Information processing method, information processing apparatus, and computer program

Abstract

To provide an information processing method or the like capable of determining the possibility of a disease by using a biosensor.SOLUTION: An information processing method includes acquiring sensor data by a biosensor for a sample collected from a subject, storing the acquired sensor data and identification information of the subject in association with each other, determining a possibility of a disease in the subject based on the sensor data, and outputting the determined possibility of the disease to an output destination corresponding to the identification information of the subject.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing method, an information processing device, and a computer program. [Background technology]

[0002] In recent years, research and development on biosensors using biological elements has been progressing. For example, Patent Document 1 discloses a low-cost odor sensor that includes a transistor with a gate electrode containing aluminum or aluminum oxide, an insect cell having an olfactory receptor placed on the gate electrode, and a detection device that detects the current generated in the transistor when the insect cell responds to an odor. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-113957 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 does not use a biosensor to determine the possibility of disease.

[0005] An object of the present disclosure is to provide an information processing method and the like that can determine the possibility of disease using a biosensor. [Means for solving the problem]

[0006] An information processing method according to one aspect of the present disclosure includes a computer executing a process in which sensor data is acquired from a biosensor for a sample collected from a subject, the acquired sensor data is stored in association with identification information of the subject, a determination is made of the possibility of disease in the subject based on the sensor data, and the determined possibility of disease is output to an output destination corresponding to the identification information of the subject.

[0007] An information processing device according to one aspect of the present disclosure includes a control unit that executes a process of acquiring sensor data from a biosensor for a sample collected from a subject, storing the acquired sensor data in association with identification information of the subject, determining the possibility of disease in the subject based on the sensor data, and outputting the determined possibility of disease to an output destination corresponding to the identification information of the subject.

[0008] A computer program according to one aspect of the present disclosure causes a computer to perform the following process: acquire sensor data from a biosensor for a sample collected from a subject; store the acquired sensor data in association with identification information of the subject; determine the possibility of disease in the subject based on the sensor data; and output the determined possibility of disease to an output destination corresponding to the identification information of the subject. [Effects of the Invention]

[0009] According to the present disclosure, biosensors can be used to determine the likelihood of disease. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram of a determination system. [Figure 2] FIG. 1 is a schematic diagram illustrating an example of the configuration of an olfactory sensor. [Figure 3] FIG. 2 is a block diagram showing the configuration of a determination device. [Figure 4] FIG. 10 is a diagram illustrating an example of information stored in a detection DB. [Figure 5] FIG. 2 is a block diagram showing the configuration of a terminal device. [Figure 6] FIG. 10 is a diagram showing an example of the change in luminescence intensity of a sensor cell over time. [Figure 7] FIG. 10 is a schematic diagram illustrating an example of a reception screen. [Figure 8]FIG. 10 shows examples of reference profiles for cells selected for disease determination and for cells not selected for disease determination. [Figure 9] FIG. 10 is a schematic diagram illustrating an example of a result screen showing a determination result. [Figure 10] FIG. 10 is a schematic diagram showing another example of a result screen showing a determination result. [Figure 11] 10 is a flowchart illustrating an example of a reference profile generation processing procedure. [Figure 12] 10 is a flowchart showing an example of a procedure for determining the possibility of a disease. [Figure 13] 10 is a flowchart illustrating an example of a processing procedure executed by a determination device according to a second embodiment. [Figure 14] 10A and 10B are diagrams illustrating examples of response profiles according to whether or not correction processing is performed. [Figure 15] FIG. 10 is a block diagram showing an example of the configuration of a determination device according to a third embodiment. [Figure 16] FIG. 1 is an explanatory diagram showing an overview of a learning model. [Figure 17] 11 is a flowchart showing an example of a procedure for a disease possibility determination process executed by the determination system of the third embodiment. [Figure 18] 10 is a flowchart illustrating an example of a re-learning process of a learning model. [Figure 19] 10 is a flowchart illustrating an example of a processing procedure executed by a determination system according to a fourth embodiment. [Figure 20] FIG. 13 is a schematic diagram showing an example of a result screen showing a determination result in the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] The present disclosure will be specifically described with reference to the drawings showing embodiments thereof.

[0012] (First embodiment) FIG. 1 is a schematic diagram of a determination system 100. The determination system 100 of this embodiment includes a determination device 1, a terminal device 2, a detection device 3, and an olfactory sensor 4. The olfactory sensor 4 is an example of a biosensor. The determination device 1 is communicably connected to the terminal device 2 and the detection device 3 via a network N such as the Internet. The determination system 100 determines the possibility of a disease in a subject based on the detection results of odor molecules in a specimen derived from the subject detected by the detection device 3 and the olfactory sensor 4, and provides a service in which the determination device 1 presents the determination results via the terminal device 2.

[0013] In the following embodiments, an example will be described in which a subject's possible disease is determined based on odor molecules detected in the subject's urine as a specimen (sample). The types of diseases to be determined are not limited, but include, for example, infectious diseases such as influenza and COVID-19, cancer, type 2 diabetes, lifestyle-related diseases such as heart disease and cerebrovascular disease, and neurological diseases such as Parkinson's disease and Alzheimer's disease. When the disease is cancer, it may be various types of cancer, such as lung cancer, esophageal cancer, breast cancer, stomach cancer, liver cancer, pancreatic cancer, gallbladder cancer, bile duct cancer, colon cancer, kidney cancer, bladder cancer, ovarian cancer, uterine cancer, prostate cancer, oral cancer, and pharyngeal cancer.

[0014] The determination device 1 is an information processing device capable of various information processing and information transmission and reception, such as a server computer, a personal computer, or a quantum computer. The determination device 1 acquires a signal indicating the odor response of the subject to urine, determines the possibility of disease in the subject based on the acquired signal, and provides the determination result to a user via the terminal device 2. The determination device 1 may be a local computer provided in the facility where the detection device 3 is installed.

[0015] The terminal device 2 is an information processing terminal used by the subject, and is, for example, a personal computer, a smartphone, a tablet terminal, etc. The terminal device 2 displays the determination result received from the determination device 1. The subject is an example of a user who receives the determination result. The determination result is not limited to being provided to the subject, but may also be provided to, for example, medical professionals, personnel at an analytical institution that performs the analysis, etc. The number of terminal devices 2 connected to the determination device 1 may be one or three or more.

[0016] The olfactory sensor 4 and the detection device 3 are managed by, for example, an analytical institution. An analytical institution is a facility that receives samples collected from subjects and analyzes the received samples. The olfactory sensor 4 includes cells 42 (see FIG. 2 ) that express olfactory receptors as detection elements, and outputs a signal indicating the response of the olfactory receptors to odor molecules. The detection device 3 detects the response signal from the olfactory sensor 4. In addition to functioning as a detector that detects the response signal described above, the detection device 3 also functions as a computer that processes various data and communicates with external devices, and transmits detection data of the response signal obtained by detection to the determination device 1 via the network N. The detector and the computer may be provided separately and configured to be able to communicate with each other. The number of detection devices 3 connected to the determination device 1 may be one or more than two.

[0017] FIG. 2 is a schematic diagram showing an example of the configuration of the olfactory sensor 4. FIG. 2 is a view of the olfactory sensor 4 as seen from above. The olfactory sensor 4 includes a substrate 41 and cells 42 arranged in wells 43 formed in the substrate 41. For example, a "384-well plate" having 384 wells 43 formed therein is used as the substrate 41. For simplicity of illustration, FIG. 2 shows a "24-well plate" having 24 wells 43 formed therein. In this embodiment, cells 42 consisting of a plurality of cells of the same type are arranged in each of the multiple wells 43. That is, a large number of cells of the same type are densely seeded in each well 43. Note that the cells 42 may be composed of a single type of cell. Each cell has an olfactory receptor. The cells 42 function as sensor cells that output signals indicating the response of the olfactory receptor to odor molecules. The cells 42 are an example of a sensor element. The olfactory receptor responds to specific odor molecules. By detecting a response signal based on the binding between the olfactory receptors provided in the olfactory sensor 4 and the odor molecules, it is possible to selectively detect the odor molecules contained in the urine of the subject.

[0018] Substrate 41 is made of a material such as glass, silicon, ceramics, resin, or metal. The surface of substrate 41 may be subjected to surface treatment such as plasma treatment, corona treatment, or UV-Ozone treatment, or may be coated with a polypeptide or the like. The shape of substrate 41 may be any shape that allows odor detection using cells 42 arranged on substrate 41, such as a rectangular plate. The size of substrate 41 is not particularly limited and can be set appropriately depending on the number of cells 42 arranged on substrate 41, etc.

