System and method of presenting risk of dementia

Abstract

To provide a dementia risk presentation system of presenting a user's risk of the dementia.SOLUTION: A presentation system 10 includes: a first database 17 in which sample brain health data 17a is associated with individual characteristics 17b, where the sample brain health data include at least any of sample brain state data 17d, the data relating to a state of the brain of a sample object person, and sample cognitive ability data 17e, the data relating to cognitive ability that is a function of the brain, and where the individual characteristics include at least one of an age, a gender, and physical information of the sample object person; a first recognition section 11 for recognizing user brain health data 18a including at least any of user brain state data 41 and user cognition ability data 61; a second recognition section 12 for recognizing user individual characteristics 43; and a risk presenting section 30 for presenting a chronological transition line 52 representing transition in the user brain health data from the past to the present of the user, and a risk line 54 relating to the dementia.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a system and method for presenting a user's dementia risk. [Background technology]

[0002] Patent Document 1 describes a dementia risk assessment system that can assess the potential risk of dementia at an extremely early stage and provide a preventive opportunity to delay the onset of dementia. This system includes a biodata detection sensor that acquires biodata of a subject while sleeping, a sleep data generation device that generates sleep data including the depth of sleep and changes in body movement of the subject over time from the biodata of the subject acquired by the biodata detection sensor, and a dementia risk assessment device that has a memory unit that stores sleep data specific to each symptom of a given dementia obtained from an actual patient, and compares the sleep data of the subject generated by the sleep data generation device with the sleep data of each symptom stored in the memory unit to assess the risk of three dementias from the sleep data of the subject. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-22310 A Summary of the Invention [Problem to be solved by the invention]

[0004] It has been known that dementia can be prevented from progressing by taking appropriate measures such as lifestyle habits such as the amount of exercise and alcohol consumption, and by providing appropriate treatment in the early stages of onset (including not only the actual onset state but also the suspected onset state). Therefore, there is a demand for early detection of the possibility of onset of dementia or the degree of progression after onset. Accordingly, various systems have been proposed for detecting the risk of onset and progression of dementia (hereinafter simply referred to as "dementia risk").

[0005] In the system for determining the risk of dementia from the above-mentioned hypnosis data, the user's brain function is estimated based on the state of sleep, and the dementia risk is recognized based on the estimation result. Therefore, the accuracy of the dementia risk is also affected by the accuracy of the estimation, so there is a problem that it is difficult to properly recognize the dementia risk. If the presence or absence of dementia risk is simply presented, there is a problem that it is difficult for those who do not have sufficient knowledge to properly understand the content of the dementia risk. Therefore, there is a demand for a system and method that can accurately recognize the dementia risk and further present it in an easy-to-understand form. [Means for solving the problem]

[0006] One aspect of the present invention is a system having a first database in which sample brain health data including at least one of sample brain state data, which is data related to the brain state of a sample subject, and sample cognitive ability data, which is data related to cognitive ability, which is a brain function, is associated with individual characteristics including at least one of the age, gender, and physical information of the sample subject; a first recognition unit that recognizes user brain health data including at least one of user brain state data, which is data related to the user's brain state, and user cognitive ability data, which is data related to cognitive ability, which is a brain function; a second recognition unit that recognizes user individual characteristics, which are the individual characteristics of the user; and a risk presentation unit that presents an aging transition line showing the transition of the user's brain health data from the past to the present, based on the past and present user brain health data, and a risk line for dementia determined by referring to the transition of the sample brain health data according to age, based on the sample brain health data associated with the individual characteristics corresponding to the user individual characteristics.

[0007] In this system, the user brain health data (data on brain health state) including at least one of data on the brain state of the user to be risk judged (user brain state data) and user cognitive ability data, which is data on cognitive ability, which is a brain function, is compared with data on the similar brain health state of a sample subject (sample brain health data) to recognize the dementia risk. The data on the brain state is direct data in the form of quantitative parameters based on the brain state that affects cognition, and is not indirect data in the form of brain function estimated from the user's brain waves, movements, etc., but is direct data in the form of data on the brain state. The cognitive ability test for acquiring the cognitive ability data is enriched in terms of content as a criterion for judging the dementia risk, and the state of the brain function can be accurately judged. An example of the cognitive ability test is described in Patent Application No. 2018-33652 filed by the applicant of the present application. Therefore, the dementia risk can be accurately recognized by comparing the user brain health data including either or both of these with the sample brain health data.

[0008] Furthermore, by presenting the dementia risk in a format that compares the risk line with an aging transition line, which is a line according to age, the user, etc. can simultaneously grasp not only the magnitude of the user's dementia risk, but also the trend of the user's dementia risk (whether it is worsening, improving, or preventing deterioration, etc.). Therefore, the user or a person who manages the user's health (hereinafter collectively referred to as "user, etc.") can intuitively grasp the magnitude of dementia risk from the information presented by this system, even if they do not have sufficient knowledge.

[0009] The risk presenting unit may include a first score presenting unit that presents a score indicating the user's dementia risk or a symbol corresponding to the score based on a result of comparison between the user's brain health data and the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics. By presenting the dementia risk as a score or a symbol corresponding to the score (hereinafter referred to as "score, etc."), the user, etc. can more intuitively grasp the magnitude of the user's dementia risk.

[0010] This system has a prediction unit that predicts an aging prediction line indicating a predicted transition of the user's brain health data from the present to the future based on the aging transition line, and the risk presentation unit may include a prediction presentation unit that presents the aging prediction line in addition to the aging transition line and the risk line. By presenting the aging prediction line, which is a prediction of the transition of the dementia risk from the present to the future, in a manner comparable to the risk line, the user, etc. can more accurately grasp the tendency of the user's dementia risk.

[0011] The risk presenter may include a second score presenter that presents, together with or instead of the aging prediction line, a score indicating the user's future dementia risk or a symbol corresponding to the score based on a result of a comparison between the user's brain health data at the first age on the aging prediction line and sample brain health data associated with individual characteristics corresponding to the individual characteristics obtained by adding the first age to the user's individual characteristics. The risk presenter may predict or forecast the user's future user brain health data at the first age separately from the aging prediction line, and present a score indicating the user's future dementia risk or a symbol corresponding to the score based on a result of a comparison between the predicted user brain health data and sample brain health data associated with individual characteristics corresponding to the individual characteristics obtained by adding the first age to the user's individual characteristics.

[0012] In the first database, the sample brain health data may be associated with individual characteristics and life characteristics including at least one of the lifestyle habits and the living environment of the sample subject. The system may further include a third recognition unit that recognizes user life characteristics that are the life characteristics of the user, and the prediction unit may predict the aging prediction line based on the sample brain health data and the aging transition line associated with the individual characteristics corresponding to the user individual characteristics, the life characteristics corresponding to the user life characteristics, and the age. When recognizing the aging prediction line, by referring to the user life characteristics, a highly reliable aging prediction line (and thus a tendency of dementia risk) can be recognized. This allows the user, etc. to more accurately grasp the tendency of the user's dementia risk.

[0013] The risk presenting unit may include a third score presenting unit that presents, together with or instead of the aging prediction line, a score indicating the user's future dementia risk or a symbol corresponding to the score based on a result of comparison between the user's brain health data of the first age on the aging prediction line, an individual characteristic corresponding to an individual characteristic obtained by adding the first age to the user's individual characteristic, and sample brain health data associated with a life characteristic corresponding to the user's life characteristic. The risk presenting unit may predict user brain health data corresponding to a user life characteristic designated by the user or arbitrarily selected, separately from the aging prediction line, and present a score indicating the user's future dementia risk or a symbol corresponding to the score based on a result of comparison between the predicted user brain health data and sample brain health data associated with the life characteristic corresponding to the user life characteristic.

[0014] The prediction unit may include a comparison prediction unit that recognizes a first aging prediction line based on a first user lifestyle characteristic and a second aging prediction line based on a second user lifestyle characteristic for the same user, and the risk presentation unit may include a comparison presentation unit that presents the first aging prediction line and the second aging prediction line side by side or consecutively so as to be comparable. The user or the like can intuitively grasp the fluctuation of the aging prediction line (and thus the dementia risk) according to the fluctuation of the lifestyle characteristic. This allows the user or the like to grasp the lifestyle characteristic that should be specifically improved, and thus provides an incentive for improving the lifestyle characteristic.

[0015] The risk presenting unit may include a fourth score presenting unit that presents a first score indicating the user's future dementia risk corresponding to the first aging prediction line and a second score indicating the user's future dementia risk corresponding to the second aging prediction line. Each score is calculated based on a result of a comparison between the user brain health data of the first age on each aging prediction line, an individual characteristic corresponding to the individual characteristic obtained by adding the first age to the user individual characteristic, and sample brain health data associated with the life characteristic corresponding to each user life characteristic. The risk presenting unit may predict the user's future dementia risk based on each user life characteristic separately from the aging prediction line, and present a first score and a second score indicating the risk.

