Cognitive impairment diagnostic device and cognitive impairment diagnostic program

Abstract

A cognitive impairment diagnostic device (1) comprises an acquisition unit (41) for acquiring a distribution map indicating a distribution of view points of a subject with respect to a diagnosis video, and a diagnostic unit (39), the diagnosis video including, in time order: first video having a first region including a visually highlighted memorization image, and a plurality of second regions including normal images displayed without highlighting; and evaluation video having a third region including an evaluation image that is similar to the memorization image and that is displayed without visual highlighting in the same position as the memorization image, and a plurality of second regions, and the diagnostic unit (39) calculating a gaze rate for the third region in the distribution map with respect to the evaluation video and diagnosing suspected cognitive impairment when the gaze rate is lower than a threshold value.

Description

[Technical field]

[0001] The present invention relates to a cognitive impairment diagnostic device and a cognitive impairment diagnostic program for diagnosing cognitive impairment. [Background technology]

[0002] Conventionally, the following techniques are related to devices for diagnosing cognitive impairment.

[0003] Patent Documents 1 and 2 propose a medical diagnostic device using gaze detection that detects the head and gaze movements of a subject and can objectively diagnose diseases related to brain function.

[0004] Patent Document 3 proposes a system for detecting eye movements of a subject for diagnosing neurological disorders.

[0005] Patent Documents 4 and 5 propose visual inspection charts for examining optic nerve disorders that appear due to diseases of the eye, such as the retina or optic nerve, or intracranial diseases.

[0006] Patent Documents 6 to 9 propose an autism diagnosis support system that uses a gaze detection unit equipped with at least an imaging camera section to diagnose autism in a subject.

[0007] Patent Document 10 proposes a brain function disorder diagnosis support device that detects the line of sight and pupils of a subject to judge the possibility of the subject having a brain disorder.

[0008] Patent Document 11 proposes a diagnostic support device that can effectively support the diagnosis of visual perception disorders.

[0009] Patent Document 12 proposes an eye movement measuring device and method capable of detecting the initial time of eye movement even from a rough image.

[0010] Patent Document 13 proposes a cognitive impairment diagnostic device that is simple, low-cost, objective, quantitative, and versatile. [Prior art documents] [Patent documents]

[0011] [Patent Document 1] Japanese Patent Application Publication No. 6-70884 [Patent Document 2] Japanese Patent Application Publication No. 6-70885 [Patent Document 3] Special Publication No. 2016-523112 [Patent Document 4] Patent No. 4560801 [Patent Document 5] Patent No. 4116354 [Patent Document 6] Patent No. 5926210 [Patent Document 7] Patent No. 5912351 [Patent Document 8] Patent No. 5761048 [Patent Document 9] Patent No. 5761049 [Patent Document 10] Patent No. 5817582 [Patent Document 11] JP 2017-158866 A [Patent Document 12] JP 2017-176302 A [Patent Document 13] International Publication No. 2019 / 098173 Summary of the Invention [Problem to be solved by the invention]

[0012] In general, cognitive function tests (neuropsychological tests) in the form of face-to-face interviews have traditionally been used to diagnose cognitive impairment and dementia. In cognitive function tests, the examiner asks the subject several questions to evaluate cognitive function, scores the contents of the answers given verbally, in writing, or by drawing figures, and calculates a cognitive function score. This method has the problem that the test takes a long time. For example, even the simplest Mini-Mental State Exam (MMSE) takes about 15 to 30 minutes. There is also the problem that the psychological stress of the subject is very large due to the nature of the interview format. In addition, there is also the problem that the scores vary widely due to differences in the skills of the examiners who conduct the interviews and the interpretation of the answers may differ depending on the examiner.

[0013] In order to solve these problems, the present inventors have proposed a cognitive dysfunction diagnostic device in Patent Document 13. Furthermore, a new cognitive dysfunction diagnostic device is desired.

[0014] The present invention aims to provide a cognitive impairment diagnostic device and a cognitive impairment diagnostic program that use a new diagnostic method that combines simplicity, low cost, objectivity, quantitativeness, and versatility in diagnosing cognitive impairment. [Means for solving the problem]

[0015] A cognitive impairment diagnosis device according to one embodiment of the present invention includes an acquisition unit that acquires a distribution map showing the distribution of a subject's gaze points on a diagnostic video, and a diagnosis unit that diagnoses cognitive impairment based on the distribution map, wherein the diagnostic video includes a first video having a first region including a memorized image displayed with visual highlighting and a plurality of second regions including normal images displayed without highlighting, and an evaluation video having, in chronological order, a third region including an evaluation image similar to the memorized image and displayed without visual highlighting in the same position as the memorized image, and the plurality of second regions, and the diagnosis unit calculates a gaze rate of the third region in the distribution map for the evaluation video, and diagnoses a suspected memory disorder if the gaze rate is lower than a threshold value.

[0016] A cognitive impairment diagnostic program according to one embodiment of the present invention is a cognitive impairment diagnostic program executed by a computer that acquires a distribution map showing the distribution of a subject's gaze points with respect to a diagnostic image and diagnoses cognitive impairment based on the distribution map, wherein the diagnostic image includes a first image having the first region including a memorized image displayed with visual highlighting and a plurality of second regions including normal images displayed without highlighting, and an evaluation image having a third region including an evaluation image similar to the memorized image and displayed in the same position as the memorized image without visual highlighting, and the plurality of second regions, in chronological order, and the cognitive impairment diagnostic program causes the computer to calculate a gaze rate of the third region in the distribution map for the evaluation image, and diagnose a suspected memory impairment if the gaze rate is lower than a threshold value. Effect of the Invention

[0017] A cognitive impairment diagnostic device and a cognitive impairment diagnostic program according to one embodiment of the present invention can diagnose cognitive impairment with simplicity, low cost, objectivity, quantitativeness, and versatility. [Brief description of the drawings]

[0018] [Figure 1A] FIG. 1A is a block diagram showing an example of the configuration of a cognitive impairment diagnosis device according to an embodiment. [Figure 1B] FIG. 1B is a block diagram showing another example of the configuration of the cognitive impairment diagnosis device according to the embodiment. [Figure 2A] FIG. 2A is a diagram showing an example of the appearance of a cognitive impairment diagnostic device according to an embodiment. [Figure 2B] FIG. 2B is a diagram showing another example of the appearance of the cognitive impairment diagnostic device according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of the contents stored in the storage unit according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of case characteristic data according to the embodiment. [Diagram 5]FIG. 5 is a flowchart showing an example of a diagnostic process performed by the cognitive impairment diagnostic device according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of the first diagnostic process according to the embodiment. [Figure 7] FIG. 7 is a diagram showing an example of a diagnostic image etc. in the first diagnostic processing of FIG. [Figure 8] FIG. 8 is a diagram showing an example of a distribution map obtained in the first diagnostic processing for a subject without memory impairment. [Figure 9] FIG. 9 is a diagram showing an example of a distribution map obtained in the first diagnostic processing for a subject with memory disorder. [Figure 10] FIG. 10 is a flowchart showing an example of the second diagnostic process according to the embodiment. [Figure 11] FIG. 11 is a diagram showing an example of a diagnostic image using linguistic instructions in the second diagnostic processing of FIG. [Figure 12] FIG. 12 is a diagram showing another example of a diagnostic image using linguistic instructions in the second diagnostic processing of FIG. [Figure 13] FIG. 13 is a diagram showing an example of a distribution map obtained in the second diagnostic processing using FIG. 11 for a subject without aphasia. [Figure 14] FIG. 14 is a diagram showing an example of a distribution map obtained in the second diagnostic processing using FIG. 11 for a subject with aphasia. [Figure 15] FIG. 15 is a diagram showing a first example of a diagnostic image that does not use linguistic instructions in the second diagnostic processing of FIG. [Figure 16] FIG. 16 is a diagram showing an example of an evaluation video corresponding to FIG. [Figure 17] FIG. 17 is a diagram showing an example of a distribution map obtained in the second diagnostic processing using FIG. 15 for a subject without aphasia. [Figure 18] FIG. 18 is a diagram showing an example of a distribution map obtained in the second diagnostic processing using FIG. 15 for a subject with aphasia. [Figure 19]FIG. 19 is a diagram showing a second example of a diagnostic image that does not use language instructions in the second diagnostic processing of FIG. [Figure 20] FIG. 20 is a diagram showing an example of an evaluation video corresponding to FIG. [Figure 21] FIG. 21 is a diagram showing an example of a distribution map obtained in the second diagnostic processing using FIG. 20 for a subject without aphasia. [Figure 22] FIG. 22 is a diagram showing an example of a distribution map obtained in the second diagnostic processing using FIG. 20 for a subject with aphasia. [Figure 23] FIG. 23 is a flowchart showing an example of the third diagnostic process in the embodiment. [Figure 24] FIG. 24 is a diagram showing an example of a diagnostic image in the third diagnostic processing of FIG. [Diagram 25] FIG. 25 is a diagram showing an example of an evaluation image in the third diagnostic processing of FIG. [Figure 26] FIG. 26 is a diagram showing an example of a distribution map in the third diagnostic processing using FIG. 24 for a subject without cognitive impairment. [Figure 27] FIG. 27 is a diagram showing the correlation between the gaze rate in the instruction sentence region in FIG. 25 and the MMSE score in the third diagnostic processing. [Figure 28] FIG. 28 is a diagram showing the correlation between the gaze rate of the off-target incorrect image in FIG. 25 and the MMSE score in the third diagnostic process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] Hereinafter, the embodiment will be specifically described with reference to the drawings.