[0019] The olfactory receptor can be derived from an animal. Examples of animals include insects, vertebrates, and mammals, and examples of olfactory receptors that can be used include flies, mosquitoes, mice, rats, rabbits, cows, dogs, and humans. Insect olfactory receptors are preferred. Insect olfactory receptors form heterocomplexes with olfactory receptor co-receptors and function as ion channels activated by odor molecules.

[0020] The amino acid sequences and coding sequences of olfactory receptors and olfactory receptor co-receptors are known or can be easily identified by sequence identity searches based on known sequences. Furthermore, amino acid mutations relative to the known amino acid sequences can be included. Amino acid mutations include, for example, amino acid substitutions, insertions, additions, or deletions.

[0021] The cells 42 may be specific cells that naturally express an olfactory receptor, or genetically modified cells into which an olfactory receptor gene has been incorporated. Genetically modified cells can be produced by transforming cells with a vector incorporating an olfactory receptor gene. When the olfactory receptor is an insect olfactory receptor, it is preferable to further incorporate a gene for an olfactory receptor co-receptor.

[0022] The cell 42 may further contain a fluorescent protein or a luminescent protein. When an odor molecule binds to an ionotropic olfactory receptor in the cell 42, cations such as calcium ions flow into the cell. By introducing into the cell 42 a gene that expresses a fluorescent protein whose fluorescence intensity changes depending on the ion concentration or a luminescent protein whose luminescence intensity changes, the response of the cell to the odor molecule can be detected by changes in fluorescence intensity or luminescence intensity. In other words, it is possible to detect odor molecules by changes in fluorescence intensity or luminescence intensity. Examples of such proteins include aequorin, yellow camelon, and GCaMP.

[0023] A calcium ion-dependent fluorescent dye may be introduced into the cell 42. By introducing the calcium ion-dependent fluorescent dye into the cell, the influx of calcium ions into the cell due to the binding of odor molecules to the olfactory receptors can be detected by a change in fluorescence intensity. Examples of such calcium ion-dependent fluorescent dyes include Fura-2, Fluo-3, and Fluo-4.

[0024] The detection device 3 detects a signal indicating a response of the cells 42 based on the binding between the olfactory receptors and odor molecules. The detection device 3 detects, for example, the luminescence intensity of each well 43 in the olfactory sensor 4 as the detection target. The detection device 3 includes, for example, a photomultiplier tube, and detects fluorescence or luminescence intensity based on changes in intracellular ion concentration. The response signal of the cells 42 detected by the detection device 3 is not limited to fluorescence or luminescence intensity, but may also be an electrical signal (potential) based on changes in intracellular ion concentration. The response signal may also be a moving or still image of the luminescence of the cells 42 captured by an imaging device such as a CCD camera.

[0025] As shown in Fig. 2, the olfactory sensor 4 of this embodiment includes a plurality of cells 42 arranged on a substrate 41. In the example shown in Fig. 2, the plurality of cells 42 are arranged in rows and columns at regular intervals on the upper surface of the substrate 41.

[0026] In general, olfactory receptors have selectivity for odor molecules. Therefore, by placing multiple cells 42 expressing different olfactory receptors on a substrate 41 and detecting the responses of each of these cells 42, it is possible to detect multiple types of odors. A single cell 42 may have one type of olfactory receptor, or multiple types of olfactory receptors. The olfactory sensor 4 may include multiple identical cells 42. The olfactory sensor 4 of this embodiment includes different types of cells 42, each of which has one type of olfactory receptor.

[0027] The number, types, and arrangement of olfactory receptors used in the olfactory sensor 4 can be determined appropriately depending on the odor molecules to be detected and the type of disease to be diagnosed. The olfactory receptors may be a specific combination for the odor molecules to be detected, or a comprehensive combination of multiple olfactory receptors may be used for various odor molecules. For example, the number of cells 42 mounted on one olfactory sensor 4, i.e., the total number of sensor cells mounted on one olfactory sensor 4, can be from 1 to 50,000, and the number of types of cells 42 mounted on one olfactory sensor 4 can be from 1 to 2,000.

[0028] The method for detecting the response signal is not limited to the above example, and any appropriate method can be used depending on the olfactory receptors in the olfactory sensor 4 and the types of odor molecules sensed by the olfactory receptors.

[0029] 3 is a block diagram showing the configuration of the determination device 1. The determination device 1 includes a control unit 11, a storage unit 12, and a communication unit 13. The determination device 1 may be a single computer, or may be a computer system configured by multiple computers and peripheral devices. The determination device 1 may be a virtual machine whose entity is virtualized, or may be a cloud.

[0030] The control unit 11 includes one or more arithmetic processing devices such as a central processing unit (CPU) or a graphics processing unit (GPU). The control unit 11 controls each component unit and executes processing using built-in memories such as a read-only memory (ROM) or a random access memory (RAM), a clock, a counter, etc. The functional units of the control unit 11 may be realized by software, or some or all of them may be realized by hardware such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0031] The storage unit 12 includes a nonvolatile memory such as a hard disk, a flash memory, or an SSD (Solid State Drive). The storage unit 12 may be separate from the determination device 1 and may be one or more external storage devices externally connected. The storage unit 12 stores various computer programs and data referenced by the control unit 11. The storage unit 12 stores a program 1P for causing a computer to execute processing related to determining the possibility of disease, and a detection DB (Data Base) 121. The storage unit 12 may further store a reference profile, which will be described later.

[0032] A computer program (program product) including program 1P may be provided by a non-transitory recording medium 1A on which the computer program is readably recorded. Storage unit 12 stores the computer program read from recording medium 1A by a reading device (not shown). Recording medium 1A may be, for example, a magnetic disk, optical disk, or semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications network and stored in storage unit 12. Program 1P may be a single computer program or may be composed of multiple computer programs. Program 1P may also be executed on a single computer or may be executed cooperatively by multiple computers.

[0033] The communication unit 13 includes a communication device that realizes communication via the network N. The control unit 11 transmits and receives data to and from the terminal device 2 and the detection device 3 via the communication unit 13.

[0034] The configuration of the determination device 1 is not limited to the above example, and may include, for example, an operation unit for accepting user operations, a display unit for displaying images, and the like.

[0035] The determination device 1 and the detection device 3 are not limited to transmitting and receiving data via the network N. The determination device 1 may, for example, be provided with an input interface for connecting to the detection device 3 and may receive data output from the detection device 3 via a signal line or the like.

[0036] 4 is a diagram showing an example of the contents of information stored in the detection DB 121. The detection DB 121 is a database that stores detection information on each of a plurality of subjects, subject information on subjects, and reference profile information on reference profiles.

[0037] The detection information table stores records that link information such as subject ID, collection date, detection device ID, detection date, detection data, and determination results, using, for example, subject ID as a key. The subject ID is identification information for uniquely identifying the subject collected from the subject. The subject ID may be an ID attached to a container that contains a specific subject. The subject ID is identification information for identifying the subject. The collection date indicates the date and time the subject was collected. The detection device ID is identification information for identifying the detection device 3 used to detect a response signal for the subject. The detection date indicates the date and time the response signal for the subject was detected by the detection device 3.

[0038] The detection data includes information indicating a response signal to the subject. The detection data is, for example, a profile of the response signal detected over time. In this embodiment, the profile of the response signal as detection data is time-dependent data of luminescence intensity. The detection data is generated for each cell 42 in the olfactory sensor 4. The determination result indicates the possibility of disease based on the detection data. The determination result may be indicated, for example, by the presence or absence of a possibility, or may be indicated by the degree of possibility classified into multiple levels. The information in the detection information table is collected, for example, via the detection device 3.

[0039] The subject information table stores records that link information such as terminal device information and subject attribute information, using, for example, the subject ID as a key. The detection information table and the subject information table are associated by the subject ID. The terminal device information is information that identifies the terminal device 2 used by the subject, and includes, for example, an address indicating the output destination of the determination result, a device ID, etc. The attribute information includes, for example, the subject's name, age, sex, health information, etc. The health information is information related to the subject's health condition, and may include information such as current symptoms, medical history, test results, and health check results. The information in the subject information table is collected, for example, through the terminal device 2.

[0040] The reference profile information table stores records linking information such as cell information, first reference profile information, and second reference profile information, using, for example, a disease ID as a key. The disease information is information for identifying the disease to be determined, and includes, for example, a disease ID and a disease name. The cell information is information for identifying the cells 42 in the olfactory sensor 4, and is information indicating the cell type selected for disease determination. The first reference profile information and the second reference profile information are information related to the first reference profile and the second reference profile corresponding to the cell information. The first reference profile and the second reference profile will be described in detail later. Note that FIG. 4 is an example, and the content of the information stored in the detection DB 121 is not limited. Furthermore, the data storage method shown in FIG. 4 is an example, and other storage formats are possible as long as the data content and the relationships between the data are maintained.

