Information processing system, information processing method, and article

Abstract

The information processing system of the embodiment (1, 2) includes an acquisition unit (101, 301) that acquires an image capturing the color of a color reagent in a color reaction using the subject's stool, and a determination unit (103, 303) that determines intestinal environment information regarding the state of the intestinal environment of the subject based on the captured image.

Description

[Technical field]

[0001] The present invention relates to an information processing system, an information processing method, and an article. [Background technology]

[0002] One method for analyzing feces is to use a color reagent (dye) that shows a color reaction when mixed with the sample. For example, various components contained in the feces can be analyzed by visually comparing the color of the reaction solution after the color reaction with the color legend or by measuring the spectral absorbance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2020 / 097043 Summary of the Invention [Problem to be solved by the invention]

[0004] However, while the more color reagents used, the more versatile the analysis becomes, the more time and effort is required to visually observe and measure color changes.

[0005] An object of the present invention is to provide an information processing system, an information processing method, and an article that can easily perform fecal analysis using a color reaction. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objective, the present invention provides an information processing system comprising an acquisition unit that acquires an image capturing the color of a color reagent in a color reaction using a subject's stool, and a determination unit that determines intestinal environment information regarding the state of the subject's intestinal environment based on the captured image.

[0007] The present invention also provides an information processing method that includes acquiring an image capturing the color of a color reagent in a color reaction using the subject's stool, and determining intestinal environment information regarding the state of the subject's intestinal environment based on the captured image.

[0008] The present invention also provides an information processing system comprising a learning unit that performs machine learning for a plurality of subjects providing training data using features of the subjects and intestinal environment information relating to the state of the intestinal environment of the subjects. Effect of the Invention

[0009] According to the present invention, it is possible to provide an information processing system, an information processing method, and an article that can easily perform fecal analysis using a color reaction. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of an information processing system. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Diagram 3] FIG. 3 is a flowchart showing a processing procedure of the information processing system. [Figure 4] FIG. 4 is a diagram for explaining the process of the calculation unit. [Diagram 5] FIG. 5 is a diagram for explaining a trained model of the information processing system. [Figure 6] FIG. 6 is a diagram illustrating an example of a configuration of an information processing system. [Figure 7] FIG. 7 is a diagram illustrating an example of a configuration of an information processing system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an information processing system, an information processing method, and an article according to an embodiment will be described with reference to the drawings. Note that the following embodiments are not limited to the following description. Also, each embodiment can be combined with other embodiments or conventional techniques as long as there is no contradiction in the processing content.

[0012] In the following embodiments, the term "subject" refers to a person who is the subject of feces (biological sample) analysis using a color reaction, typically a patient, but may also be a healthy person who is the subject of analysis for the purpose of understanding health conditions. The subject may also be an infant under one year of age, from whom it is more difficult to obtain intestinal environment information compared to adults and from whom more careful health management is required.

[0013] Furthermore, the subject of analysis is not necessarily limited to humans, and may be, for example, animals such as pets and livestock. When the subject of analysis includes animals other than humans, it will be referred to as a "subject" or a "specimen."

[0014] The term "user" refers to a person who uses the information processing system according to the embodiment, and is typically a medical professional, but does not necessarily have to be a medical professional. For example, the user may be the subject himself / herself, or a close relative, friend, or acquaintance of the subject.

[0015] (Embodiment) The configuration of an information processing system 1 according to the present invention will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration of the information processing system 1. As shown in Fig. 1, the information processing system 1 according to the embodiment includes an information processing device 10 and an image capturing device 20. The information processing device 10 and the image capturing device 20 are connected to each other by any communication means. As the communication means, for example, any network such as a LAN (Local Area Network) or a WAN (Wide Area Network) can be applied.

[0016] The information processing device 10 is a server device that provides a service of analyzing feces using a color reaction. For example, the information processing device 10 includes an acquisition unit 101, a calculation unit 102, a determination unit 103, an evaluation unit 104, and an output control unit 105. The acquisition unit 101, the calculation unit 102, the determination unit 103, the evaluation unit 104, and the output control unit 105 will be described later. In addition, the "color reaction" in this application means a reaction accompanied by a phenomenon of color development or discoloration. In addition, the phenomenon of color development or discoloration is a phenomenon that can be detected as a difference in the wavelength of light, and means a phenomenon in which the value of image data (for example, at least one color score in the RGB color space, Lab color space, CMYK color space, HSV color space, and Gray color space) obtained when a sample after a color reaction is photographed using a photographing camera (for example, SONY's Cyber-shot (DSC-WX500, WW220188)) changes.