[0020] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component arrangement and connection forms, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the scope of the claims. Furthermore, among the components in the following embodiments, components that are not described in an independent claim showing a top concept are described as optional components.

[0021] In addition, each drawing is a schematic diagram and is not necessarily a precise illustration. In addition, in each drawing, the same components are denoted by the same reference numerals.

[0022] (Embodiment) Hereinafter, a cognitive impairment diagnosis device and a cognitive impairment diagnosis program according to an embodiment will be described with reference to the drawings.

[0023] [1. Configuration of cognitive impairment diagnostic device] Fig. 1A is a block diagram showing a configuration example of a cognitive impairment diagnosis device according to an embodiment, and Fig. 2A is a diagram showing an external appearance example of the cognitive impairment diagnosis device according to an embodiment.

[0024] 1A, the cognitive impairment diagnostic device 1 includes a display unit 10, an imaging device 20, and a PC (Personal Computer) 30. The cognitive impairment diagnostic device 1 shows a configuration example in which a commercially available general PC 30 is used as a main control device, and the display unit 10 and the imaging device 20 are further added to the PC 30.

[0025] The display unit 10 is a flat panel display having a display surface 11, and displays diagnostic images for cognitive impairment on the display surface 11. As shown in FIG. 2A, the display unit 10 is a large liquid crystal display or an organic EL display that is easy for elderly people to see in order to show the diagnostic images to the subject. The display unit 10 may be a monitor for a personal computer, or a commercially available large TV set. The display unit 10 may be configured with a screen as the display surface 11 and a projector instead of a flat panel display.

[0026] The imaging device 20 is a module that can be attached to the display unit 10, and includes at least an imaging unit 21 for capturing an image of the subject's eye and a light source unit 24.

[0027] The imaging unit 21 is a stereo camera having a camera 22 and a camera 23. The cameras 22 and 23 may each be, for example, an infrared camera. In another example, the cameras 22 and 23 may each be a visible light camera. The imaging unit 21 may be a single camera instead of a stereo camera, or may be three or more cameras.

[0028] The light source unit 24 includes a light source 25 and a light source 26 that irradiate the subject with infrared light as illumination light. Each of the light source 25 and the light source 26 may include, for example, one or more infrared LEDs (Light Emitting Diodes). In another example, each of the light source 25 and the light source 26 may include one or more white LEDs (Light Emitting Diodes). Note that, if the lighting environment of the subject is sufficiently bright, the imaging device 20 does not need to include the light source unit 24. Also, the imaging device 20 may be attached to the top of the display unit 10, or may be divided and attached to the left and right.

[0029] The PC 30 includes a processor 31, a memory unit 32, an input unit 33, an output unit 34, a display unit 35, an interface unit 36, a detection unit 37, a creation unit 38, and a diagnosis unit 39. Among the functional blocks shown in Fig. 1A, the processor 31, the memory unit 32, the input unit 33, the output unit 34, the display unit 35, and the interface unit 36 ​​are configured by general hardware and software of a commercially available computer. The other functional blocks, that is, the detection unit 37, the creation unit 38, and the diagnosis unit 39, are components that are mainly realized by the processor 31 executing the cognitive impairment diagnosis program in this embodiment.

[0030] The processor 31 is a so-called CPU (Central Processing Unit) that executes a program stored in the storage unit 32.

[0031] The storage unit 32 stores a program executed by the processor 31 and data processed by the processor 31. Furthermore, the storage unit 32 constitutes a database 324. The programs stored in the storage unit 32 include various firmware, OS (Operating System), driver software, and other software, as well as the cognitive dysfunction diagnosis program in this embodiment. The data stored in the storage unit 32 includes diagnostic video data, case feature data, viewpoint data, distribution map data, and the like. The diagnostic video data is a still image or a moving image created for diagnosing cognitive dysfunction. The case feature data is data showing the characteristics of the viewpoint distribution corresponding to a typical example of cognitive dysfunction. The viewpoint data is time-series data showing the position and time of the viewpoint detected by the detection unit 37. The distribution map is created by the creation unit 38, and is a two-dimensional distribution of the viewpoints, which are plotted in real time on a two-dimensional plane in accordance with the viewpoint data.

[0032] The storage unit 32 includes a main memory or primary memory configured with a dynamic random access memory (DRAM) or the like, an auxiliary memory or secondary memory configured with a hard disk drive (HDD) device or a solid state drive (SSD) device, and a cache memory. In other words, in this document, the storage unit 32 is used as a general term for components having the function of storing programs and data.

[0033] The input unit 33 includes, for example, a keyboard, a mouse, a track pad, etc., and accepts operations by an operator.

[0034] The output unit 34 is, for example, a speaker, and outputs sound.

[0035] The display unit 35 is, for example, a liquid crystal display, and displays a diagnostic image with a distribution map superimposed thereon for the monitor of a user (here, a person performing an examination).

[0036] The interface unit 36 ​​has a function of connecting the display unit 10 and the imaging device 20 via a cable to communicate with each other. The interface unit 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 unit 36 ​​connects the display unit 10 via an HDMI (registered trademark) cable, and connects the imaging unit 21 and the light source unit 24 via a USB cable.

[0037] The detection unit 37 detects the subject's viewpoint on the display surface 11 in a time series manner based on the image captured by the imaging unit 21. For example, the detection unit 37 detects the subject's line of sight from the image captured by the imaging unit 21, and detects the coordinates of the point where the line of sight intersects with the display surface 11 as the position of the subject's viewpoint on the display surface 11. The detection of the viewpoint position is performed periodically. The period may be set between several tens of milliseconds and several hundreds of milliseconds, for example, 100 milliseconds. The detection unit 37 generates, for example, a set of coordinate data (x, y, t) including time in real time as viewpoint data representing the position of the viewpoint in a time series. Here, x and y are coordinates of a plane (for example, the display surface 11 or the diagnostic image), and t is time.

[0038] The creation unit 38 creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37. The distribution map is, for example, a diagram in which marks (for example, colored dots) corresponding to the above coordinate data (x, y, t) are plotted on a two-dimensional plane, and is superimposed in real time on the diagnostic image displayed on the display unit 35 of the PC 30. The above marks may be displayed, for example, brighter for the most recent viewpoint.

[0039] The diagnosis unit 39 diagnoses the cognitive function of the subject by determining whether the distribution map has the characteristics of the case characteristic data.

[0040] As described above, the cognitive impairment diagnostic device 1 comprises a display unit 10 that displays a diagnostic image for cognitive impairment on the display surface 11, an imaging unit 21 that images the eyes of the subject, a detection unit 37 that chronologically detects the gaze point of the subject on the display surface 11 based on the image captured by the imaging unit 21, a creation unit 38 that creates a distribution map that indicates the distribution of the gaze points detected by the detection unit 37, a memory unit 32 that stores case feature data 310 that indicates characteristics of the gaze point distribution corresponding to typical cases of cognitive impairment, and a diagnosis unit 39 that diagnoses the cognitive impairment of the subject by determining whether the distribution map has the characteristics of the case feature data 310.

[0041] According to this configuration, the diagnosis of cognitive function by the cognitive impairment diagnosing device 1 can be simple, low-cost, objective, quantitative, and versatile.

[0042] It should be noted that the PC 30 shown in FIGS. 1A and 2A may be a notebook computer, a tablet computer, or a desktop computer.

[0043] Next, another configuration example of the cognitive impairment diagnosis device 1 will be described.