[0041] 5 is a block diagram showing the configuration of the terminal device 2. The terminal device 2 includes a control unit 21, a storage unit 22, a communication unit 23, a display unit 24, and an operation unit 25.

[0042] The control unit 21 includes one or more arithmetic processing devices such as a CPU, an MPU, a GPU, etc. The control unit 21 controls each component to execute processing using built-in memory such as a ROM or a RAM, a clock, a counter, etc.

[0043] The storage unit 22 includes a non-volatile memory such as a hard disk, flash memory, or SSD. The storage unit 22 stores various computer programs and data referenced by the control unit 21. The storage unit 22 stores a program 2P for causing a computer to execute processing related to obtaining a disease possibility determination result.

[0044] A computer program (computer program product) including the program 2P may be provided by a non-transitory recording medium 2A on which the computer program is readably recorded. The storage unit 22 stores the computer program read from the recording medium 2A by a reading device (not shown). The recording medium 2A may be, for example, a magnetic disk, an optical disk, or a semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications network and stored in the storage unit 22. The program 2P may be a single computer program or may be composed of multiple computer programs. The program 2P may also be executed on a single computer or may be executed cooperatively by multiple computers.

[0045] The communication unit 23 includes a communication device that realizes communication via the network N. The control unit 21 transmits and receives data to and from the determination device 1 via the communication unit 23.

[0046] The display unit 24 includes a display device such as a liquid crystal display, an organic EL (Electro Luminescence) display, etc. The display unit 24 displays various information including the determination result according to instructions from the control unit 21.

[0047] The operation unit 25 is an interface that accepts user operations. The operation unit 25 includes, for example, a keyboard, a mouse, a touch panel device with a built-in display, a speaker, a microphone, etc. The operation unit 25 accepts operation input from the user and sends a control signal according to the operation content to the control unit 21.

[0048] Figure 6 shows an example of the change in luminescence intensity of a sensor cell over time. The vertical axis of the graph in Figure 6 represents luminescence intensity, and the horizontal axis represents time (s). Figure 6 shows the detection results of luminescence intensity for each concentration of odor molecules when a solution containing a specific odor molecule is added to a cell expressing an olfactory receptor that binds to the odor molecule. In Figure 6, the concentrations of odor molecules are labeled as concentration 1, concentration 2, and concentration 3, in descending order.

[0049] As shown in Figure 6, the profile showing the change in luminescence intensity over time is concentration-dependent. In this embodiment, the characteristics of a specific group of odor molecules in urine that can be detected by the sensor cell are estimated based on the response profile of the luminescence intensity detected from the subject's urine sample, thereby determining the possibility of disease in the subject.

[0050] The flow of the determination process in the determination system 100 of this embodiment will be described below with a specific example.

[0051] The determination device 1 accepts an application for the determination service from a subject. A user (subject) who wishes to use the service applies via a reception screen 50 provided by the determination device 1, for example, using a terminal device 2.

[0052] 7 is a schematic diagram showing an example of the reception screen 50. When the determination device 1 receives a request for login using account information and a reception screen by an operation of the subject via the terminal device 2, the determination device 1 outputs the reception screen 50 to the terminal device 2 and displays it on the display unit 24, as shown in FIG.

[0053] The reception screen 50 includes a subject information reception field 501 for receiving input of subject information related to a subject to be assessed, and a disease information reception field 502 for receiving input of a target disease for which assessment is desired. The subject uses the operation unit 25 to input the name and other information of the subject to be assessed in the subject information reception field 501. In the case of a subject who has been registered as a user in advance, the determination device 1 may refer to the detection DB 121 to read out subject information corresponding to the logged-in account information, and display the read out subject information in the subject information reception field 501.

[0054] The disease information reception field 502 displays a plurality of selectable diseases that can be determined by the determination system 100. The subject can input the designation of one or more diseases for which the subject wishes to be determined, for example, by selecting a check box corresponding to each disease. In the example shown in Fig. 7, lung cancer is selected as the target disease.

[0055] The determination device 1 calculates the usage fee for the determination service according to the total number of target diseases selected as the desired diagnosis, so that the larger the total number, the higher the fee. The usage fee may vary depending on the type of target disease. The determination device 1 displays the calculated usage fee at the bottom of the disease information reception column 502.

[0056] When various information has been entered in the subject information reception field 501 and the disease information reception field 502 and an application button 503 specifying an application for a diagnosis is selected, the terminal device 2 transmits the received subject information and target disease to the determination device 1. The determination device 1 receives the subject information and target disease and accepts the application for a diagnosis. The determination device 1 stores the received subject information and target disease in the memory unit 12, and, if necessary, transmits the subject information and information corresponding to the target disease to the detection device 3 of the analytical laboratory that will perform the test.

[0057] The reception screen 50 may also include a payment method reception field 504 as shown in Fig. 7. The subject can specify the desired payment method by selecting a specific payment method from the payment method reception field 504. The terminal device 2 may execute processing for online payment or credit card payment with a predetermined payment server or the like, depending on the accepted payment method.

[0058] It should be noted that the information related to the application for the assessment service is not limited to that received through the terminal device 2, and the assessment device 1 may acquire subject information and the like by receiving input from the user, for example.

[0059] Once the application is completed, a urine collection container is sent to the subject. The urine collection container may be distributed through a designated store, analytical institution, etc. A label with, for example, a two-dimensional or three-dimensional code representing the subject's ID is attached to the urine collection container. The subject places the collected urine in the urine collection container and submits it to the analytical institution. The urine collection container may be submitted together with the subject's attribute information, such as the subject's name, the collection date, the application number issued when the test was applied for, etc.

[0060] The urine collection container is received at the analysis facility. The detection device 3 receives input from, for example, a person in charge, and acquires the subject ID, subject ID, collection date, etc. of the subject to be tested.

[0061] Next, the analytical laboratory detects the response signal using the olfactory sensor 4. A predetermined number of cells 42, each expressing a different olfactory receptor, are arranged in an array in the olfactory sensor 4. The olfactory sensor 4 is produced, for example, by integrating DNA containing a specific insect olfactory receptor coding sequence, a specific insect olfactory receptor co-receptor coding sequence, and a calcium-sensitive photoprotein coding sequence, all of which are placed under the control of a promoter sequence, into the chromosomal genomic DNA of each cell.

[0062] A predetermined amount of a test sample (e.g., a urine sample from a test subject) is brought into contact with the olfactory sensor 4, and the luminescence intensity of each cell is detected over time by the detection device 3. This allows a response profile to be acquired that indicates changes in luminescence intensity over time. The response profile is generated for each cell 42 in the olfactory sensor 4. The detection device 3 associates the acquired response profile with the test subject ID, test subject ID, collection date, detection device ID, detection date, etc. of the test subject to be tested, and transmits the response profile to the determination device 1.

[0063] The determination device 1 may generate a response profile by performing predetermined preprocessing on the detection data received from the detection device 3. The measurement of the luminescence intensity of the olfactory sensor 4 may be performed collectively for multiple arrayed sensor cells. In such cases, the raw detection data output from the detection device 3 is expected to include luminescence intensities at various times (detection times) for multiple sensor cells. Alternatively, the detection data may contain a mixture of detection values ​​for multiple urine samples derived from subjects. The determination device 1 generates a response profile by sorting the raw detection data including multiple detection values ​​for each sensor cell or each urine sample derived from the subject, arranging the luminescence intensities over time, and converting the data into a predetermined data format. If the times of the luminescence intensities in the raw detection data are different, the determination device 1 may unify the times of each data in the response profile by interpolating the data using a predetermined interpolation method.

[0064] The determination device 1 determines the possibility of a disease in a subject based on the response profile of the obtained urine sample derived from the subject. In this embodiment, the possibility of a disease is determined by comparing the response profile of the urine sample derived from the subject to be analyzed with a reference profile generated in advance.

[0065] The reference profile includes a first reference profile based on the luminescence intensity detected from a urine sample derived from a subject in good health, and a second reference profile based on the luminescence intensity detected from a urine sample derived from a subject in poor health. For example, when the target disease is lung cancer, a subject in good health refers to a non-cancer patient who is not suffering from cancer, and a subject in poor health refers to a lung cancer patient who is suffering from lung cancer. The reference profile is, for example, generated in advance and stored in the memory unit 12.

[0066] The first and second reference profiles are obtained by detecting the above-mentioned odor molecules using urine collected from a non-cancer patient (hereinafter also referred to as healthy urine) and urine collected from a lung cancer patient (hereinafter also referred to as lung cancer urine) with the olfactory sensor 4, and generating a response profile. The first and second reference profiles are prepared for each cell 42.