[0017] The image capturing device 20 is a device that captures an optical image (captured image) that is visible using visible light. For example, the image capturing device 20 corresponds to a smartphone equipped with a camera module capable of capturing optical images, or a camera capable of capturing optical images. For example, the image capturing device 20 captures an image of a color sensor array on which a color reaction with a biological sample of a subject has been performed, and transmits the captured image to the information processing device 10.

[0018] A color sensor array is a multi-well plate (e.g., a 96-well plate) in which multiple color reagents are arranged in individual wells (holes). The bottom of each well of the multi-well plate is transparent, and the color change of the color reagent in each well can be visually observed from the bottom side.

[0019] Here, one or more known color reagents can be appropriately selected as the color reagent for stool. For example, the color reagent may be picric acid, methyl violet, malachite green, o-cresol red, thymol blue, 2,4-dinitrophenol, Congo red, methyl orange, methyl yellow, bromophenol blue, bromocresol green, bromophenol red, methyl red, litmus (blue), litmus (red), methyl purple, bromocresol purple, chlorophenol red, bromothymol blue, p-nitrophenol, neutral red, phenol red, or p-naphtholphthalein. , cresol red, phenolphthalein, thymolphthalein, alizarin yellow, 1,3,5-trinitrobenzene, nitrazine yellow, rosolic acid, bromopyrogallol red, pyrocatechol violet, 4-(4-diethylaminostyryl)-1-methylpyridinium iodide, Brooker merocyanine dye, Nile red, 2-[2-cyano-4-[(N-succinimidyloxy)carbonyl]phenyl]-1,3a,6a-triazapentalene, Reichardt's reagent, 4-dimethylamino-4-nitrostilbene , phthalocyanine, 15-crown 4 [4-(2,4-dinitrophenylazo)phenol], 18-crown 5 [4-(2,4-dinitrophenylazo)phenol], Disperse Orange 25, flavone, 3-hydroxyflavone, chalcone, cyanidin chloride, cyanine, pelargonidin chloride, peonidin, delphinidin, petunidin, malvidin, α-carotene, β-carotene, γ-carotene, δ-carotene, ε-carotene, lycopene, 9-anthryldiazomethane, DMEQ-hydrazide, rhodamine B, alcia Blue, Fluorescein, Acridine Orange Base, Pigment Blue 15, Iron(II) Phthalocyanine, Magnesium(II) Phthalocyanine, Zinc Phthalocyanine, Cobalt(II) Phthalocyanine, Dilithium Phthalocyanine, Tin(II) Phthalocyanine, Sodium Phthalocyanine, Aluminum Chloride Phthalocyanine, Lead(II) Phthalocyanine, Titanyl Phthalocyanine, Manganese(III) Tetraphenylporphyrin Chloride, 5,10,15,20-Tetraphenylporphyrinatozinc, 5,10,15,20-Tetraphenylporphyrinatozinc, 5,10,15,Examples of the color reagent include one or more of 20-tetrakis(2,4,6-trimethylphenyl)porphyrinatozinc, 5,10,15,20-tetrakis(pentafluorophenyl)porphyrinatozinc, and 5,10,15,20-tetrakis(4-methoxyphenyl)porphyrinato]cobalt, and among these, it is preferable to select 3 to 5 color reagents. Among the above color reagents, more preferable examples include one or more of thymol blue, bromocresol green, methyl red, bromocresol purple, chlorophenol red, bromothymol blue, phenol red, bromopyrogallol red, Nile red, Reichardt's reagent, rhodamine B, Alcian blue, magnesium(II) phthalocyanine, zinc phthalocyanine, sodium phthalocyanine, and phthalocyanine chloroaluminum, and among these, it is preferable to select 3 to 5 color reagents. Among the above color reagents, the most preferred ones include one or more of bromocresol green, methyl red, Reichardt's reagent, bromocresol purple, bromopyrogallol red, bromothymol blue, phenol red, and chlorophenol red, and it is preferable to select 3 to 5 color reagents among these.