[0044] Fig. 1B is a block diagram showing another example of the configuration of the cognitive impairment diagnostic device 1 in the embodiment. Fig. 1B is different from Fig. 1A in that the display unit 10, the imaging unit 20, the detection unit 37, and the creation unit 38 are deleted, and an acquisition unit 41 is added. Below, the same points will be avoided from being described repeatedly, and the differences will be mainly described.

[0045] The display unit 10, the imaging unit 20, the detection unit 37, and the creation unit 38 in Fig. 1A correspond to an eye tracking configuration for creating distribution map data showing the distribution of the subject's gaze points with respect to the diagnostic image. In the configuration example of Fig. 1B, the configuration for creating the distribution map data is separated from Fig. 1A, and a core configuration for diagnosing cognitive dysfunction is shown.

[0046] The acquisition unit 41 acquires distribution map data created by an external device, and stores it in the storage unit 32. The distribution map data is used as a diagnostic target for cognitive impairment.

[0047] The acquisition unit 41 may acquire distribution map data, diagnostic image data, case feature data, and viewpoint data from an external device. For example, if diagnostic image data, case feature data, and the like corresponding to the distribution map data are already stored in the storage unit 32, the acquisition unit 41 may acquire only the distribution map data. For example, the acquisition unit 41 may acquire distribution map data, diagnostic image data, case feature data, and viewpoint data corresponding to the first subject, and acquire only distribution map data for the second and subsequent subjects.

[0048] The cognitive impairment diagnostic device 1 in FIG. 1A may further include an acquisition unit 41 in FIG. 1B.

[0049] In addition, the external device may have a function of creating a distribution map showing the distribution of the subject's gaze over time by eye tracking, and the eye tracking method and the configuration for creating the distribution map are not important. In addition, the external device may be connected to the cognitive impairment diagnostic device 1 via the Internet, a telephone line, or a dedicated line.

[0050] The cognitive impairment diagnostic device 1 does not have to be a device having a large display unit 10 like the external appearance example of FIG. 2A, and can be configured as a smart device.

[0051] Fig. 2B is a diagram showing another example of the appearance of the cognitive impairment diagnostic device 1. The cognitive impairment diagnostic device 1 in the figure is a tablet-type smart device, and includes the same components as those in Fig. 1A, such as a display unit 10 and an imaging unit 22.

[0052] 2B serves as both the display unit 10 in FIG. 2A and the display unit 35 of the PC 30. A touch panel is formed on the surface of the display surface 11.

[0053] The imaging unit 22 is a front camera of the smart device. The imaging unit 22 is used for eye tracking by detecting the position and movement of the eyes using, for example, face recognition technology. In this case, the light source unit 24 does not need to be provided. An image is displayed to the subject on the display unit 10. In addition, a cognitive dysfunction diagnosis program is executed by a processor inside the tablet device according to the examiner's touch panel operation. For example, the smart device diagnoses cognitive dysfunction by having the subject and the examiner alternately use the smart device, or by having the examiner show the display unit 10 to the subject.

[0054] The cognitive impairment diagnostic device 1 as such a smart device may be configured as a tablet terminal, a smartphone, a notebook computer, etc. The cognitive impairment diagnostic program is created as an app for these devices. The cognitive impairment diagnostic device 1 in FIG. 2B may be configured to include the same components as those in FIG. 1B.

[0055] [1.1 Programs and data in memory unit 32] Next, the programs and data stored in the storage unit 32 will be described.

[0056] 3 is a diagram showing an example of the contents stored in the storage unit 32 in the embodiment. In the figure, the storage unit 32 stores diagnostic image data 300, case feature data 310, a program 320, viewpoint data 322, and distribution map data 323. The program 320 includes a cognitive impairment diagnosis program 321. Furthermore, the storage unit 32 includes a database 324.

[0057] Diagnostic video data 300 is a collection of multiple video data including first video data 301 to second video data 303. Each of the multiple video data is video created for diagnosing the presence or absence or the degree of cognitive dysfunction, or video created for distinguishing cases of cognitive dysfunction.

[0058] The case feature data 310 is data indicating features of gaze distribution corresponding to typical cases of cognitive dysfunction, and is a collection of multiple feature data including first feature data 311 to third feature data 313. The first feature data 311 to third feature data 313 correspond to the first video data 301 to third video data 304, respectively.

[0059] The program 320 includes various firmware, OS (Operating System), driver software, and other software, and a cognitive impairment diagnosis program 321. The cognitive impairment diagnosis program 321 is a program executed by a computer, that is, the PC 30, and causes the computer to execute the following: displaying a diagnostic image for cognitive impairment on the display surface 11, capturing an image of the subject's eyes with the imaging unit 21, detecting the subject's viewpoint on the display surface 11 in time series based on the image captured by the imaging unit 21, creating a distribution map showing the distribution of the detected viewpoints, and determining whether the distribution map has the characteristics of the case feature data, thereby diagnosing the cognitive impairment of the subject. Among these, the detection unit 37 functions as the PC 30 detecting the subject's viewpoint on the display surface 11 in time series based on the image captured by the imaging unit 21. The creation unit 38 functions as the PC 30 creating a distribution map showing the distribution of the detected viewpoints. The diagnosis unit 39 functions as the PC 30 diagnosing the cognitive impairment of the subject by determining whether the distribution map has the characteristics of the case feature data.

[0060] The viewpoint data 322 is time-series data indicating the position and time of the viewpoint detected by the detection unit 37, and is, for example, a set of coordinate data (x, y, t) including the time already described.

[0061] The distribution map data 323 is data indicating the distribution map already described.

[0062] The database 324 accumulates various data and the like. Note that the database 324 does not have to be a part of the storage unit 32, and may be provided outside the storage unit 32. Furthermore, the database 324 may be connected via a network such as the Internet.

[0063] In addition to the programs and data shown in FIG. 3, the storage unit 32 also stores diagnostic data indicating the diagnosis results of the subject, viewpoint data 322 for each subject, distribution map data 323, and data associating the diagnostic data.

[0064] Next, a specific example of the case characteristic data 310 will be described.

[0065] 4 is a diagram showing an example of case characteristic data 310 in the embodiment. The case characteristic data 310 in the figure includes first characteristic data 311 to third characteristic data 313. In the figure, the characteristics of each of the first characteristic data 311 to third characteristic data 313 and the corresponding cases of cognitive dysfunction are described.

[0066] The first feature data 311 associates the first feature with memory disorder. The first feature is a feature that typically appears in a person with a memory disorder. Specifically, the first feature indicates that when a person with a memory disorder is presented with a memorization video and then a memory recall image, the gaze rate on the memory recall image is low in a distribution map of viewpoints. The first feature is premised on showing the subject the memorization video, which is a diagnostic video based on the first video data 301, and further presenting an image for memory recall.

[0067] The second feature data 312 associates the second feature with aphasia. That is, the second feature is a feature that typically appears in a person with aphasia. Specifically, the second feature indicates that the fixation rate on the correct answer is small in a distribution map of viewpoints for an image having a plurality of options including a correct image and an incorrect image. This second feature is based on the premise that the second image, which is a diagnostic image based on the second image data 302, is shown to the subject.

[0068] The third feature data 313 associates the third feature with cognitive impairment. That is, the third feature is a feature that typically appears in a person with cognitive impairment. Specifically, the third feature indicates that the gaze rate on an incorrect image is not small in a distribution map of gaze points for an image having a plurality of options including a correct image and an incorrect image. This third feature is based on the premise that the third image, which is a diagnostic image based on the third image data 303, is shown to the subject.

[0069] [1.2 Processing example of cognitive impairment diagnosis device 1] An example of processing performed by the cognitive impairment diagnosis device 1 according to the embodiment configured as above will be described below.

[0070] FIG. 5 is a flowchart showing an example of a diagnostic process performed by the cognitive impairment diagnostic device 1 according to the embodiment.

[0071] As shown in Fig. 5, the cognitive impairment diagnosis device 1 sequentially executes a first diagnostic process (S10) to a third diagnostic process (S30). The diagnostic process example in Fig. 5 is mainly realized by the PC 30 executing the cognitive impairment diagnosis program 321.

[0072] The first diagnostic process is a diagnostic process that uses the first video data 301 and the first feature data 311. The second diagnostic process is a diagnostic process that uses the second video data 302 and the second feature data 312. The third diagnostic process is a diagnostic process that uses the third video data 303 and the third feature data 313.

[0073] The time required for each of the first to third diagnostic processes is about 0.5 minutes to several minutes.