[0067] The first reference profile is preferably generated based on the response profiles of multiple healthy urine samples obtained from multiple non-cancer patients. Similarly, the second reference profile is preferably generated based on the response profiles of multiple lung cancer urine samples obtained from multiple lung cancer patients. For example, statistical values ​​of the luminescence intensity detected from each healthy urine sample are calculated for each time period, and the first reference profile is generated based on the obtained statistical values. As the statistical value, the mean or median is preferred, with the weighted mean, geometric mean, or median being more preferred, and the geometric mean being most preferred. Using a similar method, the second reference profile is generated based on the response profiles of multiple lung cancer urine samples obtained from multiple lung cancer patients.

[0068] Furthermore, one or more cells 42 suitable for diagnosing lung cancer, the disease to be diagnosed, are selected from the multiple types of cells 42 contained in the olfactory sensor 4. Cells suitable for diagnosing lung cancer are cells that exhibit reactivity according to lung cancer urine. Cells suitable for diagnosing lung cancer are preferably cells whose reactivity changes significantly depending on whether or not the subject is afflicted with lung cancer, and whose response profile shows a significant difference. Cells suitable for diagnosing lung cancer may be cells that react to odor molecules that are contained in relatively higher amounts in lung cancer urine than in healthy urine, or may be cells that react to odor molecules that are present in lower amounts in lung cancer urine than in healthy urine. In this embodiment, cells 42 that react to odor molecules that are contained in higher amounts in lung cancer urine than in healthy urine are considered to be cells 42 suitable for diagnosing lung cancer.

[0069] It is not easy to identify in advance the cells 42 that are suitable for diagnosing the disease to be diagnosed at the stage of generating the olfactory sensor 4. For this reason, in this embodiment, an olfactory sensor 4 equipped with multiple types of cells 42 is prepared, and for each disease to be diagnosed, the cells 42 that are suitable for diagnosing the disease are identified from among the multiple types of cells 42.

[0070] The method for identifying cells 42 suitable for disease determination is not limited, but for example, they can be identified based on the dissimilarity between the first reference profile and the second reference profile. The determination device 1 may calculate the dissimilarity between the first reference profile and the second reference profile, and identify cells 42 for which the calculated dissimilarity is equal to or greater than a predetermined first threshold as cells 42 for disease determination.

[0071] The dissimilarity between the first and second reference profiles may be, for example, the difference between the maximum luminescence intensity in the first and second reference profiles. The area enclosed by the first and second reference profiles (the integral of the intensity difference between the first and second reference profiles) may also be used as the dissimilarity. The larger the difference between the maximum intensities or the integral of the intensity difference between the reference profiles, the lower the similarity between the first and second reference profiles and the greater the change in reactivity depending on the presence or absence of disease. This means that the difference in response of cells 42 depending on the presence or absence of disease is greater, making the profile more useful for disease diagnosis. While the dissimilarity between the first and second reference profiles is used in the above example, similarity may also be used.

[0072] The selection of cells 42 for disease assessment may be performed taking into consideration the accuracy of disease assessment using the cells 42. For example, response profiles for multiple test samples are obtained for multiple cells 42 initially selected for disease assessment, and the acquired response profiles are used to assess the possibility of disease according to the assessment method described below. The assessment accuracy when using each of the initially selected cells 42 is calculated based on the assessment results for each cell 42 and the known characteristics of the test sample (healthy urine or lung cancer urine). The assessment accuracy includes, for example, the AUC of the ROC curve, recall, specificity, accuracy, and precision. Based on the calculated value of assessment accuracy, the type and number of cells 42 to be selected for disease assessment are specified so as to optimize each assessment accuracy (e.g., so as to maximize each assessment accuracy). The cells 42 for disease assessment are then secondarily selected in accordance with the type and number of the specified cells 42.

[0073] FIG. 8 shows examples of reference profiles for cells selected for disease determination and cells not selected for disease determination. FIG. 8A shows an example of a reference profile for cells selected for disease determination, and FIG. 8B shows an example of a reference profile for cells not selected for disease determination. In the case of cells for disease determination, the shapes of the response profiles are significantly different between healthy urine and pseudo-lung cancer urine (urine obtained by adding odor molecules derived from lung cancer to healthy urine). On the other hand, in the case of cells not for disease determination, no significant difference is observed in the response profiles between healthy urine and pseudo-lung cancer urine.

[0074] If the type of cells 42 suitable for determining a specific disease is already known, the step of selecting cells 42 for disease determination may be omitted.

[0075] The determination device 1 determines the possibility of lung cancer by determining the similarity between the response profile of the subject's urine sample corresponding to the selected disease determination cell 42 and each of the first and second reference profiles corresponding to the disease determination cell 42. If the similarity to the first reference profile is higher than the similarity to the second reference profile, it is determined that there is no possibility of lung cancer. If the similarity to the second reference profile is higher than the similarity to the first reference profile, it is determined that there is a possibility of lung cancer. The determination device 1 may determine the possibility of lung cancer in multiple stages, as a percentage, etc., depending on the value of the similarity.

[0076] The method for determining the degree of similarity is not limited, but for example, the absolute value of the difference between the luminescence intensity in the response profile of the subject's urine sample and the luminescence intensity in the reference profile may be calculated for each elapsed time, and the sum of the absolute values ​​of the calculated differences may be used as the index for determining the degree of similarity. A smaller sum of the absolute values ​​of the luminescence intensity differences indicates a higher degree of similarity.

[0077] When multiple cells 42 for disease determination are selected, the determination device 1 individually determines the possibility of lung cancer based on each of the cells 42 for disease determination, and then combines the individually determined possibilities of lung cancer to make an overall determination of the possibility of lung cancer. The determination device 1 determines, for example, a majority vote of the determination results based on each cell 42 as the overall determination. When making the overall determination, weighting may be performed so that a determination result based on a specific cell 42 is given a greater weight. The cells 42 to be weighted may be cells 42 that are more useful in disease determination, cells 42 that have a higher degree of similarity, cells 42 that have a smaller variation in response signals, etc.

[0078] The possibility of lung cancer may be determined using a machine learning technique. The determination device 1 has prepared in advance a determination model that outputs the possibility of lung cancer when the similarity between the response profile of each cell 42 for disease determination and a reference profile is input. The similarity includes at least one of the similarity between the response profile of the subject's urine sample and a first reference profile and the similarity between the response profile and a second reference profile.

[0079] The determination device 1 inputs the similarity between the response profiles of each cell 42 for disease determination into the determination model, and obtains the possibility of lung cancer output from the determination model. The type of cell 42 may be input to the determination model along with the similarity. The determination result of the possibility of lung cancer output from the determination model corresponds to the overall determination result. In this case, individual determination based on each cell 42 may be omitted.

[0080] If the type of cells 42 suitable for diagnosing a specific disease is known, an olfactory sensor 4 may be individually generated for each type of disease to be diagnosed using only the cells 42 suitable for disease diagnosis. In this case, luminescence intensity is detected using an individual olfactory sensor 4 corresponding to the selected target disease, and the possibility of disease is determined based on the detection results of each cell 42 in the individual olfactory sensor 4. The determination device 1 associates a series of obtained information, such as the subject ID, subject ID, collection date, detection device ID, detection date, response profile, and determination result, and stores them in the detection DB 121. The determination device 1 also outputs the determination result to the terminal device 2 of the subject.

[0081] 9 is a schematic diagram showing an example of a result screen 51 showing the determination result. The result screen 51 includes a first display section 511 that displays information about the subject to be determined, and a second display section 512 that displays the determination result.

[0082] The determination device 1 displays information about the subject and the specimen to be determined on the first display unit 511 based on the information stored in the detection DB 121. The first display unit 511 displays, for example, the subject's name, the collection date of the specimen, and the specimen ID.

[0083] The determination device 1 also displays the determination result indicating the possibility of the disease on the second display unit 512. When determination results for multiple types of diseases are obtained, the result screen 51 may be configured to include multiple second display units 512 corresponding to the respective target diseases. Each second display unit 512 displays the target disease and the determination result related to the target disease.

[0084] Fig. 10 is a schematic diagram showing another example of the result screen 51 showing the determination results. In the example shown in Fig. 10, the second display section 512 of the result screen 51 further includes a detection result display field 513 that displays the detection results for the analyte detected by the olfactory sensor 4. The detection result display field 513 displays a response profile corresponding to the analyte.

[0085] In addition to the response profile corresponding to the subject, the criteria used in the disease assessment process, i.e., the first and second reference profiles, may be displayed in the detection result display field 513. For ease of explanation, only one type of response profile is shown in Fig. 10, but the detection result display field 513 may also display multiple response profiles indicating the detection results for each cell 42 used for disease assessment. The detection result display field 513 may also display an image showing the luminescence of the cells 42 in the olfactory sensor 4, captured by an imaging device.