[0020] The color reagent may be one that changes color depending on the pH or polarity of the fecal biological sample, and more preferably one that changes color depending on both pH and polarity. By using such a color reagent, the concentration or abundance of intestinal metabolites, and the abundance ratio, abundance, or Shannon index of intestinal bacteria can be determined with high accuracy. Examples of color reagents that change color depending on the pH of the fecal biological sample include bromocresol green, methyl red, bromocresol purple, bromopyrogallol red, bromothymol blue, phenol red, and chlorophenol red, and examples of color reagents that change color depending on the polarity of the fecal biological sample include Reichardt's reagent.

[0021] 1 is merely an example, and the present invention is not limited thereto. For example, the photographing device 20 does not necessarily have to be connected to the information processing device 10. Images captured by the photographing device 20 may be stored in the information processing device 10 via any portable recording medium such as a DVD (Digital Versatile Disc).

[0022] Furthermore, the color sensor array is not limited to a multi-hole plate, but may be a test piece (such as a paper device) or other article in which a plurality of color reagents are arranged at individual positions. The test piece can be made of any material, such as paper, plastic (resin), glass, fiber, ceramic, or metal. Furthermore, examples of other articles are not particularly limited as long as they can come into contact with feces, and include microfluidics, detection kits, diapers, toilet bowls, toilet paper, stool collection kits, enemas, pet litter, and pet toilet sheets, and preferably include microfluidics, diapers, toilet bowls, and pet toilet sheets.

[0023] Next, a hardware configuration of the information processing device 10 included in the information processing system 1 will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the hardware configuration of the information processing device 10.

[0024] As shown in FIG. 2, the information processing device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, an auxiliary storage device 14, an input device 15, a display device 16, and an external I / F (Interface) 17.

[0025] The CPU 11 is a processor (processing circuit) that executes a program to comprehensively control the operation of the information processing device 10 and realize various functions of the information processing device 10. For example, the functions of an acquisition unit 101, a calculation unit 102, a determination unit 103, an evaluation unit 104, and an output control unit 105 of the information processing device 10 are realized by the CPU 11.

[0026] The ROM 12 is a non-volatile memory and stores various data (information written during the manufacturing stage of the information processing device 10) including a program for starting up the information processing device 10. The RAM 13 is a volatile memory having a working area for the CPU 11. The auxiliary storage device 14 stores various data such as the program executed by the CPU 11. The auxiliary storage device 14 is configured, for example, by a hard disc drive (HDD), a solid state drive (SSD), etc.

[0027] The input device 15 is a device for performing various operations by the person operating the information processing device 10. The input device 15 is configured with, for example, a mouse, a keyboard, a touch panel, or hardware keys.

[0028] The display device 16 displays various information. For example, the display device 16 displays image data, model data, a GUI (Graphical User Interface) for receiving various operations from an operator, medical images, etc. The display device 16 is configured, for example, as a liquid crystal display, an organic EL (Electro Luminescence) display, or a cathode ray tube display. Note that the input device 15 and the display device 16 may be configured integrally, for example, in the form of a touch panel.

[0029] The external I / F 17 is an interface for connecting (communicating) with an arbitrary external device such as a server device 20.

[0030] 2 is merely an example, and the present invention is not limited thereto. For example, the hardware configuration of the information processing device 10 may be any configuration of a known computer or workstation.

[0031] A processing procedure of the information processing device 10 included in the information processing system 1 will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the processing procedure of the information processing system 1. In the description of Fig. 3, Fig. 4 and Fig. 5 will be appropriately referred to.

[0032] 3, the user performs a color reaction using feces of a subject (step S101). For example, the user brings the feces obtained from the subject into contact with a color sensor array to perform a color reaction.

[0033] Next, the user photographs the result of the color reaction of the subject (step S102). For example, the user uses the photographing device 20 to photograph the color sensor array 22 after the color reaction.

[0034] Then, the photographing device 20 transmits the photographed image to the information processing device 10 (step S103). As a result, the acquiring unit 101 of the information processing device 10 acquires the photographed image of the color sensor array 22 after the color reaction. This process may be performed by any communication means or via any portable recording medium.