[0016] The sample brain state data and the user brain state data may include data indicative of the total brain volume or the volume of at least one predetermined region of the brain, and may include brain images, cerebral blood flow, electroencephalograms, or other information that directly or indirectly indicates brain state, such as blood biomarkers.

[0017] Another aspect of the present invention is a method for indicating a risk of dementia, which includes the following steps using a system capable of referring to a first database in which sample brain health data, including at least one of sample brain state data, which is data related to a brain state of a sample subject, and sample cognitive ability data, which is data related to cognition, which is a brain function, of the sample subject, is associated with individual characteristics, including at least one of age, sex, and physical information of the sample subject. 1. Recognizing user brain health data including at least one of user brain state data, which is data relating to the state of the user's brain, and user cognitive ability data, which is data relating to cognitive ability, which is a function of the brain. 2. Recognizing user individual characteristics, which are individual characteristics of the user. 3. Presenting risk using an age-related trend line showing the progression of the user's brain health data from the past to the present based on past and present user brain health data, and a dementia risk line determined by referring to the progression of the sample brain health data according to age, based on sample brain health data associated with individual characteristics corresponding to the user's individual characteristics.

[0018] The method may further comprise presenting a score indicative of the user's dementia risk, or a symbol corresponding to the score, based on a comparison of the user's brain health data with the sample brain health data associated with an individual characteristic corresponding to the user's individual characteristic.

[0019] The method may further include predicting an aging prediction line indicating a predicted transition of the user's brain health data from the present to the future based on the aging progression line, and presenting the risk may include presenting the aging prediction line in addition to the aging progression line and the risk line. The method may also include presenting a score indicating the user's future dementia risk or a symbol corresponding to the score, together with or instead of the aging prediction line, based on a result of a comparison between the user's brain health data at the first age on the aging prediction line and sample brain health data associated with individual characteristics corresponding to the user individual characteristics plus the first age.

[0020] In the first database, the sample brain health data may be associated with individual characteristics and life characteristics including at least one of a lifestyle and a life environment of the sample subject. The method may further include recognizing a user life characteristic, the user's life characteristic, and predicting may include predicting an aging prediction line based on the sample brain health data and the aging progression line associated with the individual characteristics corresponding to the user individual characteristics, the life characteristics corresponding to the user life characteristics, and an age.

[0021] The method may further comprise presenting, together with or instead of the aging prediction line, a score indicating the user's future dementia risk or a symbol corresponding to the score based on a result of a comparison between the user's brain health data at the first age on the aging prediction line and an individual characteristic corresponding to the individual characteristic obtained by adding the first age to the user's individual characteristic, and sample brain health data associated with the life characteristic corresponding to the user's life characteristic.The method may also predict the user's brain health data at the first age specified by the user or selected arbitrarily, separately from the aging prediction line, and present a score indicating the user's future dementia risk or a symbol corresponding to the score based on a result of a comparison between the user's brain health data at the first age on the aging prediction line and the sample brain health data associated with the individual characteristic corresponding to the individual characteristic obtained by adding the first age to the user's individual characteristic, and life characteristic corresponding to the user's life characteristic.

[0022] The predicting may include predicting for the same user a first aging prediction line based on a first user lifestyle characteristic and a second said aging prediction line based on a second user lifestyle characteristic, and the presenting the risk may include presenting the first and second aging prediction lines side-by-side or consecutively for comparison. The method may include presenting a score for the first age on the first aging prediction line and a score for the first age on the second aging prediction line. The method may present multiple scores for dementia risk, either user specified or predicted based on any user lifestyle characteristics.

[0023] Another aspect of the present invention is a program (program product) having instructions for operating a computer as the above-mentioned system. The program (program product) may be provided in a form recorded on any recording medium. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a dementia risk presentation system. [Diagram 2]FIG. 13 is a diagram showing an example of a report provided by the presentation system. [Diagram 3] This is a graph plotting brain condition data to be referenced when presenting dementia risk as a change over time, with the vertical axis representing hippocampal volume and the horizontal axis representing age. [Figure 4] Similar to FIG. 3, this is a graph plotting brain condition data, with the 95% CI range also shown. [Diagram 5] An example of output including aged projection lines with estimated bands. [Figure 6] FIG. 6 is an example showing how the report display changes when lifestyle characteristics are changed. FIG. 6(a) shows the case where drinking is done every day, and FIG. 6(b) shows the case where drinking is done 2-3 days a week. [Figure 7] FIG. 7 is another example showing the change in the display content of the report when lifestyle characteristics are changed. FIG. 7(a) shows the case where drinking is done every day, and FIG. 7(b) shows the case where drinking is done 2-3 days a week. [Figure 8] FIG. 11 is a diagram showing another example of a report provided by the presentation system. [Figure 9] A diagram showing examples of tests to measure brain health. [Figure 10] Following Figure 9 is a diagram showing examples of tests or examinations to measure brain health. [Figure 11] 1 is a flowchart showing an overview of the operation of the presentation system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] FIG. 1 shows an example of a system including a device (system) that presents the risk of dementia. This system 1 includes a hospital 3 including an MRI (Magnetic Resonance Imaging) device (MRI) 3a, an information terminal (personal computer, tablet terminal) 2 that inputs various information about the user and outputs (displays) a report 50 about the dementia risk, and an information providing device (dementia risk presentation device, dementia risk presentation system, presentation system) 10 that processes information received from the MRI 3a and the tablet 2 via the Internet 9 and transmits the report 50 including the dementia risk and other information. In this example, the presentation system 10 includes computer resources such as memory and a CPU, and is implemented in a server 7 that can input and output information via the Internet (cloud) 9, but the presentation system 10 may be integrated with the user's information terminal 2, may be connected by wire or wirelessly, may be installed inside the hospital 3, and some or all of the functions of the presentation system 10 may be distributed and implemented in multiple information processing devices including the user terminal 2.

[0026] The MRI 3a acquires data (user brain state data) related to the user's brain state, and transmits the user brain state data 41 to the presentation system 10. The device that acquires the user brain state data 41 is not limited to the MRI 3a, but may be any device that has a configuration that can recognize the user brain state data of the user who is the subject of dementia risk judgment. For example, it may be a CT (Computed Tomography) examination device, an ultrasound examination device, or the like, or a device that measures brain function (cerebral blood flow) such as FDG or SPECT, or a device that measures brain waves, blood biomarkers, or the like, and information that can be measured by these may be included in the data related to the brain state. In addition, when using the amount of a predetermined protein in the brain instead of the shape or volume of the brain as the data related to the brain state, a PET (Positron Emission Tomography) image acquisition device or the like may be used. The presentation system 10 may use the brain state data 41 obtained from the MRI 3a, or may use the brain state data 41 obtained in advance and stored in the user library 18.

[0027] In the following, an example will be described in which the value of the hippocampal volume (hippocampal volume) is used as the brain state data 41, but the brain state data 41 is not limited to this. Here, the "brain state" refers to the state of the entire brain, or the state of a brain region that affects cognition (e.g., the hippocampus, supramarginal gyrus, angular gyrus, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, anterior cingulate cortex, etc.). The "state" includes the volume and shape of the entire brain or a specific region of the brain, as well as the amount of a specific protein in the brain (e.g., amyloid β, etc.). The brain state data (data related to the brain state, data related to the brain state) 41 includes quantitative parameters based on the brain state, and includes, for example, the above-mentioned brain state itself (i.e., the shape of the entire brain or a specific region), or a numerical value of the brain volume or size. The brain state data 41 may relate to a single brain state, or may relate to multiple brain states. Specifically, for example, the brain state data 41 may include the volume of the hippocampus, or may include a value obtained by a predetermined calculation formula based on the volume of the hippocampus and the amount of amyloid β in the brain.

[0028] The tablet 2, which inputs information about a user to the presentation system 10 and receives a report 50 including information about the user's dementia risk from the presentation system 10, is equipped with a touch panel 2a (input / output unit). Via the touch panel 2a, the user's individual characteristics 43 are input in response to an inquiry 42 about individual characteristics from the presentation system 10, and the user's lifestyle characteristics 45 are input and changed in response to an inquiry 44 about lifestyle characteristics. Furthermore, cognitive ability test questions 60 are presented on the touch panel 2a, and cognitive ability data 61 including answers to the cognitive ability test are input to the presentation system 10. Furthermore, the report 50 including a presentation of dementia risk is displayed on the touch panel 2a.

[0029] The user terminal used for inputting and outputting these pieces of information is not limited to the tablet 2 having the touch panel 2a. The user terminal only needs to be capable of recognizing the individual characteristics of the user who is the subject of dementia risk assessment and presenting the dementia risk. For example, a personal computer, smartphone, etc. may be used instead of a tablet. Also, instead of a touch panel which is an input / output unit in which the output unit and the input unit are integrated, an independent output unit and input unit may be used. Specifically, a keyboard, a microphone, etc. may be used as the input unit, and a display, a speaker, a printing device, etc. may be used as the output unit.