[0086] When response profiles for multiple cells 42 are displayed in the detection result display field 513, the determination device 1 may determine the display order on the screen based on the usefulness in disease determination or the similarity between each response profile and a reference profile. For example, the determination device 1 causes the detection results of cells 42 with high usefulness or similarity to be preferentially displayed in the detection result display field 513. The determination device 1 may also cause the detection results for a predetermined number of cells 42 to be displayed in the detection result display field 513 in descending order of priority. The determination device 1 may determine the display order based on the contribution of input information in the above-mentioned determination model so that cells 42 with higher contribution of similarity are displayed preferentially. The contribution can be calculated based on, for example, a SHAP (Shapley Additive exPlanation) value, a Gini coefficient, a LIME (Local Interpretable Model-Agnostic Explanations), a PFI (Permutation Feature Importance), or the like.

[0087] The detection result is not limited to being presented to the subject via the terminal device 2. The determination device 1 may output the detection result to, for example, another computer or a predetermined printing device.

[0088] 11 is a flowchart showing an example of a reference profile generation process procedure. The process in the flowchart below is executed by the control unit 11 in accordance with a program 1P stored in the storage unit 12 of the determination device 1.

[0089] The control unit 11 of the determination device 1 acquires, via the detection device 3, response profiles detected from healthy urine samples from multiple non-cancer patients and response profiles detected from lung cancer urine samples from multiple lung cancer patients (step S11). The response profiles are, for example, time-course data of luminescence intensity, and are generated for each cell 42 in the olfactory sensor 4. Each response profile may be associated with information indicating the corresponding cell 42.

[0090] The control unit 11 generates a first reference profile based on each response profile for the plurality of normal urine samples (step S12). The control unit 11 generates the first reference profile by, for example, calculating the geometric mean of the luminescence intensity detected from each normal urine sample for each time period.

[0091] The control unit 11 generates a second reference profile based on each response profile for the plurality of lung cancer urine samples (step S13). The control unit 11 generates the second reference profile by, for example, calculating the geometric mean of the luminescence intensity detected from each lung cancer urine sample for each time period.

[0092] The control unit 11 selects one or more cells 42 to be used for lung cancer diagnosis from among the multiple types of cells 42 contained in the olfactory sensor 4 (step S14). The control unit 11, for example, calculates the maximum luminescence intensity or the intensity difference integral value in each of the first and second reference profiles for each of the cells 42 contained in the olfactory sensor 4. The control unit 11 selects the cells 42 for which the calculated difference in the maximum luminescence intensity or the intensity difference integral value is equal to or greater than a first threshold value set in advance as the cells 42 for lung cancer diagnosis. In step S14, the control unit 11 may specify the type and number of cells 42 to be selected for disease diagnosis based on the accuracy of disease possibility diagnosis when each cell 42 is used, so as to optimize each diagnosis accuracy, and select the cells 42 for lung cancer diagnosis according to the identification results.

[0093] The control unit 11 associates the disease information, the cell information of the selected cell 42 for lung cancer determination, and the first and second reference profiles corresponding to the cell 42 and stores them in the detection DB 121 (step S15), and then ends the series of processes.

[0094] The control unit 11 executes the above-described process for all diseases that can be diagnosed in the diagnosis service, and generates and stores the first and second reference profiles for various disease diagnosis. The above-described process may be executed at a stage prior to the operational stage in which the diagnosis service is performed.

[0095] 12 is a flowchart showing an example of a procedure for determining the possibility of a disease. The following process is executed by the control unit 11 in accordance with a program 1P stored in the storage unit 12 of the determination device 1, and also executed by the control unit 21 in accordance with a program 2P stored in the storage unit 22 of the terminal device 2.

[0096] The control unit 21 of the terminal device 2 receives the subject information and the target disease of the subject who wishes to be diagnosed based on the subject's operation using the reception screen (step S20). The control unit 21 transmits the received subject information and the target disease to the determination device 1 (step S21).

[0097] The control unit 11 of the determination device 1 receives the subject information and the target disease (step S22).

[0098] The control unit 11 acquires a response profile generated from a urine sample derived from the subject through the detection device 3 (step S23). A response profile is generated for each cell 42 in the olfactory sensor 4. The response profile is associated with a subject ID, subject ID, collection date, detection device ID, detection date, etc. The control unit 11 may generate a response profile by acquiring raw detection data obtained by detection from the detection device 3 and performing various preprocessing operations on the acquired raw detection data. The control unit 11 associates the subject ID, subject ID, collection date, detection device ID, detection date, and response profiles by cell type and stores them in the detection DB 121 (step S24).

[0099] The control unit 11 calculates the degree of similarity between the response profile of the urine specimen derived from the subject and each of the first and second reference profiles for each cell 42 for disease determination according to the target disease (step S25).

[0100] The control unit 11 compares the calculated similarities and identifies the health state corresponding to the reference profile with the highest similarity as the health state of the subject, thereby individually determining whether or not the subject has a possible disease (step S26). In step S26, the presence or absence of a possible disease is determined for each cell 42. The control unit 11 then makes an overall determination as to whether or not the subject has a possible disease, for example, by a majority vote of the individual determinations for each cell 42 (step S27). The control unit 11 stores the obtained determination result in the detection DB 121 in association with the subject ID (step S28).

[0101] The control unit 11 generates a result screen showing the obtained disease possibility determination result (step S29). The control unit 11 transmits the generated result screen to the terminal device 2 corresponding to the subject identified by the subject information acquired in step S23 (step S30).

[0102] The control unit 21 of the terminal device 2 receives the result screen from the determination device 1 (step S31). The control unit 21 displays the received result screen on the display unit 24 (step S32), and ends the series of processes.

[0103] In the above process, the subject may request the display of a result screen using the terminal device 2 and receive the result screen in response to the request, thereby being able to check the determination result at any time. The determination result may be provided through a web service provided by the determination device 1.

[0104] In the above-described process, the control unit 11 of the determination device 1 may update the reference profile based on the response profile and determination result newly stored in the detection DB 121 by the process of step 28. The control unit 11 extracts one or more newly added response profiles from the detection DB 121, for example, at appropriate intervals. The control unit 11 regenerates a first reference profile based on the multiple extracted response profiles to which a response profile determined to indicate a lack of cancer has been newly added. Alternatively, the control unit 11 regenerates a second reference profile based on the multiple extracted response profiles to which a response profile determined to indicate a possibility of cancer has been newly added.

[0105] In the above, the olfactory sensor 4 is used to determine the possibility of disease based on detection data for a urine sample. The biosensor is not limited to an olfactory sensor having olfactory receptors, as long as it can determine the possibility of disease based on the sensor data. The biosensor may also be configured to include other biological elements (e.g., enzymes, antibodies, DNA, cells, etc.). The specimen to be analyzed is not limited to urine, but may also be, for example, blood, sweat, saliva, tears, exhaled breath, skin gas, tissue fluid, synovial fluid, follicular fluid, cerebrospinal fluid, semen, milk, vaginal fluid, etc. Furthermore, the subject for which the possibility of disease is determined is not limited to humans, but may also be an animal.

[0106] According to this embodiment, the possibility of disease can be determined based on detection data from a biosensor. The practicality of the biosensor can be improved, and health management services using the biosensor can be provided. By using a response profile that indicates the response of olfactory receptors, the possibility of disease can be determined with high accuracy. By determining the possibility of disease by comparing with a pre-generated reference profile, the process of determining the possibility of disease becomes easier.

[0107] The subject can obtain the judgment result by submitting the test sample and registering the necessary information, which reduces the burden required for testing and increases the utilization of the service. The judgment result can be confirmed using the terminal device 2, so the judgment result can be reliably grasped at any time. In addition to the judgment result, the detection result is displayed in a visually recognizable manner, so the results can be confirmed more reliably and in detail. In addition to the subject's own detection result, the reference profile that serves as the judgment standard is displayed, improving the explainability of the judgment result.

[0108] (Second embodiment) In the second embodiment, correction is performed to eliminate individual differences in response profiles. In the following embodiments, differences from the first embodiment will be mainly described, and the same reference numerals will be used to designate components common to the first embodiment, and detailed descriptions thereof will be omitted.

[0109] The response signal detected from the test subject may vary from individual to individual due to various factors. For example, due to the influence of impurities contained in the urine sample, even if the urine sample contains the same concentration of odor compounds, the luminescence intensity detected from a urine sample containing a large amount of impurities may be greater or smaller than that detected from a urine sample containing fewer impurities. In other words, due to the influence of impurities, individual differences occur in the correlation between the concentration of odor molecules and the luminescence intensity. The response signal may also vary from individual to individual due to the influence of the balance of odor molecules. Individual differences may occur not only in luminescence intensity but also in various response signals.