[0035] The calculation unit 102 of the information processing device 10 then calculates the color score of the subject based on the captured image (step S104). The color score is an example of a "feature value" that quantifies the color of the coloring reagent. The color of the coloring reagent is represented by pixel values ​​in the RGB color space, Lab color space, CMYK color space, HSV color space, and Gray color space.

[0036] Here, the process of the calculation unit 102 of the information processing device 10 will be described with reference to Fig. 4. Fig. 4 is a diagram for explaining the process of the calculation unit 102.

[0037] The calculation unit 102 generates five captured images represented in the RGB color space, the Lab color space, the CMYK color space, the HSV color space, and the Gray color space from the captured image acquired by the acquisition unit 101. For example, if the captured image acquired by the acquisition unit 101 is an image represented in the RGB color space, the calculation unit 102 converts this image into each of the remaining four color spaces. As this conversion process, a known image conversion technique can be appropriately applied.

[0038] Then, the calculation unit 102 specifies a region R1 in which the colors of the color reagents are depicted in each of the images in each color space. Specifically, the calculation unit 102 specifies the region of each well 22a of the color sensor array 22 from the captured image in which the color sensor array 22 is depicted by pattern matching processing or the like. Then, the calculation unit 102 specifies a region R1 including a predetermined number of pixels (e.g., 50 pixels x 50 pixels) including the center of each well 22a.

[0039] Then, the calculation unit 102 calculates a statistical value of pixel values ​​of a predetermined number of pixels included in each of the identified regions R1 as a color score. For example, the calculation unit 102 calculates a median value of pixel values ​​of a predetermined number of pixels included in the region R1 as a color score.

[0040] Here, the color score is numerical data in which the values ​​of the number of dimensions calculated from five images expressed in five color spaces are arranged. For example, an image expressed in the RGB color space has three-dimensional pixel values ​​of R, G, and B, so the median (statistical value) calculated from the image expressed in the RGB color space is three-dimensional, for example (x1, x2, x3). Similarly, the median calculated from the image expressed in the Lab color space is three-dimensional, so it is for example (x4, x5, x6). The median calculated from the image expressed in the CMYK color space is four-dimensional, so it is for example (x7, x8, x9, x10). The median calculated from the image expressed in the HSV color space is three-dimensional, so it is for example (x11, x12, x13). The median calculated from the image expressed in the Gray color space is one-dimensional, so it is for example (x14). In other words, the color score is 14-dimensional numerical data (x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14).

[0041] In this manner, the calculation unit 102 calculates a color score, which is 14-dimensional numerical data, for each well (color reagent).

[0042] Note that the above-mentioned processing contents of the calculation unit 102 are merely an example, and the present invention is not limited thereto. For example, in the above example, the processing of calculating the color score using all five images represented in five color spaces has been described, but the present invention is not limited thereto. For example, the calculation unit 102 can calculate the color score using at least one image of the five images represented in the five color spaces, and can also calculate the color score using images represented in a plurality of color spaces, such as two or more, three or more, or four or more.

[0043] In the above example, the process of calculating the median as the color score has been described, but the present invention is not limited to this. For example, the calculation unit 102 can calculate any statistical value such as the average value or the mode value as the color score in addition to the median value. The calculation unit 102 may also calculate the color score by combining any statistical value such as the median value, the average value, or the mode value. In this case, since 14-dimensional numerical data is obtained even when the average value is used, the color score becomes 28-dimensional numerical data when the median value and the average value are combined. That is, the calculation unit 102 may calculate at least one of the median value, the average value, and the mode value as the color score.

[0044] Also, for example, it is possible to appropriately correct the color score using data obtained by photographing the color sensor array before the color reaction is carried out. As a specific example, the correction can be performed by subtracting the pixel values ​​of the photographed image of the color sensor array 22 before the color reaction from the pixel values ​​of the photographed image of the color sensor array 22 after the color reaction, and the above-mentioned color score can be calculated using the corrected pixel values. By correcting the color score in this way, it may be possible to suppress deviations in the color score that may be affected by the photographing environment.

[0045] Returning to the explanation of FIG. 3, the judgment unit 103 of the information processing device 10 generates a judgment result by inputting the color score of the subject into the trained model (step S105). This trained model is trained using the color scores of the providers and the intestinal environment information of the providers for multiple providers who provide training data. In other words, the judgment result is the intestinal environment information of the subject. The intestinal environment information is information regarding the state of the intestinal environment of the subject (provider).