[0030] In addition, the input unit and output unit of the user terminal do not necessarily have to be provided in the same device, and do not have to be located near each other in a room, etc. For example, a display, speaker, printing device, etc. serving as an output unit of the user terminal may be located in a medical workstation provided in a location different from that where the MRI device, etc. are installed.

[0031] The server 7 operating as the presentation system 10 includes a memory (data storage section, data storage area) 16, and a processor (CPU) 8 that operates as various functions (functional units, functional sections) by loading a program (program product) 19 stored in the memory 16. The memory 16 includes a database (first database) 17 including sample brain health data 17a including at least one of sample brain state data 17d which is data relating to the brain state of a sample subject, and sample cognitive ability data 17e which is data relating to cognitive ability which is a brain function, and a user library 18 in which user brain health data (user brain health data) 18a is stored.

[0032] The database 17 further includes individual characteristics 17b including at least one of the sample subject's age, sex, and physical information, and the sample brain health data 17a is stored in association with the individual characteristics 17b. The database 17 further includes lifestyle characteristics 17c including at least one of the sample subject's lifestyle habits and living environment, and the sample brain health data 17a is stored in association with the lifestyle characteristics 17c.

[0033] Here, the "sample subject" includes not only those who have dementia (including mild dementia) or are suspected of having dementia, but also healthy individuals who do not have dementia. Individual characteristics 17b include at least one of age, sex, and physical information. Furthermore, the "physical information" included in individual characteristics 17b refers to physical information that can be quantitatively expressed. Specific examples include height, weight, body fat percentage, BMI, cholesterol level, etc.

[0034] Lifestyle characteristics 17c include at least one of lifestyle habits and living environment. "Lifestyle habits" refers to habits in daily life that affect the increase or decrease of dementia risk. Specific examples include the amount of exercise, the amount and type of food, the length and quality of sleep, the amount and frequency of communication, the amount and frequency of hobby activities, the amount and frequency of alcohol consumption, and the amount and frequency of smoking. "Living environment" refers to the environment in daily life that affects the increase or decrease of dementia risk. Specific examples include address (living environment), occupation, educational background, annual income, number of people living together, etc.

[0035] In the database 17, sample brain health data 17a including data (sample brain state data) 17d related to the brain state of the sample subject and data (sample cognitive ability data) 17e related to the result of the cognitive ability test of the sample subject is stored in a format associated with the individual characteristics 17b and life characteristics 17c of the sample subject. In the user library 18, in addition to the latest (current) user brain health data 18a including data (user brain state data) 41 related to the user's brain state and data (user cognitive ability data) 61 related to the user's cognitive ability, data acquired in the past is stored. The user library 18 may store the individual characteristics (user individual characteristics) 43 and life characteristics (user life characteristics) 45 of each user. The presentation system 10 recognizes the dementia risk by comparing the data (user brain health data) 18a related to the brain health state of the user to be risk judged with the data (sample brain state data) 17a related to the brain state of the sample subject.

[0036] Here, the data 17a and 41 relating to the brain state are direct data, that is, quantitative parameters based on the brain state that affects cognition. That is, the dementia risk presentation system 10 uses direct data, that is, data relating to the brain state, instead of indirect data, that is, brain function estimated from the user's brain waves, movements, etc., as in conventional systems. As a result, the presentation system 10 is not affected by the accuracy of estimation of brain function in recognizing dementia risk. As a result, the presentation system 10 can accurately recognize dementia risk compared to conventional systems.

[0037] The presentation system 10 has a first recognition unit 11 that acquires (recognizes) user brain health data 18a including at least one of user brain state data 41, which is data related to the state of the user's brain, and user cognitive ability data 61, which is data related to cognitive ability, which is a function of the brain. The first recognition unit 11 includes an input unit (input function, input unit) 14 that acquires the user brain state data 41, and an input unit (input function, input unit) 15 that acquires the user cognitive ability data 61. The cognitive ability data input unit 15 sends a cognitive ability test 60 to the user's terminal 2, and recognizes the user cognitive ability data 61 from the answer. The cognitive ability data input unit 15 is not limited to direct information input from the terminal 2, and may indirectly acquire information about the user by input via a paper medium, or by providing information via a doctor or other expert. The same applies to the recognition (acquisition, input) of other information.

[0038] An example of the cognitive test 60 is one that evaluates the function of each of the five cognitive domains. The cognitive domains include short-term memory, working memory, executive function, spatial cognition, and calculation. Short-term memory indicates the ability to retain and recall memorized content for a short period of time, and is said to involve the hippocampus of the brain. Working memory is the ability to manipulate retained memories in different ways, such as "remembering a phone number and pressing the button for that number to make a call," and is said to involve the prefrontal cortex and temporoparietal areas of the brain. Executive function is the ability to maintain and update rules, such as "procedures" for cooking and "changing ingredients according to the situation," and to control behavior and thinking, and is said to involve the prefrontal cortex in particular. Spatial cognition is the ability to instantly grasp and understand which direction a shape or object is facing in space, and is said to involve the parietal lobe of the brain. Calculation is the ability to perform the four arithmetic operations, and involves various areas of the brain, but is said to involve the temporoparietal area in particular.

[0039] The cognitive ability data input unit 15 performs a cognitive ability test 60 and acquires an evaluation for each of these cognitive ability domains as user cognitive ability data 61. The cognitive ability data 61 can be used as an index for accurately judging brain function by performing statistical processing of the evaluation for each cognitive ability domain as described below.

[0040] The presentation system 10 further includes a second recognition unit (individual characteristic input unit, second input unit) 12 that recognizes (acquires) user individual characteristics 43 that are individual characteristics of the user, and a third recognition unit (lifestyle characteristic input unit, third input unit) 13 that recognizes (acquires) user life characteristics 45 that are life characteristics of the user. The individual characteristic input unit 12 may send a questionnaire (questionnaire) 42 related to the individual characteristics to the user terminal 2 and recognize the individual characteristics 43 of each user from the answers thereto. In cases where an electronic medical record or the like related to the user has been created in advance, the individual characteristic input unit 12 may recognize the user individual characteristics 43 based on the description of the electronic medical record or the like.

[0041] The life characteristic input unit 13 may send a questionnaire (questionnaire) 44 related to life characteristics to the user terminal 2 and recognize the life characteristic 45 of each user from the answers thereto. In cases where an electronic medical record or the like related to a user has been created in advance, the life characteristic input unit 13 may recognize the user life characteristic 45 based on the description of the electronic medical record or the like.

[0042] The presentation system 10 further includes a recognition and prediction unit (prediction unit, prediction function, prediction unit) 20 that recognizes each user's current risk of dementia and predicts future risk, and a risk presentation unit (risk presentation function, risk presentation unit) 30 that presents information including dementia risk to the user. The recognition and prediction unit 20 includes a current risk recognition unit 21 that recognizes the current risk, a prediction unit 22 that predicts the future risk, and a score calculation unit 23 that converts the risk into a score.

[0043] The current risk recognition unit 21 recognizes a dementia risk line determined by referring to an aging transition line indicating the transition of the user's brain health data 18a from the past to the present, based on the past and present user brain health data 18a, and the age-related transition of the sample brain health data 17a, based on the sample brain health data 17a associated with individual characteristics 17b corresponding to the user individual characteristics 43, and outputs the same to the user terminal 2 via the risk presentation unit 30.

[0044] The current risk recognition unit 21 acquires the sample brain state data 17d and the sample cognitive ability data 17e associated with the individual characteristic 17b corresponding to the user individual characteristic 43 from the database 17. Then, the sample brain state data 17d is compared with the user brain state data 41, and the sample cognitive ability data 17e is compared with the user cognitive ability data 61. Then, the user's dementia risk may be recognized based on the results of these comparisons. As described above, the sample brain state data 17d and the user brain state data 41 are not limited to the hippocampus, and may be the entire brain volume or the volume of at least one predetermined area of ​​the brain.

[0045] The prediction unit 22 predicts an aging prediction line indicating a predicted transition of the user's brain health data from the present to the future based on the aging transition line, and is capable of presenting the aging prediction line in addition to the aging transition line and the risk line via the risk presentation unit 30. The score calculation unit 23 determines a score indicating the user's dementia risk or a symbol corresponding to the score based on a result of comparison between the current or future predicted user brain health data 18a and the sample brain health data 17a associated with the individual characteristic 17b corresponding to the user individual characteristic 43, and is capable of presenting the score via the risk presentation unit 30.

[0046] The risk presentation unit 30 includes a chart display unit 31 that displays an aging transition line, an aging prediction line, a risk line, etc., in a chart or graphic format, and a score presentation unit 32 that displays the obtained score. The chart display unit 31 includes a prediction presentation unit 31a that displays a prediction including the aging prediction line, and a comparison presentation unit 31b that displays the results of simulating a plurality of cases in which the life characteristics are changed. The score presentation unit 32 includes a first score presentation unit 32a that displays the current risk score (including the corresponding symbol, etc.), a second score presentation unit 32b that displays the predicted risk score, etc., a third score presentation unit 32c that displays the predicted risk score including the life characteristics, etc., and a fourth score presentation unit 32d that displays the risk score, etc., of the simulated cases in which the life characteristics are changed.

