167] is a flowchart showing an example of the second diagnosis processing in the embodiment. Further, Figure 5 Figure 10 is a diagram showing an example of the diagnosis image used in the second diagnosis processing in step S20 in the embodiment. Figure 11

[0168] In the PC 30 in step S20 in the embodiment, first, the detection of the point of view by the detection section 37 is started (S101). The diagnosis image is displayed on the display surface 11 (S102). The fixation rate Vc of the correct answer area in the diagnosis image is calculated (S103). Figure 10 Here, the diagnosis image is an image that has a first area that contains a correct answer text string image and a plurality of second areas that contain non-correct answer text string images.

[0169] In the example in step S20 in the embodiment, the diagnosis image c11 is displayed for Ta seconds. The diagnosis image c12 is displayed for Tb seconds. Each of the times Ta and Tb may, for example, be 5 seconds to several tens of seconds or so.

[0170] Figure 10

[0171] ​​​​The diagnostic image cll has a clock image in the center and four regions as selection items around the clock image. The diagnostic image cll is different from the diagnostic image cl l in that an instruction sentence is overlaid on the clock image in the center. The instruction sentence is "What is this?" The four regions around the instruction sentence are provided with a first region including a correct answer text string image "tokei" (clock) and a plurality of second regions including non-correct answer text string images "tomato", "ebi" (shrimp), and "ketto" (yarn).

[0172] Thus, the diagnostic image has, for example, an image of a well-known article and a plurality of regions as selection items including a correct name of the article and incorrect names of the article. The number of selection items is arbitrary. Further, the instruction sentence "What is this?" is displayed. The examinee is urged to look at the selection item of the correct answer by the instruction sentence.

[0173] In addition, instead of the two diagnostic images cl l and cll, Figure 11 one diagnostic image c 13 as shown in Figure 11 may be displayed.

[0174] Further, the PC 30 calculates a gaze rate Vc to the viewpoint of the first region including the correct answer text string image after the diagnostic image is displayed (S 103). Here, the gaze rate Vc can be a ratio (%) in which the viewpoint exists in the first region in a distribution pattern of the viewpoint corresponding to the diagnostic image.

[0175] Next, the PC 30 determines whether the calculated gaze rate Vc is greater than a threshold value th3 (S 104). In a case where it is determined that the gaze rate Vc is greater than the threshold value th3, it is diagnosed that the examinee has no aphasia (S 105).

[0176] Further, the PC 30 determines whether the calculated gaze rate Vc is less than the threshold value th3 and greater than a threshold value th4 (S 106). In a case where it is determined that the gaze rate Vc is less than the threshold value th3 and greater than the threshold value th4, it is diagnosed that the examinee is suspected of having aphasia (S 107).

[0177] Further, the PC 30 determines whether the calculated gaze rate Vc is less than the threshold value th4 (S 108). In a case where it is determined that the gaze rate Vc is less than the threshold value th4, it is diagnosed that the examinee has aphasia (S 109).

[0178] As for the threshold value th3 and the threshold value th4, the gaze rate Vc is calculated by taking a plurality of examinees whose aphasia is previously determined as an object, and thus appropriate setting can be made.

[0179] In addition, in a case where the diagnostic image is displayed on the display 20, Figure 12In the flowchart, instead of steps S101, S102, there can also be a step of obtaining a distribution map that indicates the distribution of the viewpoints of the examinee with respect to the diagnostic image. The flowchart that is replaced with the obtaining step can be easily implemented in the PC 30, and can be implemented in the PC 30 of Figure 10 Figure 1B

[0180] In addition, examples of outputting three kinds of diagnostic results of "no aphasia", "suspected of aphasia", and "aphasia" are shown in the PC 30 of Figure 1A

[0181] Next, an example of the distribution map obtained in the second diagnostic process will be described.

[0182] Figure 10 is a graph showing an example of the distribution map obtained in the second diagnostic process for the examinee without aphasia with respect to the MMSE 30 points. Further Figure 13 is a graph showing an example of the distribution map obtained in the second diagnostic process for the examinee with aphasia with respect to the MMSE 14 points.

[0183] In the diagnostic image c12 in the PC 30 of Figure 14 the examinee without aphasia, the distribution of the viewpoints is high in the gaze rate on the first region c111 that contains the correct answer text string image.

[0184] For this, in the diagnostic image c12 in the PC 30 of Figure 13 the examinee with aphasia, the distribution of the viewpoints is low in the gaze rate on the first region c111 that contains the correct answer text string image.