[0074] The order of the first diagnostic process to the third diagnostic process may be different from that shown in FIG.

[0075] In addition, in FIG. 5, at least one of the first diagnostic process to the third diagnostic process may be selected and executed.

[0076] Incidentally, before the start of the example of the diagnosis process in FIG. 5, a calibration process for the viewpoint detection may be performed.

[0077] [2.1 First diagnostic process] Next, a detailed description will be given of the first diagnostic process in step S10 of Fig. 5. The first diagnostic process diagnoses a memory disorder by utilizing the above-mentioned first characteristic.

[0078] 6 is a flow chart showing an example of the first diagnostic process (S10) in the embodiment, and FIG 7 is a diagram showing an example of a diagnostic image etc. in the first diagnostic process of FIG.

[0079] As shown in FIG. 6, the PC 30 first starts detection of the viewpoint by the detection unit 37 (S61), and executes loop 1 (S62 to S65) which is a process for having the subject memorize an area of ​​a specific image or character string.

[0080] In loop 1, PC 30 displays a diagnostic image to be memorized (S63), and immediately thereafter displays another image (S64). The number of times N that loop 1 is repeated may be 2 or more.

[0081] In step S63, a memorization image is presented that causes the subject to unconsciously focus his / her gaze on a specific figure or area on the screen, i.e., without any verbal instruction. In the example of FIG. 7, the diagnostic image includes memorization image b11 and memorization image b12 in chronological order. The memorization image b11 is displayed for Ta seconds, and immediately thereafter, the memorization image b12 is displayed for Tb seconds. For example, the time Ta may be about 5 seconds, and the time Tb may be about 3 seconds.

[0082] The memorization image b11 includes a total of 20 clock images, 10 at the top and 10 at the bottom of the display surface 11. Of the 20 clock images, only the clock image M1, the second from the right on the bottom row, rotates. This rotation may be the rapid rotation of the clock hands or the rotation of the clock image itself. The other 19 clock images are stationary. This is to focus the subject's attention on the clock image M1.

[0083] In the memorization image b12, a dotted frame is added to emphasize only the rotating clock image M1, and other clock images disappear, so that the subject's attention is focused on the clock image M1 without using verbal instructions.

[0084] In step S64, another image is displayed for Tc seconds to provide an interval for memorization. Tc may be, for example, about 60 seconds.

[0085] The display times Td, Te, and Tf in the second repetition in FIG. 7 may be the same as Ta, Tb, and Tc in the first repetition, or may be different times.

[0086] By repeating loop 1 in this manner, the subject unconsciously focuses his or her attention on the clock image M1 on the screen, thereby strengthening the memory.

[0087] After loop 1, PC 30 displays evaluation image b3 for Tg seconds to evoke memory (S66). Time Tg may be, for example, about 5 seconds. Evaluation image b3 in FIG. 7 has clock image M3 instead of clock image M1. That is, evaluation image b3 is configured so that all 20 clock images are still and do not draw attention to any particular figure.

[0088] In subjects without memory disorders, the content seen in the memorization images (memorization images b11 and b12) remains in their memory, so they unconsciously direct their gaze to the specific figure or area on which their attention was focused in the memorization images. The specific figure or area is the figure or area memorized in the memorization images, and in Figure 7, it is the still clock image M3 in the same position as the clock image M1.

[0089] In contrast, subjects with memory disorders do not retain memories or memorize them, so they do not unconsciously direct their gaze toward clock image M3, but tend to look at it randomly.

[0090] Therefore, the PC 30 sets a specific figure or area in the evaluation image for memory recall as a region of interest as a quantitative evaluation of memory ability, and calculates the gaze rate Vb within the region of interest as a score of memory ability (S67). Here, the gaze rate Vb may be the rate (%) of the gaze point existing in the region of the clock image M3, which is the region of interest, in the distribution pattern of the gaze points corresponding to the evaluation image.

[0091] Furthermore, the PC 30 judges whether the calculated gaze rate Vb is greater than the threshold value th1 (S68). If it is judged that the gaze rate Vb is greater than the threshold value th1, the subject is diagnosed as having no memory disorder (S69).

[0092] Furthermore, the PC 30 judges whether the calculated gaze rate Vb is smaller than the threshold value th1 and larger than the threshold value th2 (S70). If it is judged that the gaze rate Vb is smaller than the threshold value th1 and larger than the threshold value th2, the subject is diagnosed as having a suspected memory disorder (S71).

[0093] Furthermore, the PC 30 judges whether the calculated gaze rate Vb is smaller than the threshold value th2 (S72). If it is judged that the gaze rate Vb is smaller than the threshold value th2, the subject is diagnosed as having a memory disorder (S73).

[0094] The threshold value th1 and the threshold value th2 can be appropriately set by calculating the gaze rate Vb for a plurality of subjects whose presence or absence of memory disorders is known in advance.

[0095] Note that Figure 6 shows an example of outputting three types of diagnostic results: "No memory impairment," "Suspected memory impairment," and "Memory impairment." However, the present invention is not limited to this, and the diagnostic results may be two types indicating the presence or absence of a memory impairment, or four or more types indicating the presence or absence and the degree of a memory impairment.

[0096] The flowchart in Fig. 6 may have a step of acquiring a distribution map showing the distribution of the subject's viewpoints with respect to the diagnostic image, instead of steps S61 to S66. The flowchart in which the steps are replaced with the acquiring step can be easily implemented by the PC 30 in Fig. 1B, or may be implemented by the PC 30 in Fig. 1A. Next, an example of the distribution map obtained in the first diagnostic process will be described.

[0097] Fig. 8 is a diagram showing an example of a distribution map obtained by the first diagnostic process for a subject with no memory disorder and an MMSE score of 30. Fig. 9 is a diagram showing an example of a distribution map obtained by the first diagnostic process for a subject with a memory disorder and an MMSE score of 14. The MMSE score is out of 30, with 30 representing a healthy subject and a lower score representing a more severe cognitive impairment.

[0098] 8 and 9 show examples in which distribution maps are superimposed on the memorization image b11, the memorization image b12, and the evaluation image b3 displayed on the display unit 35 of the PC 30. FIG.

[0099] As shown in Figures 8 and 9, in the memorization image b11 and the memorization image b12, the subject's viewpoint is centered on the clock image M1. The memorization image b11 and the memorization image b12 are diagnostic images having a first region including the clock image M1, which is a memorization image displayed with visual emphasis, and a plurality of second regions including still clock images, which are normal images displayed without emphasis. As shown in Figures 8 and 9, the viewpoint of the memorization image is almost the same for subjects without memory impairment and subjects with memory impairment, and the viewpoint is centered on the clock image M1 in the first region.

[0100] In the evaluation video b3 of FIG. 8, the gaze distribution of the person without memory impairment shows a high gaze rate of 81% on the clock image M3 in the region of interest, which is an indication of the effect of memorization.

[0101] In contrast to this, in the evaluation video b3 of FIG. 9, the gaze distribution of the person with memory impairment shows no effect of memorization, with the gaze rate on the clock image M3 in the region of interest being low at 0%.

[0102] In the first diagnostic process, the degree of memory of the subject is quantitatively evaluated using the gaze rate within a region of interest in an evaluation image that evokes a memory as a memory score, and memory disorder is diagnosed.

[0103] As described above, the cognitive dysfunction diagnosis device that performs the first diagnostic processing includes an acquisition unit 41 that acquires a distribution map showing the distribution of the subject's gaze points on the diagnostic image, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and the diagnostic image includes a first image having a first region including a memorized image displayed with visual highlighting and a plurality of second regions including a normal image displayed without highlighting, and an evaluation image having a third region including an evaluation image similar to the memorized image and displayed without visual highlighting in the same position as the memorized image, and a plurality of second regions, in chronological order, and the diagnosis unit 39 calculates the gaze rate of the third region in the distribution map for the evaluation image, and if the gaze rate is lower than a threshold value, diagnoses a suspected memory disorder.

[0104] In addition, the cognitive dysfunction diagnosis device that performs the first diagnostic process includes a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's viewpoint on the display surface based on the image captured by the imaging unit 21, a creation unit 38 that creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and the diagnosis unit 39 displays a first video on the display unit 10, the first video having a first area including a memorized image that is displayed with visual highlighting and a plurality of second areas including normal images that are displayed without highlighting, and further displays an evaluation video on the display unit 10, the evaluation video having a third area including an evaluation image that is similar to the memorized image and is displayed without visual highlighting in the same position as the memorized image, and a plurality of second areas, calculates the gaze rate of the third area in the distribution map for the evaluation video, and diagnoses a suspected memory disorder if the gaze rate is lower than a threshold value.