[0046] Here, the trained model of the information processing system 1 will be described with reference to FIG. 5. FIG. 5 is a diagram for explaining the trained model of the information processing system 1. The upper part of FIG. 5 shows the processing during training of the trained model, and the lower part of FIG. 5 shows the processing during operation of the trained model. Here, the trained model is constructed in advance and stored in a predetermined storage area (e.g., ROM 12) that is available to the determination unit 103. The processing of the "learning unit" for constructing the trained model will be described later.

[0047] As shown in the upper part of FIG. 5, during learning, machine learning is performed using, for example, color scores based on the feces of multiple donors 1 to N and intestinal environment information. Here, the color score of the donor is calculated based on a color reaction using the donor's biological sample. The calculation method of the donor's color score is the same as the calculation method of the subject's color score by the calculation unit 102, so the explanation is omitted. In addition, the donor's intestinal environment information is a measurement value of a component to be analyzed in the biological sample, for example, the concentration or abundance of short-chain fatty acids in the feces, the abundance ratio, abundance or Shannon index of intestinal bacteria in the feces. Among these intestinal environment information, the present invention can obtain useful results when the concentration or abundance of short-chain fatty acids in the feces is used. Examples of short-chain fatty acids in the feces include at least one selected from acetic acid, succinic acid, lactic acid, propionic acid, formic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid, and examples of intestinal bacteria in the feces include the genus Bifidobacterium. Among these measured values, particularly useful results can be obtained when at least one selected from lactic acid, acetic acid, butyric acid, and propionic acid is used. It is known that short-chain fatty acids in feces are an energy source for the large intestine and an indicator of the health of the intestine. It is believed that measuring lactic acid and the like as a management indicator can lead to anti-inflammatory effects, promotion of immune cell function, and reduction in the risk of metabolic diseases (such as improvement of insulin sensitivity). The intestinal environment information can be measured by a known measurement technique. In addition, a known classification model or a known regression model can be appropriately applied as a machine learning model.

[0048] Then, as shown in the lower part of Fig. 5, during operation, the determination unit 103 inputs the subject's color score to a trained model constructed by machine learning, and causes the trained model to output a determination result indicating the subject's intestinal environment information. For example, a machine learning model trained using the concentration of acetic acid in the donor's feces outputs an estimate of the concentration of acetic acid in the subject's feces as a determination result. In this way, the determination unit 103 determines the subject's intestinal environment information based on the color score.

[0049] The above-mentioned processing contents of the determination unit 103 are merely an example, and the present invention is not limited thereto. For example, in the above description, the concentration of acetic acid in feces is estimated as intestinal environment information, but the present invention is not limited thereto. Any index can be applied as long as it is short-chain fatty acids or bacterial amount in feces that can be measured by a known measurement technique as teacher data for machine learning, or the Shannon index, which is one of the diversity indexes of intestinal flora. The Shannon index is a value that measures the diversity and balance of species in a community.

[0050] In addition, the learning process described in Fig. 6 can be executed at any timing before the operation process is executed. In addition, the learning process can also be executed to update (additionally learn) an already generated trained model.

[0051] Returning to the description of Fig. 4, the evaluation unit 104 evaluates the health index of the subject based on the intestinal environment information (step S106). Here, the health index is an index (score) indicating the health condition of the subject, and is calculated based on the intestinal environment information.

[0052] For example, it is believed that the higher the measured (estimated) value of the abundance ratio (or abundance) of short-chain fatty acids or Bifidobacterium bacteria, the healthier the person is. For this reason, the evaluation unit 104 classifies the estimated values ​​of the abundance ratio of short-chain fatty acids or Bifidobacterium bacteria into an arbitrary number of ranks such as "A," "B," "C," etc., in descending order, and outputs the classified rank as a health index.

[0053] For example, the Shannon index of intestinal bacteria is considered to have a suitable range for the measured value (estimated value). Therefore, the evaluation unit 104 classifies the Shannon index of intestinal bacteria into an arbitrary number of ranks such as "A" when it is within the suitable range, and "B", "C", etc. according to the magnitude of the value outside the range, and outputs the classified rank as a health index.