[0185] In a case where the gaze rate Vc on the first region that is the correct answer exceeds the threshold value th3, it is judged that the answer to the instruction sentence is correct, that is, it can be judged that the ability to call the article is normal. In other words, the examinee without aphasia can gaze on the correct answer selection item of "tokei (clock)" at a rate of the threshold value or more. For this, the examinee with aphasia gazes on the selection item other than the correct answer at a relatively large rate. Therefore, in the second diagnostic process, the gaze rate on the correct answer selection item can be used as an index to diagnose the presence or absence of aphasia and the degree of the examinee.

[0186] [2.2.2 Second Diagnostic Process (First Example in Which Language Instruction is Not Used)]

[0187] In the above-described second diagnostic process, an example in which language instruction is used is shown. Next, a first example in which language instruction is not used in the second diagnostic process will be described. ​​​

[0188] Figure 14 It is shown in Figure 15 The first case of diagnostic imaging in the second diagnostic process where no verbal instructions were used.

[0189] Figure 10 The diagnostic image c21 shows a display example of the display surface 11 for the subject, having correct answer areas containing meaningful text strings and incorrect answer areas containing meaningless text strings. Specifically, the diagnostic image c21 contains multiple characters arranged in a matrix. The correct answer areas correspond to rows or columns composed of meaningful text strings. The incorrect answer areas correspond to rows or columns composed of meaningless text strings. The row containing the text string "だんご (rice dumpling)" is the correct answer area. The other rows and columns are incorrect answer areas that do not contain meaningful text strings.

[0190] Figure 15 It is shown that... Figure 16 The corresponding evaluation image is shown in the figure. Additionally... Figure 15 This shows an example of a display unit 35 for the operator. Figure 16 The diagnostic image c21 is shown with a dashed box indicating the correct answer area c211 used for assessment.

[0191] The following explains the... Figure 15 An example of the viewpoint distribution of the subject in diagnostic image c21.

[0192] Figure 15 This demonstrates the use of [method / technology] for subjects with a score of 30 on the MMSE who do not have aphasia. Figure 17 An example of a distribution map obtained from the second diagnostic treatment. Additionally... Figure 15 This demonstrates the use of [method / technology] for subjects with aphasia who scored 14 on the MMSE. Figure 18 An example of a distribution map obtained in the second diagnostic treatment.

[0193] exist Figure 15 In the diagnostic image c21, the gaze distribution of people without aphasia showed a high fixation rate towards the correct answer region c211.

[0194] In this regard, Figure 17 In the diagnostic image c21, the viewpoint distribution of people with aphasia is not related to the correct answer region c211 and has a random tendency.

[0195] In diagnostic image C21, text is arranged in a matrix or grid pattern. Only in specific sections are there meaningfully connected strings of text. In other sections, text cannot be read as a meaningfully connected string from any direction.

[0196] In a case where such a matrix-like character string is displayed, the examinee without aphasia can find a character string connected meaningfully therein, and the gaze rate at the portion is unconsciously made high.

[0197] On the other hand, the examinee with aphasia cannot find a character string connected meaningfully, and thus the gaze rate at the specific portion is not relatively made high, and has a tendency to gaze at the whole randomly. Thus, in the second diagnosis process, whether or not the gaze rate at the portion of the character string connected meaningfully (in other words, the correct answer region) exceeds a threshold value can be used as an index to objectively diagnose aphasia.

[0198] In Figure 18 The diagnosis-use image c21 displays a character string of 3 characters x 3 characters of hiragana. The 3 characters located in the lowermost row are read from left to right as "dan go (rice ball)", and can be recognized as a meaningful character string. Combinations other than this basically become meaningless character strings. Although no character and sound as a pointing sentence that prompts finding of this "dan go" are presented, the healthy person without aphasia has a relatively increased gaze rate at the meaningful character string "dan go" in the unconscious.

[0199] In this regard, the examinee with aphasia cannot recognize the meaningful character string, and has no increased gaze rate at "dan go (rice ball)", and has a tendency to look at another region or a tendency to gaze at the whole of the screen randomly.

[0200] [2.2.3 Second diagnosis process (second example in which language pointing is not used)]

[0201] The second example in which language pointing is not used in the second diagnosis process is further described.