[0105] This makes it possible to provide a cognitive impairment diagnostic device that is simple, low-cost, objective, quantitative, and versatile in diagnosing memory impairment among cognitive impairments. Moreover, the diagnosis can be made without using written or spoken instructions. It can also be performed on multiple subjects with different native languages. Furthermore, the diagnosis of memory impairment can be made in just a few minutes.

[0106] Here, the first image may include, in time order, a first memorization image having a first region and a plurality of second regions, and a second memorization image having the first region but not the plurality of second regions.

[0107] This can improve the subject's ability to remember the first area.

[0108] Here, the diagnostic image may include another image that is displayed between the first image and the evaluation image.

[0109] According to this, by providing a period in which other images are displayed between memorization and evaluation, it is possible to clarify more clearly whether or not the subject is able to maintain his / her memory, thereby increasing the reliability of the diagnostic results.

[0110] Here, the diagnostic image may include an image in which a first image and another image are repeated two or more times.

[0111] This makes it possible to improve the subject's ability to remember the first area through repetition.

[0112] In addition, the cognitive dysfunction diagnostic program that performs the first diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer that acquires a distribution map showing the distribution of the subject's gaze points on a diagnostic image and diagnoses cognitive dysfunction based on the distribution map, wherein the diagnostic image includes a first image having a first area including a memorized image displayed with visual highlighting and a plurality of second areas including a normal image displayed without highlighting, and an evaluation image having a third area including an evaluation image that is similar to the memorized image and displayed in the same position as the memorized image without visual highlighting, and a plurality of second areas, in chronological order, and the cognitive dysfunction diagnostic program calculates a gaze rate of the third area in the distribution map for the evaluation image, and causes the computer to diagnose a suspected memory disorder if the gaze rate is lower than a threshold value.

[0113] In addition, the cognitive dysfunction diagnostic program for performing the first diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer including a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's viewpoint on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and causes the computer to display a diagnostic image on the display unit 10 having a first region including a memorized image displayed with visual highlighting and a plurality of second regions including normal images displayed without highlighting, and further causes the display unit 10 to display an evaluation image having a third region including an evaluation image similar to the memorized image and displayed in the same position as the memorized image without visual highlighting, and a plurality of second regions, calculates the gaze rate of the third region in the distribution map for the evaluation image, and diagnoses suspected memory disorder if the gaze rate is lower than a threshold value.

[0114] [2.2.1 Second diagnostic process (example using linguistic instructions)] Next, an example of using linguistic instructions will be described for the second diagnostic process in step S20 in Fig. 5. The second diagnostic process diagnoses aphasia by utilizing the above-mentioned second feature.

[0115] Fig. 10 is a flowchart showing an example of the second diagnostic process in the embodiment, and Fig. 11 is a diagram showing an example of a diagnostic image using language instructions in the second diagnostic process of Fig. 10.

[0116] In FIG. 10, PC 30 first starts detection of the viewpoint by detector 37 (S101), displays a diagnostic image on display surface 11 (S102), and calculates a gaze rate Vc of a correct area in the diagnostic image (S103).

[0117] The diagnostic image here is an image having a first region including a correct character string image and a plurality of second regions including incorrect character string images.

[0118] 11, a diagnostic image c11 is displayed for Ta seconds, and then a diagnostic image c12 is displayed for Tb seconds. Each of the times Ta and Tb may be, for example, about 5 seconds to 10 and a few seconds.

[0119] The diagnostic image c11 has a clock image in the center surrounded by four areas that serve as options. The diagnostic image c12 differs from the diagnostic image c11 in that an instruction message is overlaid on the clock image in the center. The instruction message is "What is the name of this?" The four areas surrounding the instruction message are a first area containing the correct character string image "clock" and multiple second areas containing the incorrect character string images "tomato," "shrimp," and "clock."

[0120] In this way, the diagnostic image may have, for example, an image of a known object and a number of areas as options including correct and incorrect names for the object. The number of options is arbitrary. Also, an instruction message such as "What is the name of this?" is displayed. The instruction message prompts the subject to look at the correct option.

[0121] Instead of the two diagnostic images c11 and c12 as shown in FIG. 11, one diagnostic image c13 as shown in FIG. 12 may be displayed.

[0122] Furthermore, the PC 30 calculates a gaze rate Vc of the gaze to the first region including the correct character string image after displaying the diagnostic video (S103). Here, the gaze rate Vc may be a ratio (%) of the gaze to the first region in the distribution pattern of the gaze corresponding to the diagnostic video.

[0123] Next, the PC 30 judges whether the calculated gaze rate Vc is greater than a threshold value th3 (S104). If it is judged that the gaze rate Vc is greater than the threshold value th3, the subject is diagnosed as not having aphasia (S105).

[0124] Furthermore, the PC 30 judges whether the calculated gaze rate Vc is smaller than the threshold value th3 and larger than the threshold value th4 (S106). If it is judged that the gaze rate Vc is smaller than the threshold value th3 and larger than the threshold value th4, the subject is diagnosed as suspected of having aphasia (S107).

[0125] Furthermore, the PC 30 judges whether the calculated gaze rate Vc is smaller than a threshold value th4 (S108). If it is judged that the gaze rate Vc is smaller than the threshold value th4, the subject is diagnosed as having aphasia (S109).

[0126] The threshold value th3 and the threshold value th4 can be appropriately set by calculating the gaze rate Vc of a plurality of subjects whose presence or absence of aphasia is known in advance.

[0127] 10 may include a step of acquiring a distribution map showing the distribution of the subject's viewpoints with respect to the diagnostic image, instead of steps S101 and S102. The flowchart in which the steps are replaced with the acquiring steps can be easily implemented by the PC 30 in FIG. 1B, or may be implemented by the PC 30 in FIG. 1A.

[0128] Note that FIG. 10 shows an example in which three types of diagnostic results, "No aphasia," "Suspected aphasia," and "Aphasia," are output; however, the present invention is not limited to this, and the diagnostic results may be two types indicating the presence or absence of aphasia, or four or more types indicating the presence or absence and the severity of aphasia.

[0129] Next, an example of the distribution map obtained in the second diagnostic processing will be described.

[0130] Fig. 13 is a diagram showing an example of a distribution map obtained by the second diagnostic process for a subject without aphasia who has an MMSE score of 30. Fig. 14 is a diagram showing an example of a distribution map obtained by the second diagnostic process for a subject with aphasia who has an MMSE score of 14.

[0131] In the diagnostic image c12 in FIG. 13, the gaze point distribution of a person without aphasia shows a high gaze rate on the first region c111 including the correct character string image.

[0132] In contrast to this, in the diagnostic image c12 in FIG. 14, the gaze point distribution of the person with aphasia shows a low gaze rate on the first region including the correct character string image.

[0133] If the fixation rate Vc to the first area, which is the correct answer, exceeds the threshold th3, it is determined that the instruction sentence is correct, that is, the ability to name objects is normal. In other words, subjects without aphasia can look at the correct answer option for "clock" at a rate equal to or higher than the threshold. In contrast, subjects with aphasia look at options other than the correct answer at a relatively large rate. Therefore, in the second diagnostic process, the presence and severity of aphasia in the subject can be diagnosed using the fixation rate to the correct answer option as an index.

[0134] [2.2.2 Second diagnostic process (first example without using linguistic instructions)] The above-mentioned second diagnostic process uses language instructions. Next, a first example of the second diagnostic process that does not use language instructions will be described.

[0135] FIG. 15 is a diagram showing a first example of a diagnostic image that does not use linguistic instructions in the second diagnostic processing of FIG.

[0136] The diagnostic image c21 in FIG. 15 shows an example of the display on the display surface 11 for the subject, and has a correct area containing a meaningful character string and an incorrect area containing a meaningless character string. Specifically, the diagnostic image c21 includes a plurality of characters arranged in a matrix. The correct area corresponds to a row or column composed of a meaningful character string. The incorrect area corresponds to a row or column composed of a meaningless character string. The row containing the character string "dango" is the correct area. The other rows and columns are incorrect areas that do not contain a meaningful character string.

[0137] Fig. 16 is a diagram showing an example of an evaluation image corresponding to Fig. 15. Fig. 16 also shows an example of a display on the display unit 35 for the operator, and clearly shows a correct answer region c211 as an evaluation region for the diagnostic image c21 in Fig. 15 by a dashed frame.