[0047] 2 shows an example of a report 50 provided to a user by the presentation system 10. This report 50 may be a screen or digital data presented on the user terminal 2 when a dementia risk judgment is made, may be an audio file, or may be a printed matter, booklet, or the like provided to the user by mail, with or without passing through the user terminal 2. The report 50 includes a brain image display area 59, an aging transition display area 51, a score display area 57, and a lifestyle characteristic display area 58, and information on dementia risk or user lifestyle characteristics is presented in each area.

[0048] Specifically, the brain image display area 59 displays an image of the user's brain included in the user brain state data 41 acquired by the MRI 3a. The aging transition display area 51 displays a graph (chart) as the dementia risk recognized by the risk recognition / prediction unit 20 via the presentation unit 30. This graph shows the result of comparison between the sample brain state data and the user brain state data. This graph includes an aging transition line 52, an aging prediction line 53, and a risk line 54.

[0049] In the score display area 57, a rank 55 relating to the overall risk judgment indicated by the alphabets A to D and F, a brain age 56a indicated by a number, and an arrow 56b which is a symbol relating to a long-term prediction are displayed as the dementia risk recognized by the risk recognition and prediction unit 20. In addition, a legend 56c relating to the rank 55 is displayed in the score display area 57. The value of this rank 55, the value of the brain age 56a, and the inclination of the arrow 56b are determined according to the score obtained based on the result of the comparison between the sample brain state data 17d and the user brain state data 41, and the result of the comparison between the sample cognitive ability data 17e and the user cognitive ability data 61. Rank A indicates high brain function, rank B indicates normal brain function, rank C indicates that brain function is declining, and rank D indicates that attention should be paid to the decline in brain function. Rank F indicates that treatment has already been started due to the decline in brain function.

[0050] The user's lifestyle characteristics 45 are displayed in the lifestyle characteristic display area 58. The user's lifestyle characteristics 45 reflect the results of changes made via the touch panel 2a.

[0051] It should be noted that the method of presenting dementia risk in such a display format is merely one example. That is, the method of presenting dementia risk by the presentation system 10 is not limited to such a configuration, and any method may be used as long as it presents the result of comparison between the user's brain health data 18a and the sample brain health data 17a associated with the user's individual characteristics and / or life characteristics. For example, the layout of the screen (report) 50 may be changed as appropriate. Also, only a graph or only display related to the score may be performed. Also, among the display related to the score, only one or two of the rank, brain age, and arrow may be displayed. It may be presented in a format completely different from the above display, for example, an image of the user's brain and an image of the brain of a sample subject who has the same individual characteristics as the user and is in an ideal health condition may be displayed side by side. Also, the result may be presented by voice.

[0052] The display contents in the aging transition display area 51 will be further explained. In this report 50, the aging transition display area 51 displays a graph showing dementia risk with hippocampal volume on the vertical axis and age on the horizontal axis. This graph includes an aging transition line 52, an aging prediction line 53, and a risk line 54. The aging transition line 52 shows the transition of the user's brain state data 41 from the past to the present. The aging prediction line 53 shows the transition of the user's brain state data 41 predicted from the present to the future. The risk line 54 is intended to show the degree of dementia risk that the user, etc. should be wary of, and divides the area in the graph into a reference range 54a and a caution zone 54b.

[0053] Users, etc. can understand the level of the user's dementia risk and the trend of their dementia risk by comparing the slope of the aging progression line 52 and the slope of the aging prediction line 53 with the slope of the risk line 54, and by referring to the position of the aging progression line 52 and the position of the aging prediction line 53 (specifically, whether they are within the reference range 54a or within the caution zone 54b).

[0054] In the process of creating the aging transition line 52 and the aging prediction line 53, the user brain state recognition unit 14 first recognizes the user's current and past brain state data 41. Specifically, the user brain state recognition unit 14 calculates the hippocampal volume using a predetermined algorithm from cross-sectional images of the user's brain acquired using the MRI 3a in the past (in this embodiment, when the user was 41 years old) and the present (at the time of dementia risk judgment (in this embodiment, when the user was 45 years old)). As the predetermined algorithm, a technology for predicting the hippocampal volume by a generation model of the image over time using a neural network may be used. To further improve accuracy, biomarker data information highly correlated with life findings may be added.

[0055] The current risk recognition section 21 of the risk recognition and prediction section 20 recognizes an aging transition line 52 regarding the user's brain state, based on the user's brain state 41 recognized by the user's brain state recognition section 14.

[0056] Specifically, as shown in Fig. 3, first, the current risk recognition unit 21 plots the user's past (age 41) hippocampal volume and the user's present (age 45) hippocampal volume on a graph with the vertical axis representing hippocampal volume and the horizontal axis representing age. After that, the risk recognition unit 21 connects them with a line segment to generate an aging transition line 52. Based on the user individual characteristic 43, the risk recognition unit 21 acquires sample brain state data 17d associated with individual characteristic 17b corresponding to the user individual characteristic 43 from the database 17.

[0057] An example of a user has an individual characteristic 43 of male. The risk recognition unit 21 acquires sample brain state data (i.e., hippocampal volume) 17d associated with the individual characteristic 17b of male. Next, the risk recognition unit 21 determines a risk line 54 by referring to the transition according to age of the sample brain state data 17d recognized based on the individual characteristic.

[0058] In FIG. 3, the vertical axis is hippocampal volume and the horizontal axis is age, and the sample brain state data 17d extracted by the individual characteristics 17b are plotted. In FIG. 3, black and white circles represent each sample brain state data 17d, white circles represent sample brain state data 17d belonging to the heavy drinker group, and black circles represent other sample brain state data 17d. In the figure, a first average line 66 indicating the average of the hippocampal volume of the extracted sample brain state data 17d including the heavy drinker group is shown by a solid line, and a second average line 67 indicating the average of the hippocampal volume of the sample brain state data 17d only of the heavy drinker group is shown by a dashed line. Such statistical processing may be performed in advance or may be performed by the risk recognition unit 21.

[0059] The risk recognition unit 21 generates a risk line 54 shown by a dotted line at a position below the first average line 66. Specifically, it generates a line (a line indicating the bottom 2.3%) based on a value (x1-2σ) that is 2σ (where "σ" represents the standard deviation of the hippocampal volume (sample brain state data) by age) lower than each value x1 of the first average line 66, and recognizes the line as the risk line 54.

[0060] The method of recognizing the risk line 54 of the present invention is not limited to such a method, and may be determined by referring to the transition of the sample brain state data according to age based on the sample brain state data 17d associated with the individual characteristic corresponding to the user's individual characteristic. For example, in the case where the user's individual characteristic is male as in the present embodiment, the risk line 54 may be determined based on the average line of the sample brain state data 17d associated with the user's life characteristic 45 and the corresponding life characteristic 17c among the sample brain state data 17d, rather than all of the sample brain state data 17d associated with the individual characteristic of male. Specifically, the risk line 54 may be determined based on the second average line 67, which is a trend line of only the group with a high alcohol intake.

[0061] Also, for example, the average line itself may be used as the risk line 54, rather than a line based on a value of (-2σ) for each value of the average line. Also, the risk line 54 may be generated using a different value (for example, a value of (-3σ) for each value of the average line). Also, instead of generating the risk line 54 based on such statistically generated data, the risk line 54 may be generated simply by referring to the ranking of the data, etc. Specifically, for example, the risk line 54 may be generated based on data of a predetermined ranking (for example, data within the bottom three) among the sample brain state data 17d associated with the individual characteristic corresponding to the user individual characteristic.

[0062] Next, the prediction unit 22 of the risk recognition and prediction unit 20 predicts (calculates) an aging prediction line 53 indicating a predicted transition of the user's brain health data from the present to the future, based on the aging transition line 52. The prediction unit 22 includes a function of predicting the aging prediction line 53 based on the aging transition line 52 and the sample brain health data 17a associated with the individual characteristic 17b corresponding to the user individual characteristic 43, the life characteristic 17c corresponding to the user life characteristic 45, and age.

[0063] The prediction unit 22 generates an aging prediction line 53 based on the sample brain state data 17d associated with the life characteristic 17c corresponding to the user life characteristic 45, in this example, based on the second average line 67 of the sample brain state data (white circle) 17d of the individual characteristic that the user drinks a lot, based on the user life characteristic 45. Specifically, the prediction unit 22 refers to the transition of the second average line 67 from the reference age of the user brain state data 41 (45 years old, which is the most recent age in this embodiment) to a predetermined age (50 years old in this embodiment), and generates, as the aging prediction line 53, a line that follows the same transition as the transition of the second average line 67, starting from the reference age of the aging transition line 52.