[0202] Figure 15 Fig. 21 is a diagram showing a second example of a diagnosis-use image in which language pointing is not used in the second diagnosis process of the present embodiment. Figure 19 Fig. 22 is a diagram showing a second example of a diagnosis-use image in which language pointing is not used in the second diagnosis process of the present embodiment. Figure 10 Fig. 23 is a diagram showing an example of an evaluation-use image corresponding to Fig. 22. Figure 20 Fig. 24 is a diagram showing an example of an evaluation-use image corresponding to Fig. 21.

[0203] Figure 19 The diagnosis-use image c31 of Fig. 31 shows a display example of the display surface 11 for the examinee, and has a correct answer region including a meaningful character string and a non-correct answer region including a meaningless character string. Specifically, in the diagnosis-use image c31, the column including the character string of "tanabata (Tanabata)" is a correct answer region, and the rows and columns other than this are non-correct answer regions not including a meaningful character string.

[0204] Further, Figure 19An example of a display unit 35 for the operator is shown. Figure 20 China's target Figure 20 The diagnostic image c31 is shown with a dashed box indicating the correct answer area c311 used for assessment.

[0205] The following explains the... Figure 19 An example of the viewpoint distribution of the subject in diagnostic image c31.

[0206] Figure 19 This demonstrates the use of [method / technology] for subjects with a score of 30 on the MMSE who do not have aphasia. Figure 21 An example of a distribution map obtained from the second diagnostic treatment. Additionally... Figure 19 This demonstrates the use of [method / technology] for subjects with aphasia who scored 14 on the MMSE. Figure 22 An example of a distribution map obtained in the second diagnostic treatment.

[0207] exist Figure 19 In diagnostic images (c12), the fixation rate on the correct answer region (c311) was increased in the viewpoint distribution of individuals without aphasia. Regarding this, in Figure 21 In the diagnostic image c31, the viewpoint distribution of people with aphasia is not related to the correct answer region c311 and has a random tendency.

[0208] exist Figure 22 The diagnostic image c31 shows a string of 4 x 4 hiragana characters. The four characters in the second column from the right, read from top to bottom, form "たなばた (Tanabata)," which is a meaningful string. All other combinations are essentially meaningless. No prompts or sounds are provided to encourage the discovery of "たなばた." Healthy individuals without aphasia unconsciously show a relatively higher fixation rate on the meaningfully connected string "たなばた." In contrast, subjects with aphasia cannot recognize this meaningfully connected string, so their fixation rate on this area is not increased; they tend to look at other areas or gaze randomly across the entire image.

[0209] As described above, the cognitive impairment diagnostic device 1 for performing the second diagnostic process includes: an acquisition unit 41 that acquires a distribution map showing the distribution of the subject's viewpoints in relation to a diagnostic image; and a diagnosis unit 39 that diagnoses cognitive impairment based on the distribution map. The diagnostic image has a first region and a plurality of second regions. The first region contains a correct answer image or a memorized image for diagnosis, and the second regions contain other images. The diagnosis unit 39 calculates the fixation rate of the first region or the second region in the distribution map of the diagnostic image and diagnoses whether there is cognitive impairment by comparing the fixation rate with a threshold.

[0210] Further, the cognitive dysfunction diagnosis device 1 that performs the second diagnosis processing includes: a display section 10 having a display surface; an imaging section 21 that photographs the eyes of a subject; a detection section 37 that detects the visual point of the subject on the display surface in a time series based on an image photographed by the imaging section 21; a creation section that creates a distribution map that shows the distribution of the visual point detected by the detection section 37; and a diagnosis section 39 that diagnoses cognitive dysfunction based on the distribution map, the diagnosis section 39 causing a diagnosis image to be displayed on the display section 10, the diagnosis image having a first region that includes a correct answer image or a learning image for diagnosis and a plurality of second regions that include other images, calculating the fixation rate of the first region or the second regions in the distribution map for the diagnosis image, and diagnosing whether or not there is cognitive dysfunction by comparing the fixation rate with a threshold value.

[0211] According to the above, a cognitive dysfunction diagnosis device that is simple, low cost, objective, quantitative, and general-purpose in the diagnosis of cognitive dysfunction can be provided. Moreover, the cognitive dysfunction can be diagnosed in only a few seconds to tens of seconds.