[0054] In this manner, the evaluation unit 104 evaluates the health index relating to the health of the subject based on the intestinal environment information (step S106).

[0055] Then, output control unit 105 outputs the health index (step S107). For example, output control unit 105 causes the health index to be displayed on display device 16, or stores it in ROM 12. Output control unit 105 may also transmit the health index to an information processing terminal that can be viewed by the user. The information processing terminal causes the health index transmitted from output control unit 105 to be displayed on a specified display device, or stores it in a specified storage device.

[0056] Note that the output target of the output control unit 105 is not limited to the health index, and may be, for example, the judgment result of the intestinal environment information. If the health index is not output, the configuration and processing of the evaluation unit 104 are not necessary.

[0057] As described above, in the information processing system 1 according to the present invention, the acquisition unit 101 of the information processing device 10 acquires a captured image in which the color of a color reagent in a color reaction using the subject's stool is captured. The determination unit 103 determines intestinal environment information related to the state of the intestinal environment of the subject based on the captured image. In this way, the information processing system 1 according to the present invention can easily perform fecal analysis using a color reaction. For example, the information processing system 1 can easily perform multifaceted fecal analysis using a color reagent.

[0058] Furthermore, in the information processing system 1, the calculation unit 102 calculates a feature amount that digitizes the color based on the captured image. This allows the information processing system 1 to easily digitize the color change of the color developing reagent.

[0059] Furthermore, the information processing system 1 can easily perform multifaceted analyses not only on human biological samples but also on biological samples obtained from animals such as pets and livestock, using color reagents suitable for detecting components to be analyzed in the biological samples. This is considered to be particularly useful in understanding the health conditions of animals that cannot be interviewed.

[0060] Note that the contents described in the above embodiment are merely examples, and the present invention is not limited thereto. For example, in the above embodiment, the process of calculating the color score using the captured image of the color sensor array 22 after the color reaction has been described, but the captured image of the color sensor array 22 before the color reaction can also be used. For example, it is also possible to generate a difference image between the captured images before and after the color reaction, and calculate the color score from this difference image.

[0061] In the above embodiment, the function of the information processing device 10 is provided as a server device, but the present invention is not limited to this. For example, the function of the information processing device 10 may be provided in an information processing terminal such as a personal computer, tablet, or smartphone owned by an individual user.

[0062] (Modification) In the above embodiment, the information processing device 10 and the photographing device 20 are provided as separate devices in the information processing system 1, but the present invention is not limited to this. For example, the information processing device 10 and the photographing device 20 may be provided as an integrated device.

[0063] The configuration of an information processing system 2 according to a modified example will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the configuration of the information processing system 2. As shown in Fig. 6, the information processing system 2 includes an information processing device 30 that is compatible with a personal computer, tablet, smartphone, or the like having a photographing function. The hardware configuration of the information processing device 30 is basically the same as the hardware configuration of the information processing device 10 shown in Fig. 2, and therefore description thereof will be omitted.

[0064] The information processing device 30 includes an acquisition unit 301, a calculation unit 302, a determination unit 303, an evaluation unit 304, and an output control unit 305. The configurations of the calculation unit 302, the determination unit 303, the evaluation unit 304, and the output control unit 305 shown in Fig. 6 are basically similar to the configurations of the calculation unit 102, the determination unit 103, the evaluation unit 104, and the output control unit 105 shown in Fig. 1, and therefore description thereof will be omitted.

[0065] The acquiring unit 301 has a configuration similar to that of the acquiring unit 101, and also has an imaging unit 301A that handles the imaging function of the information processing device 30. The imaging unit 301A is a camera module (optical sensor) capable of capturing optical images, and has functions similar to those of the imaging device 20.

[0066] That is, the acquiring unit 301 acquires a captured image of the color sensor array 22 after the color reaction by controlling the function of the photographing unit 301A. Specifically, the photographing unit 301A photographs the color sensor array on which the color reaction with the subject's biological sample has been carried out under the control of the acquiring unit 301. The acquiring unit 301 generates a captured image based on the information photographed by the photographing unit 301A. The generated captured image is sent to the calculating unit 302 and used for subsequent processing. The function of generating the captured image may be provided in the photographing unit 301A.