[0138] Next, an example of the distribution of subject's viewpoints with respect to the diagnostic image c21 in FIG. 15 will be described.

[0139] Fig. 17 is a diagram showing an example of a distribution map obtained by the second diagnostic process using Fig. 15 for a subject without aphasia who has an MMSE score of 30. Also, Fig. 18 is a diagram showing an example of a distribution map obtained by the second diagnostic process using Fig. 15 for a subject with aphasia who has an MMSE score of 14.

[0140] In the diagnostic image c21 in FIG. 17, the gaze point distribution of a person without aphasia shows a high gaze rate on the correct answer area c211.

[0141] In contrast to this, in the diagnostic image c21 of FIG. 18, the distribution of gaze points of a person with aphasia tends to be random, regardless of the correct answer region c211.

[0142] In the diagnostic image c21, characters are arranged in a matrix or a grid. Only in certain parts of the image are there "character strings that have meaningful connections." The other parts do not have any meaningful connections no matter which way they are read.

[0143] When presented with such a matrix of character strings, subjects without aphasia are able to find strings of characters that make sense and unconsciously focus their gaze on those parts more frequently.

[0144] On the other hand, subjects with aphasia cannot find character strings with meaningful connections, so the fixation rate on specific parts is relatively low and they tend to look at the whole randomly. Therefore, in the second diagnostic process, it is possible to objectively diagnose aphasia by using as an index whether the fixation rate on parts of character strings with meaningful connections (i.e., correct answer areas) exceeds a threshold value.

[0145] In the diagnostic image c21 of Figure 15, a string of 3 x 3 hiragana characters is displayed. When the bottom three characters are read from left to right, they can be recognized as the meaningful string "dango" (rice dumplings). Any other combinations are basically meaningless strings of characters. No letters or sounds are presented as instructions to encourage the subject to find "dango," but in healthy subjects without aphasia, the rate of fixation on the meaningful string of characters "dango" unconsciously increases relatively.

[0146] In contrast, subjects with aphasia are unable to recognize these meaningfully connected strings of characters, and do not increase their gaze on "dango." Instead, they tend to look randomly at other areas or the entire screen.

[0147] [2.2.3 Second diagnostic process (second example without using linguistic instructions)] Furthermore, a second example in which no linguistic instruction is used in the second diagnostic process will be described.

[0148] Fig. 19 is a diagram showing a second example of a diagnostic video that does not use a linguistic instruction in the second diagnostic processing of Fig. 10. Fig. 20 is a diagram showing an example of an evaluation video corresponding to Fig. 19.

[0149] 19 shows an example of the display on the subject's display surface 11, and has a correct answer area including a meaningful character string and an incorrect answer area including a meaningless character string. Specifically, in the diagnostic image c31, the column including the character string "Tanabata" is the correct answer area, and the other rows and columns are incorrect answer areas that do not include a meaningful character string.

[0150] FIG. 20 shows an example of a display on the operator's display unit 35, in which a correct answer region c311 as an evaluation region is clearly indicated by a dashed frame for the diagnostic image c31 in FIG.

[0151] Next, an example of the distribution of subject's viewpoints with respect to the diagnostic image c31 in FIG. 19 will be described.

[0152] Fig. 21 is a diagram showing an example of a distribution map obtained by the second diagnostic process using Fig. 19 for a subject with MMSE score of 30 and no aphasia. Also, Fig. 22 is a diagram showing an example of a distribution map obtained by the second diagnostic process using Fig. 19 for a subject with MMSE score of 14 and aphasia.

[0153] In the diagnostic image c21 in Fig. 21, the gaze point distribution of a person without aphasia has a high gaze rate on the correct answer region c311. In contrast, in the diagnostic image c31 in Fig. 22, the gaze point distribution of a person with aphasia tends to be random, unrelated to the correct answer region c311.

[0154] In the diagnostic image c31 of FIG. 19, a 4 x 4 hiragana character string is displayed. When the second row of four characters from the right is read from top to bottom, it can be recognized as a meaningful string of characters, "Tanabata" (Star Festival). Any other combinations are basically meaningless strings. No instructions or voice prompts are presented to encourage the subject to find "Tanabata." Normal subjects without aphasia unconsciously increase their gaze rate relative to the meaningfully connected string of characters, "Tanabata." In contrast, subjects with aphasia cannot recognize this meaningfully connected string of characters, so their gaze rate does not increase on this area, and they look at other areas or the entire screen randomly.

[0155] As described above, the cognitive dysfunction diagnosis device 1 performing the second diagnostic processing includes an acquisition unit 41 that acquires a distribution map showing the distribution of the subject's gaze point on the diagnostic image, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, where the diagnostic image has a first area including a correct image for diagnosis or a memorized image, and a plurality of second areas including other images, and the diagnosis unit 39 calculates the gaze rate of the first area or the gaze rate of the second area in the distribution map for the diagnostic image, and diagnoses the presence or absence of cognitive dysfunction by comparing the gaze rate with a threshold value.

[0156] In addition, the cognitive dysfunction diagnosis device 1 that performs the second diagnostic processing includes a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's gaze point on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the gaze points detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and the diagnosis unit 39 displays a diagnostic image on the display unit 10, the diagnostic image having a first area including a correct image or memorized image for diagnosis and a plurality of second areas including other images, calculates the gaze rate of the first area or the gaze rate of the second area in the distribution map for the diagnostic image, and diagnoses the presence or absence of cognitive dysfunction by comparing the gaze rate with a threshold value.

[0157] This makes it possible to provide a cognitive impairment diagnostic device that is simple, low-cost, objective, quantitative, and versatile in diagnosing cognitive impairment. Moreover, it is possible to diagnose cognitive impairment in just a few seconds to a few tens of seconds.

[0158] In addition, the cognitive dysfunction diagnostic device 1 performing the second diagnostic processing includes an acquisition unit 41 that acquires a distribution map showing the distribution of the subject's gaze points on the diagnostic image, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, where the diagnostic image has a first region including a correct string and a plurality of second regions including incorrect strings, and the diagnosis unit 39 calculates the gaze rate to the first region in the distribution map for the diagnostic image, and diagnoses suspected aphasia if the gaze rate is lower than a threshold value.

[0159] In addition, the cognitive dysfunction diagnosis device 1 that performs the second diagnostic processing includes a display unit 10 having a display surface, an imaging unit 21 that images the eyes of the subject, a detection unit 37 that chronologically detects the subject's gaze point on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the gaze points detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and the diagnosis unit 39 displays a diagnostic image on the display unit 10, the diagnostic image having a first area including a correct string and a plurality of second areas including incorrect strings, calculates the gaze rate to the first area in the distribution map for the diagnostic image, and diagnoses suspected aphasia if the gaze rate is lower than a threshold value.

[0160] This makes it possible to provide a cognitive impairment diagnostic device that is simple, low-cost, objective, quantitative, and versatile in diagnosing aphasia, one of the cognitive impairments, and can diagnose cognitive impairment in just a few to several tens of seconds.

[0161] Here, the diagnostic video may have an image region including an image showing a known object, a question region including a question asking the name of the object, a first region which is a correct answer region including a character string image showing the name of the object, and a second region which is an incorrect answer region including a character string image other than the name of the object.

[0162] According to this, aphasia related to the names of objects can be diagnosed using linguistic instructions in the form of questions.

[0163] Here, the diagnostic image may have a correct answer region that includes a meaningful character string and a correct answer region that includes a meaningless character string. This allows aphasia to be diagnosed without the use of written or spoken instructions.

[0164] Here, the diagnostic image may include a plurality of characters arranged in a matrix, with the correct areas corresponding to rows or columns that make up meaningful character strings, and the incorrect areas corresponding to rows or columns that make up meaningless character strings.

[0165] This allows the diagnostic images to be simplified and aphasia to be diagnosed without the use of written or spoken instructions.

[0166] In addition, the cognitive dysfunction diagnostic program that performs the second diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer that acquires a distribution map showing the distribution of the subject's gaze point on a diagnostic image and diagnoses cognitive dysfunction based on the distribution map, wherein the diagnostic image has a first area including a diagnostic corrective image or a memorized image, and a plurality of second areas including other images, and the cognitive dysfunction diagnostic program causes the computer to calculate the gaze rate of the first area or the gaze rate of the second area in the distribution map for the diagnostic image, and diagnose the presence or absence of cognitive dysfunction by comparing the gaze rate with a threshold value.