[0212] Further, the cognitive dysfunction diagnosis device 1 that performs the second diagnosis processing includes: a display section 10 having a display surface; an imaging section 21 that photographs the eyes of a subject; a detection section 37 that detects the visual point of the subject on the display surface in a time series based on an image photographed by the imaging section 21; a creation section that creates a distribution map that shows the distribution of the visual point detected by the detection section 37; and a diagnosis section 39 that diagnoses cognitive dysfunction based on the distribution map, the diagnosis section 39 causing a diagnosis image to be displayed on the display section 10, the diagnosis image having a first region that includes a correct answer image or a learning image for diagnosis and a plurality of second regions that include other images, calculating the fixation rate of the first region or the second regions in the distribution map for the diagnosis image, and diagnosing whether or not there is cognitive dysfunction by comparing the fixation rate with a threshold value.

[0213] Further, the cognitive dysfunction diagnosis device 1 that performs the second diagnosis processing includes: a display section 10 having a display surface; an imaging section 21 that photographs the eyes of a subject; a detection section 37 that detects the visual point of the subject on the display surface in a time series based on an image photographed by the imaging section 21; a creation section that creates a distribution map that shows the distribution of the visual point detected by the detection section 37; and a diagnosis section 39 that diagnoses cognitive dysfunction based on the distribution map, the diagnosis section 39 causing a diagnosis image to be displayed on the display section 10, the diagnosis image having a first region that includes a correct answer image or a learning image for diagnosis and a plurality of second regions that include other images, calculating the fixation rate of the first region or the second regions in the distribution map for the diagnosis image, and diagnosing whether or not there is cognitive dysfunction by comparing the fixation rate with a threshold value.

[0214] By the above, a cognitive dysfunction diagnosis device can be provided, which is simple, low cost, objective, quantitative, and general-purpose for the diagnosis of aphasia in cognitive dysfunction. Moreover, the cognitive dysfunction can be diagnosed in only a few seconds to tens of seconds.

[0215] Here, the diagnosis image can have an image area, a question area, a first area, and a second area, the image area can include an image showing a known object, the question area can include a question sentence inquiring about the name of the object, the first area can be a correct answer area including a character string image showing the name of the object, and the second area can be a non-correct answer area including a character string image other than the name of the object.

[0216] By the above, the aphasia related to the name of the object can be diagnosed by using the language indication such as the question sentence.

[0217] Here, the diagnosis image can have a correct answer area including a meaningful character string and a correct answer area including a meaningless character string.

[0218] By the above, the aphasia can be diagnosed without using the indication sentence and the sound indication.

[0219] Here, the diagnosis image can include a plurality of characters arranged in a matrix, the correct answer area can correspond to a row or a column composed of a meaningful character string, and the non-correct answer area can correspond to a row or a column composed of a meaningless character string.

[0220] By the above, the diagnosis image can be simplified, and the aphasia can be diagnosed without using the indication sentence and the sound indication.

[0221] Further, the cognitive dysfunction diagnosis program that performs the second diagnosis processing causes a computer to perform the following processing, obtains a distribution map showing a distribution of a viewpoint of a subject with respect to a diagnosis image, and diagnoses a cognitive dysfunction based on the distribution map, the diagnosis image has a first area including a correct answer image or a recognition image for diagnosis and a plurality of second areas including other images, and the cognitive dysfunction diagnosis program causes the computer to perform the following processing, calculates a fixation rate of the first area or the second area in the distribution map with respect to the diagnosis image, and diagnoses whether or not there is a cognitive dysfunction by comparing the fixation rate with a threshold value.

[0222] Furthermore, the cognitive impairment diagnostic procedure for the second diagnostic process is a computer-executed program comprising: a display unit 10 having a display surface; a camera unit 21 for capturing images of the subject's eyes; a detection unit 37 for detecting the subject's gaze points on the display surface in a time sequence based on the images captured by the camera unit 21; a production unit for producing a distribution map showing the distribution of gaze points detected by the detection unit 37; and a diagnosis unit 39 for diagnosing cognitive impairment based on the distribution map. The cognitive impairment diagnostic procedure causes the computer to perform the following processing: displaying a diagnostic image on the display unit 10; the diagnostic image having a first region containing a correct answer image or a memorized image for diagnosis, and multiple second regions containing other images; calculating the fixation rate of the first or second region in the distribution map for the diagnostic image; and diagnosing whether or not cognitive impairment exists by comparing the fixation rate with a threshold.