[0110] When performing a determination based on a response profile, the occurrence of such individual differences leads to a decrease in determination accuracy. In particular, as explained in the first embodiment, when determining whether a urine sample resembles a healthy urine profile or a lung cancer urine profile by comparing a target response profile with a reference profile, the possibility of erroneous determination due to the influence of individual differences increases. In this embodiment, a correction process is performed to eliminate individual differences in the response profile, thereby improving determination accuracy.

[0111] 13 is a flowchart showing an example of a processing procedure executed by the determination device 1 of the second embodiment. The processing of FIG. 13 is executed, for example, between step S24 and step S25 of the first embodiment.

[0112] The control unit 11 of the determination device 1 selects one or more cells 42 to be used for correcting individual differences from among the multiple types of cells 42 contained in the olfactory sensor 4 (step S41). Cells 42 for correcting individual differences are preferably cells 42 whose reactivity to the target disease (e.g., lung cancer) is lower than that of cells for disease determination. More preferably, cells for correcting individual differences are cells whose reactivity does not change significantly depending on whether or not the subject has lung cancer, and whose response profile does not show a significant difference. Cells for correcting individual differences may be cells that respond to odor molecules that are present at approximately the same concentrations in both healthy urine and lung cancer urine, or cells that respond to odor molecules that have been artificially added to urine but that are not present in the urine prior to addition.

[0113] The control unit 11 selects cells 42 for correcting individual differences based on, for example, the dissimilarity between a first reference profile based on non-cancerous urine (healthy urine) and a second reference profile based on lung cancer urine. Specifically, the control unit 11 calculates the dissimilarity between the first reference profile and the second reference profile for each cell 42 included in the olfactory sensor 4. The control unit 11 selects cells 42 for which the calculated dissimilarity is less than a predetermined second threshold as cells 42 for correcting individual differences. The dissimilarity between the first reference profile and the second reference profile may be, for example, the difference between the maximum luminescence intensity in the first reference profile and the maximum luminescence intensity in the second reference profile, or the integral value of the intensity difference between the first reference profile and the second reference profile, as in the case of identifying cells 42 for disease assessment. Note that, although the dissimilarity between the first reference profile and the second reference profile is used in the above, similarity may also be used.

[0114] Preferably, the first and second reference profiles are each generated based on response profiles of multiple urine samples obtained from multiple individuals. The second threshold value may be the same as or smaller than the first threshold value used to select the cells 42 for disease determination. The cells 42 for correcting individual differences may be selected from among the cells 42 in the olfactory sensor 4, other than the cells 42 for disease determination. If the type of the cells 42 for correcting individual differences is known, the above-described selection step may be omitted.

[0115] The control unit 11 acquires a correction profile corresponding to the selected correction cell 42 (step S42). The correction profile may be a response profile generated based on statistical values ​​of the first and second reference profiles. When the maximum value of the luminescence intensity or the integral value of the intensity difference between the first and second reference profiles is approximately zero, either the first or second reference profile may be used as the correction profile. A correction profile is acquired for each correction cell 42.

[0116] The control unit 11 calculates a correction coefficient (correction value) for correcting the response profile based on the acquired correction profile and the response profile of the subject's urine sample (step S43).

[0117] The correction coefficient can be calculated, for example, by the following method. The area enclosed by the response profile and correction profile of the subject's urine sample and the x-axis (time axis) is divided into sections at predetermined time intervals. For each section, the ratio between the area of ​​the first region enclosed by the response profile and the area of ​​the second region enclosed by the correction profile is calculated. The geometric mean value of the ratios in all sections is used as the correction coefficient. Note that the method for calculating the correction coefficient is not limited to the above example, and any method may be used as long as it is capable of correcting individual differences in the response profile. The correction coefficient may be adjusted depending on the type of determination cell 42. For example, the correction coefficient for each determination cell 42 may be calculated by multiplying the above correction coefficient calculated based on the correction profile and the response profile by a predetermined coefficient that is set in advance for each determination cell 42.

[0118] When multiple cells 42 are selected as correction cells 42, the control unit 11 may calculate the above-mentioned correction coefficient for each correction cell 42 and determine the final correction coefficient by obtaining the statistical value (e.g., geometric mean, median, etc.) of each calculated correction coefficient.

[0119] The control unit 11 uses the calculated correction coefficient to correct the response profile of each cell 42 selected for disease determination (step S44). Specifically, the control unit 11 multiplies each luminescence intensity in the response profile for disease determination by the correction coefficient to generate a corrected response profile. The control unit 11 uses the corrected response profile to execute the processes from step S25 onward, thereby determining the possibility of disease based on the corrected response profile.

[0120] According to the above-described process, a correction coefficient is calculated based on the response profile and the reference profile of the correction cell 42 selected from the cells 42 in the olfactory sensor 4, and the response profile of the disease assessment cell 42 can be corrected using the calculated correction coefficient. The correction cell 42 does not exhibit different response signal properties depending on a specific property (e.g., whether or not there is a possibility of a disease), and the response profile is hardly dependent on the specific property. On the other hand, the disease assessment cell 42 exhibits different response signal properties depending on the specific property, and the response profile is strongly dependent on the specific property.

[0121] Figure 14 shows examples of response profiles with and without correction processing. In the example shown in Figure 14A, the response profile before correction shows a decrease in overall luminescence intensity due to the influence of impurities that weaken activity. The response profile after correction has been corrected to increase luminescence intensity. The correction shown in Figure 14A can reduce the possibility of erroneously determining that a subject who may have a disease is not likely to have a disease.

[0122] In the example shown in Figure 14B, the response profile before correction shows a high overall luminescence intensity due to the influence of contaminants that enhance activity. The response profile after correction has been corrected to show a low luminescence intensity. The correction shown in Figure 14B reduces the possibility of erroneously determining that a subject who is not likely to have a disease is likely to have a disease.

[0123] According to this embodiment, it is possible to correct for individual differences between subjects and suppress a decrease in determination accuracy due to individual differences. When measuring a biological sample using a biosensor that uses a biological element, it is thought that differences in response signals may occur due to various factors. According to this embodiment, it is possible to appropriately eliminate such individual differences.

[0124] According to this embodiment, cells for individual correction can be efficiently determined based on the characteristics of the response profile. By preparing a biosensor with multiple sensor cells and acquiring the response profile of each cell, appropriate cells for individual correction can be selected according to the various properties to be evaluated.

[0125] (Third embodiment) In the third embodiment, a learning model is used to determine the likelihood of disease.

[0126] 15 is a block diagram showing an example of the configuration of a determination device 1 according to the third embodiment. The determination device 1 according to the third embodiment stores a learning model 122 in the storage unit 12. The learning model 122 is a machine learning model that has learned predetermined training data. The learning model 122 is expected to be used as a program module that constitutes part of artificial intelligence software.

[0127] 16 is an explanatory diagram showing an overview of the learning model 122. The learning model 122 receives a response profile of luminescence intensity detected from a urine sample of a subject as input, and outputs information indicating whether or not the subject is likely to have a target disease corresponding to the response profile. The learning model 122 of this embodiment is composed of a plurality of individual learning models 123. In the following description, the individual learning models 123 are also referred to as a first learning model 123A, a second learning model 123B, and a third learning model 123C.

[0128] The first learning model 123A is a model for determining the possibility of lung cancer as a first disease, and receives a response profile relating to a plurality of cells 42 as input and outputs the presence or absence of the possibility of lung cancer. The second learning model 123B is a model for determining the possibility of prostate cancer as a second disease, and receives a response profile relating to a plurality of cells 42 as input and outputs the presence or absence of the possibility of prostate cancer. The third learning model 123C is a model for determining the possibility of colon cancer as a third disease, and receives a response profile relating to a plurality of cells 42 as input and outputs the presence or absence of the possibility of colon cancer. In this way, the individual learning model 123 is constructed to correspond to each disease to be determined. Note that the number of individual learning models 123 included in the learning model 122 may be four or more. Since all the individual learning models 123 have the same configuration, the configuration of the first learning model 123A will be described below.

[0129] The first learning model 123A is, for example, a convolutional neural network (CNN), which is a type of neural network. The first learning model 123A includes an input layer to which each response profile is input, an output layer that outputs the presence or absence of the possibility of lung cancer, and an intermediate layer (hidden layer). The intermediate layer may include a convolutional layer, a pooling layer, a fully connected layer, etc. The intermediate layer has multiple nodes that extract features of the response profile and passes the extracted features using various parameters to the output layer. When a response profile is input to the input layer, a calculation is performed in the intermediate layer using the learned parameters, and output information indicating the classification result of the presence or absence of the possibility of lung cancer is output from the output layer.