[0167] In addition, the cognitive dysfunction diagnostic program that performs the second diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer that includes a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's viewpoint on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and causes the computer to display a diagnostic image on the display unit 10, the diagnostic image having a first area including a diagnostic correct image or memorized image, and a plurality of second areas including other images, calculate the gaze rate of the first area or the gaze rate of the second area in the distribution map for the diagnostic image, and diagnose the presence or absence of cognitive dysfunction by comparing the gaze rate with a threshold value.

[0168] Another cognitive dysfunction diagnostic program that performs a second diagnostic process is a cognitive dysfunction diagnostic program executed by a computer that acquires a distribution map showing the distribution of the subject's gaze on a diagnostic image and diagnoses a cognitive dysfunction based on the distribution map, wherein the diagnostic image has a first area including a correct string and a plurality of second areas including incorrect strings, and the cognitive dysfunction diagnostic program causes the computer to calculate a gaze rate to the first area in the distribution map for the diagnostic image, and diagnose suspected aphasia if the gaze rate is lower than a threshold value.

[0169] Another cognitive dysfunction diagnostic program for performing the second diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer including a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's viewpoint on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and causes the computer to display a diagnostic image having a first area including a correct string and a plurality of second areas including incorrect strings, calculate a gaze rate to the first area in the distribution map for the diagnostic image, and diagnose suspected aphasia if the gaze rate is lower than a threshold value.

[0170] [2.3 Third diagnostic process] Next, a description will be given of the third diagnostic process shown in step S30 in Fig. 5. The third diagnostic process diagnoses cognitive impairment by utilizing the above-mentioned third feature.

[0171] Fig. 23 is a flowchart showing an example of the third diagnostic processing in the embodiment, and Fig. 24 is a diagram showing an example of a diagnostic image in the third diagnostic processing of Fig. 23.

[0172] In FIG. 23, PC 30 first starts detection of the viewpoint by detector 37 (S201), displays a diagnostic image on display surface 11 (S202), and calculates a gaze rate Va of an incorrect answer area in the diagnostic image (S203).

[0173] The diagnostic image here is an image having a first region including a correct image and a plurality of second regions including incorrect images. For example, the diagnostic image is an image having a first region including a correct image showing a first figure, an instruction region including an instruction for inducing a direct view of the first figure, a second region including an incorrect image showing a figure similar to the first figure, and another second region including an incorrect image showing a figure not similar to the first figure.

[0174] The example of Fig. 24 shows a diagnostic image a1 displayed on the subject's display surface 11. This diagnostic image a1 has each of the shapes of a pentagon, a hexagon, a heptagon, and a triangle around the instruction "Stare at the hexagon."

[0175] FIG. 25 is a diagram showing an example of an evaluation image in the third diagnostic process of FIG. 23. The example of FIG. 25 shows a diagnostic image a1 as an evaluation image displayed on the display unit 35 of the PC 30. The diagnostic image a1 of FIG. 25 has a first region including a hexagonal correct answer image a11 as a first figure, an instruction text region a12 saying "Please stare at the hexagon", and a plurality of second regions including incorrect answer images. The plurality of second regions include a second region including a pentagonal incorrect answer image similar to the first figure, a second region including a heptagonal incorrect answer image similar to the first figure, and a second region including an off-target incorrect answer image a13 including a triangle not similar to the first figure.

[0176] Furthermore, the PC 30 calculates a gaze rate Va of the viewpoint to the incorrect answer area including the incorrect answer image after the diagnostic video is displayed (S203). Here, the gaze rate Vc may be a ratio (%) of the viewpoint existing in the first area in the distribution pattern of the viewpoint corresponding to the diagnostic video. The incorrect answer area to be calculated for the gaze rate Va may be the second area of ​​the off-target incorrect answer image a13, the instruction text area a12, or the combined area of ​​the second area of ​​the off-target incorrect answer image a13 and the instruction text area a12.

[0177] Next, the PC 30 judges whether the calculated gaze rate Va is greater than a threshold value th5 (S204). If it is judged that the gaze rate Va is greater than the threshold value th5, the subject is diagnosed as having a cognitive impairment (S205).

[0178] Furthermore, the PC 30 judges whether the calculated gaze rate Va is smaller than the threshold value th5 and larger than the threshold value th6 (S206). If it is judged that the gaze rate Va is smaller than the threshold value th5 and larger than the threshold value th6, the subject is diagnosed as having a suspected cognitive impairment (S207).

[0179] Furthermore, the PC 30 judges whether the calculated gaze rate Va is smaller than the threshold value th6 (S208). If it is judged that the gaze rate Va is smaller than the threshold value th6, the subject is diagnosed as having no cognitive impairment (S209).

[0180] The threshold value th5 and the threshold value th6 can be appropriately set by calculating the gaze rate Va for a plurality of subjects whose presence or absence of cognitive impairment is known in advance.

[0181] 23 may have a step of acquiring a distribution map showing the distribution of the subject's viewpoints with respect to the diagnostic image, instead of steps S201 and S202. The flowchart in which the steps are replaced with the acquiring steps can be easily implemented by the PC 30 in FIG 1B, or may be implemented by the PC 30 in FIG 1A.

[0182] Fig. 26 is a diagram showing an example of a distribution map in the third diagnostic process using Fig. 24 for a subject without cognitive impairment. As shown in the figure, the subject without cognitive impairment follows the instructions on the screen to select the correct image a11 from the options and gazes at it. The gaze rate for the incorrect images is low, and the gaze rate for the irrelevant incorrect image a13 is extremely low.

[0183] The third diagnostic process evaluates the cognitive function of the subject using the gaze rate to the incorrect image and the gaze rate to the instruction text area as indicators, rather than the gaze rate to the correct image. In other words, the gaze rate to the instruction text area and the gaze rate to the incorrect image in the diagnostic video are inversely correlated with the cognitive function of the subject, so that the cognitive function can be evaluated using these gaze rates as indicators.

[0184] Regarding the rate of gaze on incorrect images, in particular the "rate of gaze on irrelevant incorrect images J is inversely correlated with the subject's cognitive function. For example, if four options are presented and one of them is a correct image, the remaining three are incorrect images. One of the three incorrect images is made to be an image with content far removed from the correct answer, making it a particularly irrelevant incorrect image. In other words, the irrelevant incorrect image is an option that would be judged to be clearly incorrect if cognitive function was normal. The rate of gaze on correct images is positively correlated with the subject's cognitive function, but the rate of gaze on irrelevant incorrect images is negatively correlated with the subject's cognitive function.

[0185] The diagnostic image shown in Figure 24 displays four images as options (pentagon, hexagon, heptagon, triangle) and also displays the instruction "Stare at the hexagon." The hexagon is the "correct image," and in this case, the triangle is the "incorrect image." The pentagon and heptagon are similar to the correct hexagon, making it difficult to select, but for healthy subjects, the triangle does not cause them to make a choice.

[0186] Fig. 27 is a diagram showing a correlation between the gaze rate of the instruction sentence region in Fig. 25 and the MMSE score in the third diagnostic process, and Fig. 28 is a diagram showing a correlation between the gaze rate of the off-target incorrect image in Fig. 25 and the MMSE score in the third diagnostic process.

[0187] As shown in Figures 27 and 28, the gaze rate on the instruction sentence region a12 and the gaze rate on the irrelevant incorrect image a13 both show a statistically significant negative correlation with the subject's cognitive function score (MMSE score).

[0188] From this viewpoint, the third diagnostic process evaluates the cognitive function of the subject using the gaze rate on incorrect images and the gaze rate on instruction text areas as indices, rather than the gaze rate on correct images.

[0189] As described above, the cognitive dysfunction diagnosis device 1 performing the third diagnostic processing includes an acquisition unit 41 that acquires a distribution map showing the distribution of the subject's gaze points on the diagnostic image, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, where the diagnostic image has a first region including a correct image and a plurality of second regions including incorrect images, and the diagnosis unit 39 calculates the gaze rate of regions other than the first region in the distribution map for the diagnostic image, and diagnoses a suspicion of cognitive dysfunction if the gaze rate is higher than a threshold value.

[0190] In addition, the cognitive dysfunction diagnosis device 1 that performs the third diagnostic processing includes a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's viewpoint on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, and the diagnosis unit 39 displays a diagnostic image having a first area including a correct image and a plurality of second areas including incorrect images on the display unit 10, calculates the gaze rate of areas other than the first area in the distribution map for the diagnostic image, and diagnoses a suspicion of cognitive dysfunction if the gaze rate is higher than a threshold value.