[0223] In addition, other cognitive impairment diagnostic procedures for performing the second diagnostic process involve the computer performing the following processing: obtaining a distribution map showing the distribution of the subject's viewpoints in relation to the diagnostic images; diagnosing cognitive impairment based on the distribution map; the diagnostic images having a first region containing a string of correct answers and multiple second regions containing strings of incorrect answers; the cognitive impairment diagnostic procedure involves the computer performing the following processing: calculating the fixation rate of the first region in the distribution map of the diagnostic images; and diagnosing suspected aphasia if the fixation rate is lower than a threshold.

[0224] Furthermore, the other cognitive impairment diagnostic procedure for performing the second diagnostic process is a cognitive impairment diagnostic procedure executed by a computer, which includes: a display unit 10 having a display surface; a camera unit 21 for capturing images of the subject's eyes; a detection unit 37 for detecting the subject's viewpoints on the display surface in a time sequence based on the images captured by the camera unit 21; a production unit for producing a distribution map showing the distribution of viewpoints detected by the detection unit 37; and a diagnosis unit 39 for diagnosing cognitive impairment based on the distribution map, wherein the computer performs the following processing: displaying a diagnostic image on the display unit 10, the diagnostic image having a first region containing a text string of correct answers and multiple second regions containing text strings of incorrect answers, calculating the fixation rate of the first region in the distribution map for the diagnostic image, and diagnosing suspected aphasia if the fixation rate is lower than a threshold.

[0225] [2.3 Third Diagnostic Treatment]

[0226] Next Figure 19 Step S30 in the document will be described as the third diagnostic procedure. In the third diagnostic procedure, cognitive impairment is diagnosed using the third feature described above.

[0227] Figure 5 is a flowchart showing an example of the 3rd diagnosis processing in the embodiment. Further Figure 23 is a diagram showing an example of a diagnosis image of the 3rd diagnosis processing of Figure 24

[0228] In the PC 30 of Figure 23 , first, the detection by the detection section 37 is started (S201), the diagnosis image is displayed on the display surface 11 (S202), and the gaze rate Va of the incorrect answer region in the diagnosis image is calculated (S203).

[0229] Here, the diagnosis image is an image having a 1st region including a correct answer image, and a plurality of 2nd regions including incorrect answer images. For example, the diagnosis image includes a 1st region, a cue region, a 2nd region, and another 2nd region, the 1st region includes a correct answer image showing a 1st figure, the cue region includes a cue for inducing a direct view of the 1st figure, the 2nd region includes an incorrect answer image showing a figure similar to the 1st figure, and the other 2nd region includes an incorrect answer image showing a figure not similar to the 1st figure,

[0230] Figure 23 An example of the diagnosis image al of

[0231] Figure 24 is a diagram showing an example of an evaluation image of the 3rd diagnosis processing of Figure 25 Figure 23 An example of the diagnosis image al of Figure 25 , has a 1st region including a correct answer image al 1 showing a hexagon as a 1st figure, a cue region al 2 of "Please gaze at the hexagon", and a plurality of 2nd regions including incorrect answer images. The plurality of 2nd regions include a 2nd region including an incorrect answer image of a pentagon similar to the 1st figure, a 2nd region including an incorrect answer image of a heptagon similar to the 1st figure, and a 2nd region including an incorrect answer image al 3 of a triangle not similar to the 1st figure.

[0232] ​​Furthermore, after displaying the diagnostic image, PC30 calculates the fixation rate Va of the viewpoint viewing the region containing the incorrect answer image (S203). Here, the fixation rate Vc can be the percentage (%) of the viewpoint existing in the first region in the distribution pattern of viewpoints corresponding to the diagnostic image. In addition, the incorrect answer region that is the object of the fixation rate Va calculation can be the second region of the outrageous incorrect answer image a13, the indicator sentence region a12, or the region that combines the second region of the outrageous incorrect answer image a13 and the indicator sentence region a12.

[0233] Next, PC30 determines whether the calculated fixation rate Va is greater than the threshold th5 (S204). If the fixation rate Va is determined to be greater than the threshold th5, the subject is diagnosed with cognitive impairment (S205).

[0234] Then, PC30 determines whether the calculated fixation rate Va is smaller than the threshold th5 and larger than the threshold th6 (S206). If the fixation rate Va is determined to be smaller than the threshold th5 and larger than the threshold th6, the subject is diagnosed as having suspected cognitive impairment (S207).

[0235] Then, PC30 determines whether the calculated fixation rate Va is smaller than the threshold th6 (S208). If the fixation rate Va is determined to be smaller than the threshold th6, the subject is diagnosed as having no cognitive impairment (S209).