[0130] The input data input to the first learning model 123A is a response profile of the cells 42 selected for lung cancer determination from among all the cells 42 in the olfactory sensor 4. The input data of the first learning model 123A may include the type of the cells 42 corresponding to the response profile.

[0131] The first learning model 123A can be generated by preparing training data in which labels indicating the possibility of lung cancer are associated with response profiles, and using the training data to train an untrained neural network. For example, a diagnosis by an experienced physician can be used as the correct label. The training data includes response profiles detected from multiple subjects who are likely to have lung cancer and response profiles detected from multiple subjects who are not likely to have lung cancer. The learning model 122 learns the relationship between these response profiles and the possibility of lung cancer.

[0132] The determination device 1 inputs multiple response profiles contained in training data into the input layer of a pre-training neural network model, undergoes arithmetic processing in the intermediate layer, and obtains the presence or absence of a possibility of lung cancer output from the output layer. The determination device 1 compares the presence or absence of a possibility of lung cancer output from the output layer with the presence or absence of a possibility of lung cancer included in the training data, and optimizes parameters such as weights between neurons using, for example, backpropagation so that the presence or absence of a possibility of lung cancer output from the output layer approaches a correct value. Note that the learning model 122 may be constructed by an external device and deployed to the determination device 1.

[0133] The determination device 1 provides a response profile of a cell 42 for disease determination corresponding to the target disease to each individual learning model 123 corresponding to one or more diseases selected as the target of determination, and obtains the possibility of the target disease output from each individual learning model 123.

[0134] The input data to the individual learning model 123 is not limited to an image representing a response profile of the luminescence intensity, but may also be a value of the luminescence intensity over time. Of course, the input to the individual learning model 123 may also be a response signal other than the luminescence intensity.

[0135] The input data input to the individual learning model 123 may further include subject information about the subject, as shown in Fig. 16. The subject information that serves as input elements to the individual learning model 123 includes, for example, attributes such as the age and gender of the subject corresponding to the subject, and health information such as current symptoms, medical history, test results, and health check results.

[0136] The individual learning model 123 is not limited to estimating whether or not there is a possibility of a disease, but may output, for example, a probability level classified into multiple categories according to the degree of probability, or may output a numerical value indicating the probability as a percentage.

[0137] The individual learning model 123 may be configured to take as input response profiles for samples collected from a subject on multiple collection dates, i.e., time-series response profiles, and output the possibility of disease. In this case, the individual learning model 123 may take as input the most recent response profile and the response profiles from the past few times, and output the possibility of disease in the subject at present, or the possibility of disease in the future.

[0138] The configuration of the learning model 122 is not limited to the above example, and may be any configuration that can identify the possibility of a target disease from the time-series data of the response signal. The learning model 122 may be a model constructed using other learning algorithms, such as a recurrent neural network (RNN), a graph neural network (GNN), a transformer, a support vector machine (SVM), logistic regression, or eXtreme Gradient Boosting (XGBoost).

[0139] As the individual learning model 123, one model may be constructed for each type of cell 42. In this case, the determination device 1 may derive an overall determination result for one disease based on the individual determination results output from each individual learning model 123 corresponding to the cell type. The determination device 1 may make an overall determination of the possibility of disease using a determination model, as in the first embodiment. In this case, the determination model may be configured to receive the possibility of disease for each type of cell 42 output from the learning model 122 as input and output the possibility of disease. Note that the learning model 122 may be configured to output the possibility of various diseases using a single learning model 122.

[0140] FIG. 17 is a flowchart showing an example of a procedure for determining the possibility of a disease, which is executed by the determination system 100 of the third embodiment.

[0141] The control unit 21 of the terminal device 2 executes the same processes as steps S20 to S21, and receives the subject information and the target disease (step S50) and transmits them (step S51).

[0142] The control unit 11 of the determination device 1 executes the same processes as steps S22 to S24 to receive the subject information and the target disease (step S52), acquire a response profile (step S53), and store the acquired information in the detection DB 121 (step S54).

[0143] The control unit 11 selects an individual learning model 123 corresponding to the received target disease and the disease determination cells 42 corresponding to the target disease from among multiple individual learning models 123 included in the learning model 122 stored in the memory unit 12 (step S55).

[0144] The control unit 11 inputs the response profile of the corresponding disease assessment cell 42 into each selected individual learning model 123 (step S56). The control unit 11 may read out subject information of the subject identified by the subject ID corresponding to the response profile based on the information stored in the detection DB 121, and input the subject's attributes and health information corresponding to the read response profile into the individual learning model 123. The control unit 11 acquires the possibility of disease output from the individual learning model 123 (step S57). The individual learning model 123 outputs the presence or absence of the possibility of disease, for example, for each type of disease. Thereafter, the control unit 11 performs the same processes as steps S28 to S32.

[0145] The determination device 1 may execute the above-described re-learning of the learning model 122. FIG.

[0146] The control unit 11 of the determination device 1 acquires a doctor's diagnosis result regarding a disease in the subject (step S61). The diagnosis result may be acquired, for example, by accepting an input from the subject through the subject's terminal device 2, or may be acquired by communication with a computer at a medical institution.

[0147] The control unit 11 re-learns the learning model 122 using the possibility of the disease indicated by the acquired doctor's diagnosis result, and updates the learning model 122 (step S62). Specifically, the control unit 11 re-learns using the response profile input into the individual learning model 123 corresponding to the disease for which the diagnosis result was obtained and the possibility of the disease indicated by the diagnosis result as training data, and updates the individual learning model 123. The control unit 11 optimizes parameters so that the possibility of the disease output from the individual learning model 123 approximates the diagnosis result, and regenerates the individual learning model 123.

[0148] According to this embodiment, the possibility of disease can be easily and accurately determined using the learning model 122. By constructing an individual learning model 123 according to the disease type, the accuracy of determining the possibility of disease can be improved. By using the subject's attributes and health information as input elements to the learning model 122, the possibility of disease can be determined taking into account more diverse information, and improved accuracy is expected.

[0149] By re-learning the learning model 122 based on the doctor's diagnosis results, the learning model 122 can be optimized through the operation of this system.

[0150] (Fourth embodiment) In the fourth embodiment, the possibility of a disease is determined using a plurality of determination methods. The determination device 1 of the fourth embodiment not only determines the possibility of a disease based on sensor data obtained by the olfactory sensor 4, but also acquires the result of the determination of the possibility of a disease determined by other determination methods.

[0151] Other determination methods are not particularly limited as long as they are capable of determining the possibility of disease, but examples include methods using image data obtained by an imaging inspection device, detection data obtained by a physical sensor, detection data obtained by a chemical sensor, and detection data obtained by a biosensor using biological elements other than cells having olfactory receptors.

[0152] Examples of methods using image data include determination based on image data obtained by an X-ray inspection device, a CT inspection device, an MRI inspection device, a PET inspection device, an ultrasound inspection device, etc. Examples of methods using detected data of physical quantities include determination based on detected data of physical quantities such as body temperature, pulse, heart rate, intravascular pressure, and intraocular pressure. Examples of methods using detected data from biosensors equipped with biological elements such as enzymes, antibodies, nucleic acids, microorganisms, sugar chains, and lipid membranes include determination based on detected data of chemical substance amounts such as urea, glucose, monoamines, sucrose, phospholipids, total cholesterol, triglycerides, amino acids, IgI, IgA, IgM, and albumin. Other determination methods may be determination based on test data obtained by genetic testing, chromosome testing, cell surface marker testing, biomarker (e.g., amino acids, microRNA, nucleic acids, proteins, etc.) testing, etc. The determination process by each method may be performed by the determination device 1 or externally. Determination of the possibility of disease by other determination methods may be a secondary determination performed based on the results of the primary determination, with the determination by the olfactory sensor 4 being the primary determination.

[0153] 19 is a flowchart showing an example of a processing procedure executed by the determination system 100 of the fourth embodiment. After performing a comprehensive determination of the possibility of disease by the processing up to step S27 of the first embodiment, for example, the determination system 100 executes the following processing.

[0154] The control unit 11 of the determination device 1 acquires a result of the determination of the possibility of disease determined by a determination method different from the determination method using the olfactory sensor 4 (step S71). The determination result by the different determination method may be acquired, for example, by communication with a computer of another testing institution. The determination result may be associated with the subject's identification information.

[0155] The control unit 11 derives a final judgment result based on the acquired judgment results from the different judgment methods and the previously acquired judgment result from the judgment method using the olfactory sensor 4 (step S72). The control unit 11 determines the final judgment result as, for example, a majority vote of the judgment results from the judgment methods. When making the final judgment, weighting may be performed so that the weight of the judgment result based on a specific judgment method is increased. Thereafter, the control unit 11 executes the same processes as steps S28 to S32.