[0064] In this embodiment, the user's life characteristics 45 are referenced when recognizing the aging prediction line 53. This is to recognize a highly reliable aging prediction line (and thus a tendency of dementia risk). However, the method of recognizing the aging prediction line of the present invention is not limited to such a method, and any method may be used as long as it is a method of recognition based on the aging transition line 52. For example, the slope of the aging transition line 52 may be recognized, and the aging prediction line 53 may be generated and recognized using the slope. Also, any method may be used as long as it is determined based on the sample brain state data 17d associated with the life characteristic 17c corresponding to the user's life characteristic 45, by referring to the transition of the sample brain state data 17d according to age. For example, the sample brain state data 17d related to two or more life characteristics 17c may be referenced, not just the sample brain state data 17d related to only one life characteristic 17c.

[0065] For an unspecified number of sample subjects of age z, if the regression equation of an evaluation factor x consisting of at least one of the three factors of "sample brain state data", "sample cognitive ability data" and "user individual characteristics" is expressed by the following formula (1), and for a sample subject having a predetermined lifestyle characteristic (e.g., drinking habit) among the unspecified number of sample subjects of age z, if the regression equation of the evaluation factor x is expressed by the following formula (2), the evaluation factor x at the next time point t+1 (t represents a discrete time such as 5 years or 10 years) is expressed by the following formula (3). t+1 is the evaluation factor x at time t t Based on this, it is predicted according to the following equation (3). x = β0 + β1z (β0 and β1 are regression coefficients) (1) x' = β0' + β1'z (β0' and β1' are regression coefficients) (2) x t+1 =x t +{IF life property THEN (x t+1 '-x t ') ELSE(x t+1 -x t )} (3)

[0066] The prediction presentation unit 31a, which displays the chart of the risk presentation unit 30, displays the aging transition line 52, the aging prediction line 53, and the risk line 54 in parallel in the aging transition display area 51 of the report 50 so as to be comparable. The prediction presentation unit 31a may display the plot showing the sample brain state data 17d, the first average line 66, and the second average line 67, but these are deleted so that the user can easily see them. The prediction presentation unit 31a may apply different colors to the reference range 54a above the risk line 54 and the caution zone 54b below it. In addition, the prediction presentation unit 31a plots the ages (41 years old, 45 years old, and 50 years old in this embodiment) that are the references for the aging transition line 52 and the aging prediction line 53 on the horizontal axis of the graph.

[0067] In this embodiment, when the aging prediction line 53 is above the risk line 54, it means that the value cdf(x|μ,σ) of the cumulative density function of the evaluation factor x at the future time point t+1, which is given by the following formula (4), is t+1 |μ t+1 ,σ t+1 ) is greater than 2σ. cdf(x|μ,σ)=(1 / 2){1+erf(x-μ) / (2σ 2 ) 1 / 2}) (erf is the error function) (4)

[0068] The prediction unit 22 may display the aging prediction line 53 with an estimated width 53w indicating a region with high probability. As shown in FIG. 4, the first average line 66 and the second average line 67 include errors, and 95% CI (Confidence Interval) is indicated by lines 66a and 67a, respectively. The prediction unit 22 may obtain an estimated width 53w of the aging prediction line 53 for each user's final time point t, using the estimated width of the second average line 67 of the same age at a future time point t+1, and the prediction presentation unit 31a may display the estimated width 53w in the aging transition display area 51.

[0069] Figure 5 shows a different example of the aging prediction line 53 using the estimated range 53w. In this example, it is suggested that at a future time point t+1, the estimated range 53w of dementia risk may fall below the risk line 54 from pre-MCI (the pre-stage of Mild Cognitive Impairment) to MCI, indicating that there is a probabilistic risk of progressing to AD (Alzheimer's Disease).

[0070] In this way, the presentation system 10 presents the dementia risk in a form of comparison with the aging progression line 52, the aging prediction line 53, and the risk line 54, which are lines according to age. This allows the user, etc., to simultaneously grasp not only the magnitude of the user's dementia risk, but also the trend of the user's dementia risk (whether it is worsening, improving, or preventing deterioration, etc.).

[0071] The prediction unit 22 of the presentation system 10 further includes a comparison prediction unit 22a that recognizes a first aging prediction line 53 based on the first user lifestyle characteristic 45 and a second aging prediction line 53a based on the second user lifestyle characteristic 45 for the same user. When the user lifestyle characteristic 45 is changed by the user, the display content related to dementia risk also changes according to the change. For this reason, the presentation system 10 includes a comparison prediction unit 22a that simulates the results of changing the user lifestyle characteristic 45, and a comparison presentation unit 31b that presents the results in a comparative arrangement or consecutively.

[0072] FIG. 6 shows two types of reports 50 in which the aging prediction line 53 has changed by changing the user lifestyle characteristics 45. In the report 50 shown in FIG. 6(a), the content of the amount of alcohol consumed in the lifestyle characteristics display area 58 is set to "everyday," and in the report 50 shown in FIG. 6(b), the content of the amount of alcohol consumed in the lifestyle characteristics display area 58 is changed to "2-3 times a week." As a result, in the report 50 shown in FIG. 6(b), the aging prediction line 53a displayed in the aging transition display area 51 is above the aging prediction line 53 shown in the report 50 in FIG. 6(a) and has a slope away from the risk line 54, indicating that the risk of dementia is reduced.

[0073] In the comparison prediction unit 22a, when the content of the amount of alcohol consumed in the user life characteristic 45 is changed from "everyday" to "two to three times a week," a sample with a small amount of alcohol consumed is extracted from the sample brain state data 17d extracted in FIG. 3 to obtain a new average line (third average line, not shown), and a new aging prediction line 53a is generated based on the average line by a process similar to that for the aging prediction line 53. The comparison presentation unit 31b may present the first aging prediction line 53 of the previous condition and the second aging prediction line 53a of the next condition as separate reports 50, or may present them side by side so that they can be referred to within the report 50. The comparison presentation unit 31b may display the first aging prediction line 53 and the second aging prediction line 53a consecutively so that they can be compared. Specifically, after the first aging prediction line 53 is hidden after a predetermined time has elapsed, the second aging prediction line 53a may be displayed within a time period during which the user or the like can grasp the general shape of the first aging prediction line 53.

[0074] The comparison prediction unit 22a and the comparison presentation unit 31b may obtain and present the aging prediction line 53 by changing the conditions of the user lifestyle characteristics 45 any number of times, not limited to twice. This function allows the user, etc., to intuitively grasp the change in dementia risk according to the change in the lifestyle characteristics 45 using the presentation system 10. This allows the user, etc., to grasp the specific lifestyle characteristics that should be improved, and thus provides an incentive for improving the lifestyle characteristics.

[0075] The risk recognition / prediction unit 20 of this presentation system 10 includes a score calculation unit 23 that converts the dementia risk into a score, and the risk presentation unit 30 includes a score presentation unit 32 that presents the dementia risk based on the score. The score calculation unit 23 compares the sample brain state data 17d and the sample cognitive ability data 17e recognized based on the user brain state data 41, the user cognitive ability data 61, the user individual characteristics 43, and the user life characteristics 45, and performs scoring based on the result of the comparison, and determines a rank, a brain age, and a corresponding symbol based on the score.

[0076] For example, the value cdf(x t |μ t ,σ t ) of the cumulative density function cdf(x|μ,σ) of the evaluation factor x such as the sample brain state data 17d shown in the formula (4) above at the current time point t may be defined as the score S. The rank 55 may be determined according to which numerical range among a plurality of numerical ranges (for example, four numerical ranges of 0.0 < S ≤ 0.25, 0.25 < S ≤ 0.50, 0.50 < S ≤ 0.75, 0.75 < S ≤ 1.0) the score S is included in. Further, the score calculation unit 23 may include a function 23a for determining the brain age 56a by referring to a table in which various values of the score S or each of a plurality of numerical ranges is associated with each of a plurality of brain ages.

[0077] In addition, the score calculation unit 23 may include a function 23b for determining a symbol 56b corresponding to the score based on the deviation of the value cdf(x t+1 |μ t+1 ,σ t+1 ) of the cumulative density function cdf(x|μ,σ) of the evaluation factor x at the future time point t + 1 from its value cdf(x t |μ t ,σ t ) at the current time point t. When the deviation is a positive value or a predetermined positive value or more, there is a possibility of risk reduction. When the deviation is a negative value or a predetermined negative value or less, there is a possibility of risk increase. When the deviation is 0 or its magnitude is less than a predetermined positive value and greater than a predetermined negative value, it is determined that there is no change in risk. Then, the symbol 56b corresponding to the score S is determined according to whether there is a "possibility of decrease", "possibility of increase", and "no change" in risk.

[0078] The function 23b for determining the symbol may include a function for determining a long-term prediction symbol 56b corresponding to the score S based on whether the value cdf(xt+1|μt+1,σt+1) of the evaluation factor x at the future time point t + 1 is 0.023 or more, which is equivalent to (-2σ, the threshold value indicating the lower 2.3%) of the population, or less than that.