[0236] Regarding the thresholds th5 and th6, they can be appropriately set by calculating the fixation rate Va for multiple subjects who have been pre-determined to have cognitive impairment.

[0237] In addition, Figure 25 In the flowchart, instead of steps S201 and S202, a step of obtaining a distribution map can be included, which shows the distribution of the subject's viewpoints for diagnostic images. The flowchart replaced with this obtaining step can be easily... Figure 23 Implemented in PC30, and can also be used in Figure 1B Implemented in PC30.

[0238] Figure 1A This demonstrates the use of [the technology] on subjects without cognitive impairment. Figure 26 The figure shows an example of a distribution map in the third diagnostic process. As the figure illustrates, subjects without cognitive impairment follow the instructions on the screen, selecting the correct answer image a11 from the options and fixing their gaze on that image. The fixation rate on incorrect answer images is low, especially on the outrageously incorrect answer image a13, where the fixation rate is extremely low.

[0239] In the third diagnostic process, not only the fixation rate on the correct answer image, but also the fixation rate on the incorrect answer image and the fixation rate on the indicator sentence region can be used as indicators to assess the subject's cognitive function. That is, the fixation rate on the indicator sentence region and the fixation rate on the incorrect answer image in the diagnostic images are inversely correlated with the subject's cognitive function, so these fixation rates can be used as indicators to assess cognitive function.

[0240] The fixation rate on incorrect answer images, especially on "outrageously incorrect answer images," shows an inverse correlation with the cognitive function of the test subjects. For example, when presented with four options, one of which is the correct answer image, the remaining three are incorrect. One of these three incorrect answer images is defined as an image containing content significantly different from the correct answer, considered an "outrageously incorrect answer image." In other words, an outrageously incorrect answer image is an option that would be clearly judged as incorrect if cognitive function were normal. The fixation rate on the correct answer image shows a positive correlation with the cognitive function of the test subjects, but the fixation rate on outrageously incorrect answer images shows a negative correlation.

[0241] exist Figure 24 In the diagnostic images shown, four images are displayed as choices (pentagon, hexagon, heptagon, and triangle), with the instruction "Please look at the hexagon." The hexagon is the "correct answer image," while the triangle becomes the "outrageous incorrect answer image." The pentagon and heptagon, similar to the hexagon as the correct answer, cause hesitation for the test subject when choosing, but healthy individuals would not hesitate to choose the triangle.

[0242] Figure 24 This shows the third diagnostic treatment. Figure 27 A graph showing the correlation between fixation rate in the indicator region and MMSE score. Additionally... Figure 25 This shows the third diagnostic treatment. Figure 28 A graph showing the correlation between fixation rate and MMSE scores for images with wildly inaccurate incorrect answers.

[0243] like Figure 25 as well as Figure 27 As shown, the fixation rate for the indicator region a12 and the fixation rate for the outrageous incorrect answer image a13 are both statistically negatively correlated with the subject's cognitive function score (MMSE score).

[0244] From this perspective, the third diagnostic process not only uses the fixation rate on the correct answer image, but also the fixation rate on the incorrect answer image and the fixation rate on the indicator sentence region as indicators to assess the cognitive function of the test subject.

[0245] The cognitive impairment diagnosis device 1 that performs the third diagnosis processing includes: an acquisition unit 41 that acquires a distribution map that shows a distribution of a point of view of a subject with respect to a diagnosis image; and a diagnosis unit 39 that diagnoses a cognitive impairment based on the distribution map, the diagnosis image having a first region that includes a correct answer image and a plurality of second regions that include incorrect answer images, the diagnosis unit 39 calculating a fixation rate in the distribution map with respect to the diagnosis image for a region other than the first region, and diagnosing as suspected of having a cognitive impairment when the fixation rate is higher than a threshold value.

[0246] Further, the cognitive impairment diagnosis device 1 that performs the third diagnosis processing includes: a display unit 10 having a display surface; an imaging unit 21 that captures an eye of a subject; a detection unit 37 that detects a point of view of the subject on the display surface in a time series based on an image captured by the imaging unit 21; a creation unit that creates a distribution map that shows a distribution of the point of view detected by the detection unit 37; and a diagnosis unit 39 that diagnoses a cognitive impairment based on the distribution map, the diagnosis unit 39 causing a diagnosis image to be displayed on the display unit 10, the diagnosis image having a first region that includes a correct answer image and a plurality of second regions that include incorrect answer images, calculating a fixation rate in the distribution map with respect to the diagnosis image for a region other than the first region, and diagnosing as suspected of having a cognitive impairment when the fixation rate is higher than a threshold value.