[0156] Fig. 20 is a schematic diagram showing an example of a result screen 51 showing the determination result of the fourth embodiment. In the example shown in Fig. 20, the result screen 51 includes a first display section 511 that displays information about the subject to be determined, and a plurality of second display sections 512 corresponding to each of a plurality of target diseases selected by the subject.

[0157] Each second display unit 512 displays the disease type of the target disease and the determination result for that target disease. The determination device 1 displays the determination results of the possibility of disease by each determination method for each type of determination method on the second display unit 512 based on the determination results by each determination method. The determination device 1 also displays the final determination result based on each determination result on the second display unit 512.

[0158] According to this embodiment, by combining a plurality of determination methods, it is possible to further improve the accuracy of determining the possibility of disease. By providing the subject with the determination results for each determination method, it becomes possible to understand the determination results in detail.

[0159] The following additional notes are provided regarding the above-described embodiments. (Appendix 1) acquiring sensor data from a biosensor for a sample collected from a subject; storing the acquired sensor data and the target's identification information in association with each other; determining a likelihood of disease in the subject based on the sensor data; The determined possibility of the disease is output to an output destination corresponding to the subject's identification information. An information processing method in which processing is performed by a computer. (Appendix 2) The biosensor includes a plurality of types of biological elements that have different reactivity depending on the possibility of disease. 1. The information processing method described in Appendix 1. (Appendix 3) The biosensor has reactivity corresponding to a plurality of types of diseases, Accept the selection of the disease type to be judged, Determine the possibility of a disease related to the selected disease type 1. An information processing method according to claim 1 or 2. (Appendix 4) Accept the selection of the disease type to be judged, The acquired sensor data is input into a learning model corresponding to a selected disease type from among a plurality of learning models that use sensor data for a sample collected from a subject to determine the possibility of a specific disease in the subject, and the possibility of the disease related to the selected disease type is determined. 10. An information processing method according to any one of claims 1 to 3. (Appendix 5) acquiring subject information including attributes or health status of the subject; The acquired subject information and sensor data are input into a learning model that uses subject information related to the subject and sensor data for a sample to determine the possibility of a disease in the subject, and the possibility of a disease is determined. 10. An information processing method according to any one of claims 1 to 4. (Appendix 6) acquiring time-series sensor data for samples collected from the subject on multiple collection dates; The acquired time-series sensor data is input into a learning model that uses time-series sensor data for a sample collected from a subject to determine the possibility of future disease in the subject, and the possibility of future disease is determined. 6. An information processing method according to any one of claims 1 to 5. (Appendix 7) obtaining a physician's diagnosis of a disease in the subject; The learning model is updated based on the sensor data for the sample collected from the subject and the acquired diagnosis result. 10. An information processing method according to any one of claims 4 to 6. (Appendix 8) The result of the determination of the possibility of the disease in the subject, the sensor data for the sample collected from the subject, and the reference sensor data according to the possibility of the disease are output in association with each other. 8. An information processing method according to any one of claims 1 to 7. (Appendix 9) acquiring image information representative of the response of the biosensor to the sample; The result of the determination of the possibility of the disease in the subject is output in association with the acquired image information. 10. An information processing method according to any one of claims 1 to 8. (Appendix 10) Further, a result of the determination of the possibility of a disease determined by a determination method different from the method of determining the possibility of a disease using the biosensor is obtained, A final determination result is obtained based on the determination result of the possibility of disease using the biosensor and the determination result of the possibility of disease determined by the different determination method. 10. An information processing method according to any one of claims 1 to 9. (Appendix 11) the biosensor comprises cells having olfactory receptors; The different determination methods determine the possibility of disease using information obtained by at least one of an image inspection device, a physical sensor, a chemical sensor, a biosensor having a biological element other than the cells, a genetic test, a chromosome test, a cell surface marker test, and a biomarker test. 11. An information processing method according to any one of claims 1 to 10. (Appendix 12) Display information showing the identification information of the subject, the type of disease to be determined, and the result of the determination of the possibility of the disease in the subject is output. 12. An information processing method according to any one of claims 1 to 11. (Appendix 13) Output display information showing the probability of disease determined by each determination method 13. An information processing method according to any one of Supplementary Note 10 to Supplementary Note 12.

[0160] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. The sequences shown in each embodiment are not limited, and the order of each process may be changed within a range consistent with the present invention, and multiple processes may be executed in parallel. The entity that performs each process is not limited, and the process of each device may be executed by another device within a range consistent with the present invention.

[0161] The matters described in each embodiment can be combined with each other. In addition, the independent claims and dependent claims described in the claims can be combined with each other in any combination, regardless of the reference format. Furthermore, although the claims use a format in which a claim references two or more other claims (multiple claim format), this is not limited to this format. A multiple claim (multi-multi claim) that references at least one other multiple claim may also be used. [Explanation of symbols]

[0162] 100 Judgment System 1 Judgment device 11 Control section 12 Storage section 13 Communications Department 1A Recording Media 1P Program 121 Detection DB 122 Learning Model 2. Terminal Device 21 Control Unit 22 Memory section 23 Communications Department 24 Display section 25 Control section 2A Recording Media 2P Program 3. Detection equipment 4. Olfactory sensor 42 cells

Claims

1. acquiring sensor data from a biosensor for a sample collected from a subject; storing the acquired sensor data and the target's identification information in association with each other; determining a likelihood of disease in the subject based on the sensor data; The determined possibility of the disease is output to an output destination corresponding to the subject's identification information. An information processing method in which processing is performed by a computer.

2. The biosensor includes a plurality of types of biological elements that have different reactivity depending on the possibility of disease. The information processing method according to claim 1 .

3. The biosensor has reactivity corresponding to a plurality of types of diseases, Accept the selection of the disease type to be judged, Determine the possibility of a disease related to the selected disease type 3. The information processing method according to claim 1.

4. Accept the selection of the disease type to be judged, The acquired sensor data is input into a learning model corresponding to a selected disease type from among a plurality of learning models that use sensor data for a sample collected from a subject to determine the possibility of a specific disease in the subject, and the possibility of the disease related to the selected disease type is determined.

3. The information processing method according to claim 1.

5. acquiring subject information including attributes or health status of the subject; The acquired subject information and sensor data are input into a learning model that uses subject information related to the subject and sensor data for a sample to determine the possibility of a disease in the subject, and the possibility of a disease is determined.

3. The information processing method according to claim 1.

6. acquiring time-series sensor data for samples collected from the subject on multiple collection dates; The acquired time-series sensor data is input into a learning model that uses time-series sensor data for a sample collected from a subject to determine the possibility of future disease in the subject, and the possibility of future disease is determined.

3. The information processing method according to claim 1.

7. obtaining a physician's diagnosis of a disease in the subject; The learning model is updated based on the sensor data for the sample collected from the subject and the acquired diagnosis result. The information processing method according to claim 4.

8. The result of the determination of the possibility of the disease in the subject, the sensor data for the sample collected from the subject, and the reference sensor data according to the possibility of the disease are output in association with each other.

3. The information processing method according to claim 1.

9. acquiring image information representative of the response of the biosensor to the sample; The result of the determination of the possibility of the disease in the subject is output in association with the acquired image information.

3. The information processing method according to claim 1.

10. Further, a result of the determination of the possibility of a disease determined by a determination method different from the method of determining the possibility of a disease using the biosensor is obtained, A final determination result is obtained based on the determination result of the possibility of disease using the biosensor and the determination result of the possibility of disease determined by the different determination method.

3. The information processing method according to claim 1.

11. the biosensor comprises cells having olfactory receptors; The different determination methods determine the possibility of disease using information obtained by at least one of an image inspection device, a physical sensor, a chemical sensor, a biosensor having a biological element other than the cells, a genetic test, a chromosome test, a cell surface marker test, and a biomarker test.

3. The information processing method according to claim 1.

12. Display information showing the identification information of the subject, the type of disease to be determined, and the result of the determination of the possibility of the disease in the subject is output.

3. The information processing method according to claim 1.

13. Output display information showing the probability of disease determined by each determination method The information processing method according to claim 10.

14. acquiring sensor data from a biosensor for a sample collected from a subject; storing the acquired sensor data and the target's identification information in association with each other; determining a likelihood of disease in the subject based on the sensor data; The determined possibility of the disease is output to an output destination corresponding to the subject's identification information. Equipped with a control unit that executes processing Information processing device.

15. acquiring sensor data from a biosensor for a sample collected from a subject; storing the acquired sensor data and the target's identification information in association with each other; determining a likelihood of disease in the subject based on the sensor data; The determined possibility of the disease is output to an output destination corresponding to the subject's identification information. A computer program that causes a computer to perform a process.

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