[0079] The score calculation method is not limited to a method using parametric modeling, such as the above-mentioned method using a cumulative density function. For example, a method using a non-parametric ranking may be used. Specifically, various quantile calculation methods using average rank, media rank, approximation, etc. may be used.

[0080] The score presentation unit 32 of the presentation system 10 displays (outputs) a rank 55 determined based on the score, a brain age 56a, an arrow 56b, and a legend 56c relating to the rank on the touch panel 2a of the tablet 2 or the report 50. In this way, the presentation system 10 presents the dementia risk as a score or a symbol corresponding to the score. This allows the user, etc., to intuitively grasp the magnitude of the user's dementia risk.

[0081] Score calculation unit 23 may include a function 23c for calculating the user's future dementia risk, for example, one year from now, in addition to the function of calculating the current dementia risk score and displaying it via first score presentation unit 32a. Future score calculation function 23c may obtain a score indicating the user's future dementia risk based on a result of comparison between user brain health data 18a of the first age on aging prediction line 53 and sample brain health data 17a associated with individual characteristics corresponding to individual characteristics 17b obtained by adding the first age to user individual characteristics 43, and output the calculated score or a symbol corresponding to the score by second score presentation unit 32b together with or instead of aging prediction line 53.

[0082] The function 23c for calculating a future score may obtain a score indicating the user's future dementia risk based on a result of comparison between the user brain health data 18a of the first age on the aging prediction line 53 and the individual characteristic 17b corresponding to the individual characteristic obtained by adding the first age to the user individual characteristic 43, and the sample brain health data 17a associated with the life characteristic 17c corresponding to the user life characteristic 45, and output the calculated score or a symbol corresponding to the score by the third score presenting unit 32c together with or instead of the aging prediction line 53. The function 23c for calculating a future score may include a function for calculating a score based on the user brain health data 18a designated or arbitrarily selected by the user instead of the user brain health data 18a on the aging prediction line 53.

[0083] The function 23c for calculating the future score may include a simulation function 23d for checking how the score changes by changing the user life characteristic 45. The function 23d calculates a first score indicating the user's future dementia risk based on a result of comparison between the user brain health data 18a of the first age on the first aging prediction line 53 reflecting the first user life characteristic 45, the individual characteristic 17b corresponding to the individual characteristic obtained by adding the first age to the user individual characteristic 43, and the sample brain health data 17a associated with the life characteristic 17c corresponding to the first user life characteristic 45. Furthermore, the function 23d calculates a second score indicating the user's future dementia risk based on a result of comparison between the user brain health data 18a of the first age on the second aging prediction line 53a based on the second user life characteristic 45, the individual characteristic 17b corresponding to the individual characteristic 43 obtained by adding the first age to the user individual characteristic, and the sample brain health data 17a associated with the life characteristic 17c corresponding to the second user life characteristic 45. The fourth score presenting unit 32d may output the first and second scores or a symbol corresponding thereto together with or instead of the aging prediction line. When the user's lifestyle characteristics are changed by the user, etc., the display content related to the dementia risk also changes according to the change.

[0084] In the report 50 shown in FIG. 7(a), the content of the amount of alcohol consumed in the life characteristic display area 58 is "everyday", and in the report 50 shown in FIG. 7(b), the content of the amount of alcohol consumed has been changed to "2-3 times a week". In these reports 50, the risk of dementia at a certain point in the future, for example, one year from now, is displayed as a score, and by changing the content of the amount of alcohol consumed from "everyday" to "2-3 times a week", the aging prediction line 53 changes, and the sample brain health data 17a to be compared changes from the "high alcohol consumption group" to the "low alcohol consumption group". In this case, the overall risk judgment 55 changes from "B" to "A", the brain age 56a becomes younger, and the long-term prediction symbol 56b also changes to a flat direction.

[0085] In this way, in the presentation system 10, the future dementia risk can be output as a score or a rank in response to changes in the user lifestyle characteristics 45. This allows the user, etc. to intuitively grasp the change in dementia risk in response to changes in the lifestyle characteristics. This allows the user, etc. to grasp the specific lifestyle characteristics that should be improved, and thus provides an incentive for improving the lifestyle characteristics.

[0086] FIG. 8 shows an example of a different type of report showing the risk of dementia. This report 50 is output from the risk presentation unit 30 of the presentation system 10 in the same manner as above. The report 50 includes an analysis result display area 75 of the user cognitive ability data 61 and an aging transition display area 71 of the user cognitive ability data 61 in addition to the score display area 57 and the aging transition display area 51 of the user brain state data 41. The score display area 57 includes a display of a CQ score (Cognitive Quotient Score) 76 in addition to the rank 55 and the brain age 56a. The CQ score 76 is a comprehensive score of an individual's cognitive ability and brain function based on each cognitive function test, and when the average is set to 100 and the standard deviation is set to 15, it becomes a scale compatible with the scoring of IQ (intelligence quotient) and other cognitive ability tests. The CQ score (cq) 76 is calculated by the following formula (5). [Formula 5] TIFF2024177486000002.tif35156

[0087] The variables xi are the evaluation results of each domain of the cognitive ability test 60, and in this report 50, they are the test results of each function of short-term memory, working memory, executive function, spatial cognition, and calculation. The test items (domains) included in the cognitive ability test 60 are not limited to these, and may be a combination of forward recitation, backward recitation, calculation, Stroop test, and mental rotation, or a combination of other test items. In formula (5), the scores of each test item (domain) are standardized (z) to normal distribution and then summed up to obtain the total score (cq, CQ score) 76.

[0088] In this report 50, the cumulative density function cdf(cq|μ,σ)77 for the same age is calculated using the cumulative density function shown in formula (4), and a rank 55 is determined based on the numerical range that includes the calculated CQ score 76. For example, assuming a normal distribution with an average of 100 and a standard deviation of 15, the "Above" region with a standard score exceeding 110 is set as rank A, the "Average and Low Average" region with a standard score of 80-110 is set as rank B, the "Low" region with a standard score of 70-79 is set as rank C, and the "Very Low" region with a standard score of less than 70 is set as rank D.

[0089] It should be noted that cognitive tests are not limited to these, and scores may be assessed according to the tests or tests listed in Figures 9 and 10 that measure brain health status (including the state of cognitive function and the presence and severity of brain and mental disorders) or other similar types of tests or tests.

[0090] The analysis result display area 75 of the report 50 includes a radar chart 78 showing the results of each item (short-term memory, working memory, executive function, spatial cognition, and calculation) of the cognitive ability test 60 and the standard for the same age. The aging trend display area 71 of the user cognitive ability data 61 shows aging trend lines 72a to 72e for each item (short-term memory, working memory, executive function, spatial cognition, and calculation) of the user cognitive ability data 61 and standard lines 74a to 74e for the same age group as risk lines.

[0091] The aging trend display area 51 of the report 50 shows an aging trend line 52 of the user's brain state data 41, a risk line 54, and an average trend line 65 for the same age group based on the user's individual characteristics 43. From these displays, the user can clearly understand the risk of dementia and the progress of the health condition of the brain by looking at the report 50.

[0092] FIG. 11 shows an outline of the process in the presentation system 10 described above using a flowchart. In step 101, the first recognition unit 11 recognizes (acquires) the user brain health data 18a including at least one of the user brain state data 41, which is data related to the state of the user's brain, and the user cognitive ability data 61, which is data related to the cognitive ability, which is the function of the brain. In step 102, the second recognition unit (individual characteristic input unit) 12 recognizes (acquires) the user individual characteristic 43, which is the individual characteristic of the user. In step 103, the presentation system 10 judges whether a prediction process is requested, and if a prediction process is not requested, in step 104, the risk presentation unit 30 presents a risk based on the past and present user brain health data 18a, using an aging transition line 52 showing the transition of the user's user brain health data 18a from the past to the present, and a dementia-related risk line 54 determined by referring to the transition according to age of the sample brain health data based on the sample brain health data 17a associated with the individual characteristic 17b corresponding to the user individual characteristic 43.

[0093] In step 105, the risk presenting unit 30 further presents a score 55 indicating the user's dementia risk, or a symbol corresponding to the score, based on the result of the comparison between the user's brain health data 18a and the sample brain health data 17a associated with the individual characteristic 17b corresponding to the user's individual characteristic 43. Furthermore, in step 106, the risk presenting unit 30 creates a report 50 including this information and provides it to the user. The report 50 at this stage does not include an aging prediction line.

[0094] When a prediction process is requested in step 103, in step 107, the presentation system 10 further judges whether or not the user life characteristic has been input. If the user life characteristic 45 has been input, in step 108, the third recognition unit (life characteristic input unit) 13 recognizes (acquires) the user life characteristic 45, which is the life characteristic of the user. In step 109, the prediction unit 22 of the risk recognition and prediction unit 20 predicts (calculates) the aging prediction line 53 based on the individual characteristic 17b corresponding to the user individual characteristic 43, the life characteristic 17c corresponding to the user life characteristic 45, the sample brain health data 17a associated with age, and the aging transition line 52. In step 108, if the user life characteristic 45 has not been input, in step 109, the prediction unit 22 generates the aging prediction line 53 based on the sample brain health data 17a associated with the individual characteristic 17b and the aging transition line 52.