[0247] According to the above, it is possible to provide a cognitive impairment diagnosis device that is simple, low cost, objective, quantitative, and universal in the diagnosis of cognitive impairment. It is also possible to diagnose a cognitive impairment using a fixation rate on an incorrect answer image as an index. Furthermore, it is possible to diagnose a cognitive impairment in a period of only several seconds to several tens of seconds.

[0248] Here, the diagnosis image can include a cue region, the first region, the second region, and another second region, the cue region including a cue for inducing a direct view of the first figure, the first region including a correct answer image that shows the first figure, the second region including an incorrect answer image that shows a figure similar to the first figure, and the other second region including an incorrect answer image that shows a figure not similar to the first figure, and the diagnosis unit 39 can calculate a fixation rate on the cue region or the other second region.

[0249] According to the above, it is possible to diagnose a cognitive impairment using a fixation rate on the cue region or the other second region as an index.

[0250] Further, the cognitive impairment diagnosis program that performs the third diagnosis processing is a program that causes a computer to execute processing, the computer including: a display section 10 having a display surface; an imaging section 21 that photographs an eye of a subject; a detection section 37 that detects a point of view of the subject on the display surface in a time series based on an image photographed by the imaging section 21; a production section that produces a distribution map that shows a distribution of the point of view detected by the detection section 37; and a diagnosis section 39 that diagnoses a cognitive impairment based on the distribution map, the diagnosis section 39 causing a diagnosis image to be displayed on the display section 10, the diagnosis image having a first region that includes a correct answer image and a plurality of second regions that include incorrect answer images, the computer executing processing that calculates a fixation rate in the distribution map for the diagnosis image for a region other than the first region, and in a case where the fixation rate is higher than a threshold value, diagnosing that the cognitive impairment is suspected.

[0251] Further, the cognitive impairment diagnosis program that performs the third diagnosis processing is a program that causes a computer to execute processing, the computer including: a display section 10 having a display surface; an imaging section 21 that photographs an eye of a subject; a detection section 37 that detects a point of view of the subject on the display surface in a time series based on an image photographed by the imaging section 21; a production section that produces a distribution map that shows a distribution of the point of view detected by the detection section 37; and a diagnosis section 39 that diagnoses a cognitive impairment based on the distribution map, the diagnosis section 39 causing a diagnosis image to be displayed on the display section 10, the diagnosis image having a first region that includes a correct answer image and a plurality of second regions that include incorrect answer images, the computer executing processing that calculates a fixation rate in the distribution map for the diagnosis image for a region other than the first region, and in a case where the fixation rate is higher than a threshold value, diagnosing that the cognitive impairment is suspected.

[0252] In addition, the cognitive impairment diagnosis apparatus 1 can simultaneously take a plurality of subjects as targets. In this case, the imaging section 21 photographs a plurality of subjects, the detection section 37 detects a point of view for each subject, the production section 38 produces a distribution map for each subject, and the diagnosis section 39 can perform diagnosis for each subject. Further, in a case where the cognitive impairment diagnosis apparatus 1 simultaneously takes a plurality of subjects as targets, the cognitive impairment diagnosis apparatus 1 can include a plurality of imaging devices 20. In this case, the imaging device 20 and the subject can be one-to-one or one-to-many. Thus, the cognitive impairment diagnosis apparatus 1 can further improve the efficiency of group examination.

[0253] The above-described embodiments and modified examples thereof are exemplified and described for the purpose of explaining the technical contents of the present application, and the gist thereof is not intended to limit the technical scope of the application in the present application to the described contents. The technical scope of the application in the present application also includes various modifications, substitutions, additions, and omissions within the scope of the contents of the specification, the drawings, and the claims or equivalent thereto, which can be conceived by those skilled in the art.

[0254] Industrial Applicability

[0255] The present application can be used for a cognitive impairment diagnosis device and a cognitive impairment diagnosis program for diagnosing cognitive impairment.