[0191] According to this, it is possible to provide a cognitive dysfunction diagnostic device that is simple, low-cost, objective, quantitative, and versatile in diagnosing cognitive dysfunction. Moreover, it is possible to diagnose cognitive dysfunction using the rate of gaze on incorrect images as an index. Furthermore, it is possible to diagnose cognitive dysfunction within just a few seconds to several tens of seconds.

[0192] Here, the diagnostic image includes an instruction region including an instruction to guide direct viewing of the first figure, a first region including a correct image showing the first figure, a second region including an incorrect image showing a figure similar to the first figure, and another second region including an incorrect image showing a figure not similar to the first figure, and the diagnostic unit 39 may calculate the gaze rate of the instruction region or the other second region.

[0193] According to this, the gaze rate on the instruction sentence area or another second area can be used as an index to diagnose cognitive dysfunction.

[0194] In addition, the cognitive dysfunction diagnostic program that performs the third diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer that acquires a distribution map indicating the distribution of the subject's gaze points on a diagnostic image and diagnoses cognitive dysfunction based on the distribution map, wherein the diagnostic image has a first area including a correct image and a plurality of second areas including incorrect images, and the cognitive dysfunction diagnostic program calculates a gaze rate of areas other than the first area in the distribution map for the diagnostic image, and if the gaze rate is higher than a threshold value, causes the computer to diagnose a suspected cognitive dysfunction.

[0195] In addition, the cognitive dysfunction diagnostic program for performing the third diagnostic processing is a cognitive dysfunction diagnostic program executed by a computer including a display unit 10 having a display surface, an imaging unit 21 that images the subject's eyes, a detection unit 37 that chronologically detects the subject's viewpoint on the display surface based on the image captured by the imaging unit 21, a creation unit that creates a distribution map showing the distribution of the viewpoints detected by the detection unit 37, and a diagnosis unit 39 that diagnoses cognitive dysfunction based on the distribution map, in which the diagnosis unit 39 causes the computer to display a diagnostic image having a first area including a correct image and a plurality of second areas including incorrect images, calculates the gaze rate of areas other than the first area in the distribution map for the diagnostic image, and diagnoses a suspicion of cognitive dysfunction if the gaze rate is higher than a threshold value.

[0196] The cognitive impairment diagnostic device 1 may target multiple subjects at the same time. In this case, the imaging unit 21 images multiple subjects, the detection unit 37 detects the viewpoint for each subject, the creation unit 38 creates a distribution map for each subject, and the diagnosis unit 39 diagnoses each subject. When the cognitive impairment diagnostic device 1 targets multiple subjects at the same time, the device may include multiple imaging devices 20. In this case, the imaging devices 20 and subjects may be in a one-to-one or one-to-many relationship. This allows the cognitive impairment diagnostic device 1 to further improve the efficiency of group screening.

[0197] The above-mentioned embodiment and its modified examples are described as examples for the purpose of explaining the technical contents of the present invention, and are not intended to limit the technical scope of the present invention to the contents of this description. The technical scope of the present invention includes modifications, substitutions, additions, and omissions to the extent that a person skilled in the art can conceive of within the scope of the specification, drawings, and claims or equivalents thereto. [Industrial Applicability]

[0198] The present invention can be used in a cognitive impairment diagnosis device and a cognitive impairment diagnosis program for diagnosing cognitive impairment. [Explanation of symbols]

[0199] 1. Cognitive impairment diagnostic device 10 Display 11 Display surface 20 Imaging device 21 Imaging unit 22, 23 Camera 24 Light source section 25, 26 light source 30 PC 31 Processors 32 Storage section 33 Input section 34 Output section 35 Display section 36 Interface section 37 Detection section 38 Creation Department 39 Diagnostic Department 300 Diagnostic image data 301 First video data 302 Second video data 303 Third Video Data 310 Case Characteristics Data 311 First characteristic data 312 Second feature data 313 Third characteristic data 320 Programs 321 Cognitive impairment diagnostic program 322 Viewpoint Data 323 Distribution map data 324 Database

Claims

1. an acquisition unit that acquires a distribution map indicating a distribution of the subject's viewpoints with respect to the diagnostic image; A diagnosis unit that diagnoses cognitive impairment based on the distribution map; Equipped with The diagnostic image includes a first image having a first region including a memorization image that is visually highlighted and a plurality of second regions including normal images that are displayed without highlighting; The evaluation video includes, in time order, a third area including an evaluation image similar to the memorization image and displayed without visual emphasis at the same position as the memorization image, and the plurality of second areas; The first image includes, in time order, a first memorization image having the first region and the plurality of second regions, and a second memorization image having the first region but not the plurality of second regions; The diagnosis unit includes: Calculating a gaze rate of the third region in a distribution map for the evaluation video; If the gaze rate is lower than the threshold, a diagnosis of suspected memory disorder is made. Cognitive impairment diagnostic device.

2. An acquisition unit that acquires a distribution map indicating a distribution of subject's viewpoints with respect to a diagnostic image; A diagnosis unit that diagnoses cognitive impairment based on the distribution map; Equipped with The diagnostic image includes a first image having a first region including a memorization image that is visually highlighted and a plurality of second regions including normal images that are displayed without highlighting; The evaluation video includes, in time order, a third area including an evaluation image similar to the memorization image and displayed without visual emphasis at the same position as the memorization image, and the plurality of second areas; the diagnostic image includes another image displayed between the first image and the evaluation image, and includes an image in which the first image and the other image are repeated two or more times; The diagnosis unit includes: Calculating a gaze rate of the third region in a distribution map for the evaluation video; If the gaze rate is lower than the threshold, a diagnosis of suspected memory disorder is made. Cognitive impairment diagnostic device.

3. an acquisition unit that acquires a distribution map indicating a distribution of the subject's viewpoints with respect to the diagnostic image; A diagnosis unit that diagnoses cognitive impairment based on the distribution map; Equipped with the diagnostic image has a first region including a correct character string image for diagnosis and a plurality of second regions including incorrect character string images; The diagnosis unit includes: Calculating a gaze rate of the first region in the distribution map for the diagnostic image; If the fixation rate is lower than the threshold, a diagnosis of suspected aphasia is made. Cognitive impairment diagnostic device.

4. The diagnostic image is an image region including an image showing a known object; a question area including a question asking for the name of the object; The first area includes a character string image indicating the name of the object; The second area includes a character string image other than the name of the object. The cognitive impairment diagnosis device according to claim 3.

5. The diagnostic image is the first region containing a meaningful character string; and the second region including a meaningless character string. The cognitive impairment diagnosis device according to claim 3.

6. The diagnostic image is It contains a number of characters arranged in a matrix, the first region corresponds to a row or column composed of a meaningful character string; The second region corresponds to a row or column that is made up of meaningless characters. The cognitive impairment diagnosis device according to claim 5.

7. A cognitive impairment diagnosis program executed by a computer to acquire a distribution map showing a distribution of a subject's gaze points with respect to a diagnostic image and diagnose a cognitive impairment based on the distribution map, The diagnostic image includes a first image having a first region including a memorization image that is visually highlighted and a plurality of second regions including normal images that are displayed without highlighting; The evaluation video includes a third area including an evaluation image similar to the memorization image and displayed at the same position as the memorization image without visual emphasis, and the evaluation video includes the second areas, in order of time; The first image includes, in time order, a first memorization image having the first region and the plurality of second regions, and a second memorization image having the first region but not the plurality of second regions; The cognitive impairment diagnostic program includes: Calculating a gaze rate of the third region in a distribution map for the evaluation video; If the gaze rate is lower than the threshold, a diagnosis of suspected memory disorder is made. and causing the computer to execute the steps of: Cognitive impairment diagnostic program.

8. A cognitive impairment diagnosis program executed by a computer to obtain a distribution map showing a distribution of a subject's gaze points with respect to a diagnostic image, and diagnose a cognitive impairment based on the distribution map, The diagnostic image includes a first image having a first region including a memorization image that is visually highlighted and a plurality of second regions including normal images that are displayed without highlighting; The evaluation video includes a third area including an evaluation image similar to the memorization image and displayed at the same position as the memorization image without visual emphasis, and the evaluation video includes the second areas, in order of time; the diagnostic image includes another image displayed between the first image and the evaluation image, and includes an image in which the first image and the other image are repeated two or more times; The cognitive impairment diagnostic program includes: Calculating a gaze rate of the third region in a distribution map for the evaluation video; If the gaze rate is lower than the threshold, a diagnosis of suspected memory disorder is made. and causing the computer to execute the steps of: Cognitive impairment diagnostic program.

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