[0095] In step 110, the risk presenting unit 30 presents the aging prediction line 53 in addition to the aging transition line 52 and the risk line 54. In step 111, the score calculation unit 23 calculates a score 55 indicating the user's future dementia risk based on a result of comparison between the user brain health data 18a of the first age on the aging prediction line 53 and the individual characteristic 17b corresponding to the individual characteristic obtained by adding the first age to the user individual characteristic 43, and the sample brain health data 17a associated with the life characteristic 17c corresponding to the user life characteristic 45, and the risk presenting unit 30 presents the score 55 or a symbol corresponding to the score together with or instead of the aging prediction line 53. In step 108, if the user life characteristic 45 is not input, in step 110, the score calculation unit 23 calculates the score 55 based on the sample brain health data 17a associated with the individual characteristic 17b.

[0096] In step 112, the presentation system 10 judges whether a different (next) user life characteristic 45 is provided. If a different user life characteristic 45 is provided, the process returns to step 108 to obtain a different life characteristic (second user life characteristic) 45, and performs the same process as described above. In step 109, a second aging prediction line 53a related to the second user life characteristic 45 is predicted, and in step 110, the first aging prediction line 53 related to the previous life characteristic (first user life characteristic) 45 and the second aging prediction line 53a related to the second user life characteristic 45 are presented side by side or consecutively in a comparable manner. In step 111, a second score 55 on the second aging prediction line 53a related to the second user life characteristic 45 is calculated, and presented together with or instead of the aging prediction lines 53 and 53a in a state comparable to the first score 55 on the first aging prediction line 53 related to the first user life characteristic 45.

[0097] If there is no input of the next user lifestyle characteristic in step 112, it is determined in step 113 whether or not multiple predictions have been performed, and if multiple predictions have not been performed, in step 114, the risk presenting unit 30 creates a report 50 including the aging transition line 52, the aging prediction line 53, the risk line 54, and the score 55, and provides it to the user. If multiple aging prediction lines 53 have been generated, in step 115, the risk presenting unit 30 creates a report 50 including the simulated multiple aging prediction lines 53 and multiple scores 55, and provides it to the user.

[0098] In this embodiment, when scoring the dementia risk, the brain state data and cognitive ability data are used with reference to the user's lifestyle characteristics. This is to recognize the reliability score (and thus the dementia risk). However, the scoring method of the present invention is not limited to such a method, and it is sufficient that the scoring is performed based on the result of comparing the user's brain state data with sample brain state data associated with individual characteristics corresponding to the user's individual characteristics. For example, it is not necessary to refer to the lifestyle characteristics, and it is also possible to use only the brain state data without using the cognitive ability data.

[0099] In addition, as in the report 50 shown in Figure 2, when an age-related change display area 51 is provided in addition to the score display area 57 and the dementia risk based on brain state data is displayed in the age-related change display area 51, the score 55 etc. displayed in the score display area 57 may be determined by referring only to the cognitive ability data.

[0100] As described above, the presentation system 10 recognizes dementia risk by comparing brain health data including data on the brain state of the user to be risk-judged (user brain state data) and data on the brain state of a sample subject (sample brain state data). The data on the brain state is direct data in the form of quantitative parameters based on the brain state that affects cognition. In other words, the dementia risk presentation system 10 can use direct data on the brain state, rather than indirect data on the brain function inferred from the user's brain waves, movements, etc., as in conventional systems.

[0101] As a result, in the presentation system 10, recognition of dementia risk is less affected by the estimation accuracy regarding brain function. As a result, the presentation system 10 can recognize dementia risk more accurately than conventional systems.

[0102] In addition, the presentation system 10 can present the recognized dementia risk to the user, etc., in the form of a result of comparison between the user brain state data and the sample brain state data. As a result, according to the presentation system 10, the user, etc., can intuitively grasp the magnitude of the dementia risk even if he or she does not have sufficient knowledge. In addition, the presentation system 10 can present the dementia risk in a form that compares the aging transition line, which is a line according to age, with the risk line. Therefore, the user, etc., can simultaneously grasp not only the magnitude of the user's dementia risk, but also the tendency of the user's dementia risk (whether it is worsening, improving, or suppressing deterioration, etc.). In addition, the aging prediction line, which is a prediction of the progression of the dementia risk from the present to the future, can be presented in a manner comparable to the risk line, and the user, etc., can more accurately grasp the tendency of the user's dementia risk.

[0103] Furthermore, when recognizing the aging prediction line, the presentation system 10 can refer to one or more user lifestyle characteristics, and can recognize a highly reliable aging prediction line (and thus a dementia risk trend), while allowing the user to more accurately grasp the user's dementia risk trend. Furthermore, by presenting aging prediction lines relating to a plurality of user lifestyle characteristics in a form that allows comparison, the user can intuitively grasp the fluctuation of the aging prediction line (and thus the dementia risk) according to the fluctuation of the lifestyle characteristics. This allows the user to grasp the specific lifestyle characteristics that should be improved, and thus provides an incentive for improving the lifestyle characteristics.

[0104] Furthermore, the presentation system 10 can present the dementia risk as a score or the like. When recognizing the score or the like, the user can refer to the user lifestyle characteristics and recognize a highly reliable score or the like (and thus the dementia risk). This allows the user to more accurately grasp the tendency of the user's dementia risk. In addition, since the user can grasp the specific lifestyle characteristics that should be improved, an incentive is given to improve the lifestyle characteristics.

[0105] The above discloses a dementia risk presentation system that presents a user's dementia risk, comprising: a data storage unit in which sample brain state data, which is data relating to the brain state of a sample subject, is stored in association with individual characteristics including at least one of the age, gender, and physical information of the sample subject; a user brain state recognition unit that recognizes user brain state data, which is data relating to the brain state of the user; a user individual characteristic recognition unit that recognizes user individual characteristics, which are the individual characteristics of the user; a risk recognition unit that compares the user brain state data with the sample brain state data associated with the individual characteristics corresponding to the user individual characteristics; and a risk presentation unit that presents the results of the comparison made by the risk recognition unit. The sample brain state data and the user brain state data are data indicating the total brain volume or the volume of at least one specified area of ​​the brain, and the risk recognition unit recognizes an aging transition line indicating the transition of the user's brain state data from the past to the present based on the past user brain state data and the current user brain state data, and recognizes a risk line determined by referring to the transition of the sample brain state data according to age based on the sample brain state data associated with the individual characteristic corresponding to the user individual characteristic, and the risk presentation unit may present the aging transition line and the risk line in parallel for comparison.

[0106] The risk recognition unit may recognize the aging transition line and the risk line, and recognize an aging prediction line showing a transition of the user's brain state data predicted from the present to the future based on the aging transition line, and the risk presentation unit may present the aging transition line, the aging prediction line, and the risk line in a comparable juxtaposed manner. The system may include a user life characteristic recognition unit that recognizes a user life characteristic that is a life characteristic including at least one of a lifestyle habit and a life environment of the user, the data storage unit stores the sample brain state data in association with the individual characteristic and the life characteristic of the sample subject, and the risk recognition unit may recognize a trend line showing a transition of the sample brain state data according to age based on the sample brain state data associated with the life characteristic corresponding to the user life characteristic, and recognize the aging prediction line based on the trend line and the aging transition line. When the user lifestyle characteristics are changed, the risk recognition unit may re-recognize the aging prediction line based on the changed user lifestyle characteristics, and the risk presentation unit may present the aging prediction line recognized based on the user lifestyle characteristics before the change and the aging prediction line recognized based on the user lifestyle characteristics after the change in a comparable parallel manner or consecutively in a comparable manner.

[0107] The risk recognition unit may recognize the dementia risk of the user as a score based on a result of a comparison between the user brain state data and the sample brain state data associated with the individual characteristic corresponding to the user individual characteristic, and the risk presentation unit may present the score or a symbol corresponding to the score. The system may include a user life characteristic recognition unit that recognizes a user life characteristic that is a life characteristic of the user, and the data storage unit stores the sample brain state data in association with the individual characteristic and a life characteristic including at least one of a lifestyle habit and a life environment of the sample subject, and the risk recognition unit may recognize the dementia risk of the user as a score based on a result of a comparison between the user brain state data and the sample brain state data associated with the individual characteristic corresponding to the user individual characteristic and the sample brain state data associated with the life characteristic corresponding to the user life characteristic.