[0256] Figure 28 List of Symbols

[0257] 1 Cognitive impairment diagnosis device

[0258] 10 Display section

[0259] 11 Display surface

[0260] 20 Imaging device

[0261] 21 Imaging section

[0262] 22, 23 Camera

[0263] 24 Light source section

[0264] 25, 26 Light source

[0265] 30 PC

[0266] 31 Processor

[0267] 32 Storage section

[0268] 33 Input section

[0269] 34 Output section

[0270] 35 Display section

[0271] 36 Interface section

[0272] 37 Detection section

[0273] 38 Production section

[0274] 39 Diagnosis section

[0275] 300 Diagnosis image data

[0276] 301 First image data

[0277] 302 Second image data

[0278] 303 Third image data

[0279] 310 Case feature data

[0280] 311 First feature data

[0281] 312 Second feature data

[0282] 313 Third feature data

[0283] 320 Program

[0284] 321 cognitive impairment diagnosis program

[0285] 322 viewpoint data

[0286] 323 distribution map data

[0287] 324 database

Claims

1. A cognitive dysfunction diagnosis apparatus comprising: an acquisition unit that acquires a distribution map that shows a distribution of a viewpoint of a subject with respect to a diagnosis image; and a diagnosis unit that diagnoses cognitive dysfunction based on the distribution map, wherein the diagnosis image includes, in chronological order, a first image and an evaluation image, the first image has a first region and a plurality of second regions, the first region contains a memorization image that is visually emphasized, and the second regions contain normal images that are not emphasized, the evaluation image has a third region and the plurality of second regions, the third region contains an evaluation image that is an image similar to the memorization image and is displayed without visual emphasis at the same position as the memorization image, the first image includes, in chronological order, a first memorization image and a second memorization image, the first memorization image has the first region and the plurality of second regions, and the second memorization image has the first region and does not have the plurality of second regions, the diagnosis unit calculates a fixation rate of the third region in the distribution map with respect to the evaluation image, and diagnoses that there is a suspicion of memory disorder when the fixation rate is lower than a threshold value.

2. The cognitive dysfunction diagnosis apparatus according to claim 1, wherein the diagnosis image includes another image that is displayed between the first image and the evaluation image.

3. The cognitive dysfunction diagnosis apparatus according to claim 2, wherein the diagnosis image includes an image in which the first image and the another image are repeated two or more times.

4. A cognitive dysfunction diagnosis apparatus comprising: an acquisition unit that acquires a distribution map that shows a distribution of a viewpoint of a subject with respect to a diagnosis image; and a diagnosis unit that diagnoses cognitive dysfunction based on the distribution map, wherein the diagnosis image has a first region and a plurality of second regions, the first region contains a correct answer string image for diagnosis, and the second regions contain incorrect answer string images, the diagnosis unit calculates a fixation rate of the first region in the distribution map with respect to the diagnosis image, and diagnoses that there is a suspicion of aphasia when the fixation rate is lower than a threshold value.

5. The cognitive dysfunction diagnosis apparatus according to claim 4, wherein the diagnosis image has an image region, a question region, the first region, and the second regions, the image region contains an image that shows a known object, the question region contains a question sentence that asks a name of the object, the first region contains a string image that shows the name of the object, and the second regions contain string images other than the name of the object.

6. The cognitive dysfunction diagnosis apparatus according to claim 4, wherein the diagnosis image has the first region and the second regions, the first region contains a meaningful string, and the second regions contain meaningless strings.

7. The cognitive dysfunction diagnosis apparatus according to claim 6, wherein the diagnosis image contains a plurality of characters that are arranged in a matrix shape. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ The first region corresponds to a row or column composed of meaningful character strings, The second region corresponds to a row or column composed of meaningless character strings.

8. A cognitive dysfunction diagnosis program recording medium recording a cognitive dysfunction diagnosis program which causes a computer to execute processing of obtaining a distribution map showing a distribution of a viewpoint of a subject with respect to a diagnosis-use image, and diagnosing cognitive dysfunction based on the distribution map, The diagnosis-use image includes a first image and an evaluation-use image in time series, The first image has a first region and a plurality of second regions, the first region contains a memorized image displayed with visual emphasis, and the second regions contain normal images displayed without emphasis, The evaluation-use image has a third region and the plurality of second regions, the third region contains an evaluation-use image, the evaluation-use image is an image similar to the memorized image and is displayed without visual emphasis at the same position as the memorized image, The first image includes a first memorized image and a second memorized image in time series, the first memorized image has the first region and the plurality of second regions, and the second memorized image has the first region and does not have the plurality of second regions, The cognitive dysfunction diagnosis program causes the computer to execute processing of: calculating a fixation rate of the third region in a distribution map with respect to the evaluation-use image, diagnosing as suspected of having a memory disorder when the fixation rate is lower than a threshold value.

Images