[0108] The system includes a user life characteristic recognition unit that recognizes user life characteristics that are life characteristics of the user, and the data storage unit stores the sample brain state data in association with the individual characteristics and life characteristics including at least one of the lifestyle habits and living environment of the sample subject, and when the user life characteristics are changed, the risk recognition unit re-recognizes the score based on the changed user life characteristics, and the risk presentation unit may present the score recognized based on the user life characteristics before the change or a symbol corresponding to the score and the score recognized based on the changed user life characteristics or a symbol corresponding to the score in a comparable parallel manner or consecutively.

[0109] The above also discloses a dementia risk presentation method for presenting a user's dementia risk, comprising the steps of: a data storage unit storing sample brain state data, which is data relating to the brain state of a sample subject, in association with individual characteristics including at least one of the age, gender, and physical information of the sample subject; a user brain state recognition unit recognizing the user brain state data, which is data relating to the brain state of the user; a user individual characteristic recognition unit recognizing user individual characteristics, which are the individual characteristics of the user; a risk recognition unit comparing the user brain state data with the sample brain state data associated with the individual characteristic corresponding to the user individual characteristic; and a risk presentation unit presenting a result of the comparison by the risk recognition unit.

[0110] Although the illustrated embodiment has been described above, the present invention is not limited to such an embodiment. For example, in the presentation system of the above embodiment, the value of the hippocampal volume (hippocampal volume) is used as data related to the brain state. In addition, when determining the dementia risk, the user's lifestyle characteristics are referred to in addition to the user's individual characteristics. However, the dementia risk presentation system of the present invention is not limited to such a configuration, and it is sufficient if it compares brain state data based on individual characteristics to determine the dementia risk. For example, instead of the hippocampal volume, a value related to the entire brain or other regions may be used, and lifestyle characteristics may not be referred to. In addition, other modifications that can be conceived by a person skilled in the art are included in the scope of the present invention according to the scope of the claims. [Explanation of symbols]

[0111] 10 Presentation System, 17 Database

Claims

1. a first database in which sample brain health data including at least one of sample brain state data, which is data related to the brain state of a sample subject, and sample cognitive ability data, which is data related to cognition, which is a brain function, is associated with individual characteristics including at least one of age, sex, and physical information of the sample subject; A first recognition unit that recognizes user brain health data including at least one of user brain state data, which is data related to a state of the user's brain, and user cognitive ability data, which is data related to cognitive ability, which is a function of the brain; A second recognition unit that recognizes a user individual characteristic that is an individual characteristic of the user; A system having a risk presentation unit that presents an aging trend line showing the trend of the user's brain health data from the past to the present based on the past and present user's brain health data, and a dementia risk line determined by referring to the age-related trend of the sample brain health data based on the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics.

2. In claim 1, The system, wherein the risk presentation unit includes a first score presentation unit that presents a score indicating the user's dementia risk or a symbol corresponding to the score based on a result of a comparison between the user's brain health data and the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics.

3. In claim 1 or 2, further comprising: a prediction unit that predicts an aging prediction line showing a predicted transition of the user's brain health data from the present to the future based on the aging transition line, The risk presentation unit includes a prediction presentation unit that presents the aging prediction line in addition to the aging trend line and the risk line.

4. In claim 3, The system includes a second score presentation unit that presents a score indicating the user's future dementia risk, or a symbol corresponding to the score, together with or instead of the aging prediction line, based on a result of a comparison between the user's brain health data for a first age on the aging prediction line and the sample brain health data associated with the individual characteristics corresponding to the individual characteristics of the user plus the first age.

5. In claim 3 or 4, In the first database, the sample brain health data is associated with the individual characteristics and life characteristics including at least one of a lifestyle and a life environment of the sample subject; A third recognition unit that recognizes a user life characteristic that is a life characteristic of the user, The prediction unit predicts the aging prediction line based on the sample brain health data and the aging trend line associated with the individual characteristics corresponding to the user's individual characteristics, the life characteristics corresponding to the user's life characteristics, and age.

6. In claim 5, The risk presentation unit includes a third score presentation unit that presents a score indicating the user's future dementia risk or a symbol corresponding to the score, together with or instead of the aging prediction line, based on a result of a comparison between the user's brain health data for a first age on the aging prediction line, the individual characteristics corresponding to the individual characteristics obtained by adding the first age to the user's individual characteristics, and the sample brain health data associated with the life characteristics corresponding to the user's life characteristics.

7. In claim 5 or 6, The prediction unit includes a comparison prediction unit that recognizes, for a same user, a first aging prediction line based on the first user lifestyle characteristic and a second aging prediction line based on the second user lifestyle characteristic; The system, wherein the risk presentation unit includes a comparison presentation unit that presents the first aging prediction line and the second aging prediction line side-by-side or consecutively in a comparative manner.

8. In claim 7, the risk presenter displays a first score indicating the user's future dementia risk or a symbol corresponding to the first score, based on a result of a comparison between the user's brain health data for a first age on the first aging prediction line, the individual characteristic corresponding to the individual characteristic obtained by adding the first age to the user's individual characteristic, and the sample brain health data associated with the life characteristic corresponding to the first user life characteristic; a fourth score presentation unit that presents a second score indicating the user's future dementia risk or a symbol corresponding to the second score, together with or instead of the first aging prediction line and the second aging prediction line, based on a result of a comparison of the user's brain health data for a first age on the second aging prediction line with the individual characteristic corresponding to the individual characteristic obtained by adding the first age to the user individual characteristic, and the sample brain health data associated with the life characteristic corresponding to the second user life characteristic.

9. In any one of claims 1 to 8, The system, wherein the sample brain state data and the user brain state data include data indicative of at least one of total brain volume, volume of at least one predetermined region of the brain, brain images, electroencephalograms, and cerebral blood flow.

10. A method for indicating a risk of dementia using a system capable of referring to a first database in which sample brain health data including at least one of sample brain state data, which is data related to a brain state of a sample subject, and sample cognitive ability data, which is data related to cognition, which is a brain function, of the sample subject is associated with individual characteristics including at least one of age, sex, and physical information of the sample subject, Recognizing user brain health data including at least one of user brain state data, which is data related to the state of the user's brain, and user cognitive ability data, which is data related to cognitive ability, which is a function of the brain; Recognizing a user individual characteristic, the user individual characteristic; and presenting risk using an aging trend line showing the progression of the user's brain health data from the past to the present based on the past and present user's brain health data, and a dementia risk line determined by referring to the progression of the sample brain health data according to age based on the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics.

11. In claim 10, further comprising: presenting a score indicating the user's dementia risk, or a symbol corresponding to the score, based on a result of a comparison between the user's brain health data and the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics.

12. In claim 10 or 11, further comprising: predicting an aging prediction line showing a predicted transition of the user's brain health data from the present to the future based on the aging trend line; The method, wherein presenting the risk includes presenting the aging projection line in addition to the aging line and the risk line.

13. In claim 12, further comprising: A method comprising presenting, together with or instead of the aging prediction line, a score indicating the user's future dementia risk, or a symbol corresponding to the score, based on a result of a comparison between the user's brain health data for a first age on the aging prediction line and the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics plus the first age.

14. In claim 12 or 13, In the first database, the sample brain health data is associated with the individual characteristics and life characteristics including at least one of a lifestyle and a life environment of the sample subject; The method further comprises recognizing a user lifestyle characteristic, the user lifestyle characteristic; The method, wherein the predicting includes predicting the aging prediction line based on the sample brain health data and the aging trend line associated with the individual characteristics corresponding to the user individual characteristics, the life characteristics corresponding to the user life characteristics, and age.

15. In claim 14, further comprising: A method comprising presenting, together with or instead of the aging prediction line, a score indicating the user's future dementia risk, or a symbol corresponding to the score, based on a result of a comparison of the user's brain health data for a first age on the aging prediction line with the individual characteristics corresponding to the individual characteristics of the user plus the first age, and the sample brain health data associated with the individual characteristics corresponding to the user's individual characteristics plus the first age, and the life characteristics corresponding to the user's life characteristics.

16. In claim 14 or 15, The predicting includes predicting, for a same user, a first aging prediction line based on a first of the user lifestyle characteristics and a second aging prediction line based on a second of the user lifestyle characteristics; The method, wherein presenting the risk includes presenting the first aging projection line and the second aging projection line side-by-side or consecutively for comparison.

17. In claim 16, further comprising: presenting, together with or instead of the first aging prediction line and the second aging prediction line, a first score indicating a future dementia risk of the user or a symbol corresponding to the first score based on a result of a comparison of the user brain health data for the first age on the first aging prediction line with the individual characteristic corresponding to the individual characteristic obtained by adding the user individual characteristic to the first age, and the sample brain health data associated with the life characteristic corresponding to the first user life characteristic, and a second score indicating a future dementia risk of the user or a symbol corresponding to the second score based on a result of a comparison of the user brain health data for the first age on the second aging prediction line with the individual characteristic corresponding to the individual characteristic obtained by adding the user individual characteristic to the first age, and the sample brain health data associated with the life characteristic corresponding to the second user life characteristic.

18. 10. A program having instructions for operating a computer as a system according to any one of claims 1 to 9.

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