[0067] (Learning Department) In addition, the information processing system according to the present invention may be equipped with a learning function for constructing the trained model described in the above embodiment.

[0068] The configuration of an information processing system 3 according to the present invention will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the configuration of the information processing system 3. As shown in Fig. 7, the information processing system 3 includes an information processing device 40 and an imaging unit 50. The hardware configuration of the information processing device 40 is basically the same as the hardware configuration of the information processing device 10 shown in Fig. 2, and therefore a description thereof will be omitted. In addition, the configuration of the imaging unit 50 is basically the same as the configuration of the imaging device 20 shown in Fig. 2, and therefore a description thereof will be omitted.

[0069] The information processing device 40 is a server device that constructs a trained model for determining the health index of a subject. For example, the information processing device 40 includes an acquisition unit 401, a calculation unit 402, a learning unit 403, and an output control unit 404. The configurations of the acquisition unit 401, the calculation unit 402, and the output control unit 404 shown in Fig. 7 are basically the same as the configurations of the acquisition unit 101, the calculation unit 102, and the output control unit 105 shown in Fig. 1, and therefore description thereof will be omitted.

[0070] Here, the learning unit 403 performs machine learning using the color scores based on the feces of multiple providers 1 to N and the intestinal environment information to construct a trained model for determining the intestinal environment information of the subject. For example, the learning unit 403 performs the machine learning described in the upper part of FIG. 5 to generate a trained model that outputs a determination result indicating the intestinal environment information of the subject by inputting the color score of the subject. The learning unit 403 stores the generated trained model in any storage area available to the determination unit 103.

[0071] That is, the learning method according to the present invention performs machine learning for a plurality of providers who provide training data, using the provider's feature amounts and the provider's intestinal environment information.

[0072] Although the case has been described here in which the information processing device 40 includes the learning unit 403, the present invention is not limited to this. For example, the learning unit 403 may be included in the information processing device 10.

[0073] In addition, when generating a trained model that outputs a judgment result indicating a health index of animals such as pets and livestock, it is preferable that the living organisms providing the biological samples for learning are animals, not humans. When the living organisms providing the biological samples for learning include animals other than humans, they are referred to as "subjects (experimental subjects)."

[0074] According to the embodiment and modified examples described above, feces analysis using a color reaction can be easily carried out.

[0075] The programs executed by the information processing systems 1, 2, and 3 according to the above-described embodiments and modifications may be provided by recording them in an installable or executable format on a computer-readable recording medium such as a CD-ROM, a flexible disk (FD), a CD-R, a DVD, or a USB (Universal Serial Bus), or may be provided or distributed via a network such as the Internet. Moreover, the various programs may be provided by being pre-installed in a non-volatile storage medium such as a ROM.

[0076] The above-described embodiment can be arbitrarily combined with the above-described modified examples, and the above-described modified examples may be arbitrarily combined with each other.

[0077] [Example] The present invention will be described in more detail below based on examples, but the present invention is not limited thereto. In this example, a machine learning model corresponding to the reagent conditions, change factors, and target variables corresponding to Examples 1 to 16 shown in Tables 1, 2, and 3 was constructed, and the coefficient of determination was evaluated as an index of the estimation accuracy. Tables 1, 2, and 3 show the results of the reagent conditions, change factors, color space, target variables, and coefficient of determination (R2 value) for each example.

[0078] In this example, 100 human feces specimens (biological samples) collected from infants within one year of age were used.

[0079] The color reagents used in this example are as follows: CR: Chlorophenol red (Tokyo Chemical Industry (TCI)) dissolved in ethanol at a concentration of 1 mM. BB: Bromothymol blue (Fujifilm Wako) dissolved in ethanol at a concentration of 1 mM. BG: Bromocresol Green (Fujifilm Wako) dissolved in ethanol at a concentration of 1 mM. BP: Bromocresol Purple (Fujifilm Wako) dissolved in ethanol at a concentration of 1 mM. MR: Methyl red (Fujifilm Wako) dissolved in ethanol at a concentration of 1 mM. PR: Phenol red (Fujifilm Wako) dissolved in ethanol at a concentration of 0.8 mM. BR: Bromopyrogallol Red (Tokyo Chemical Industry (TCI)) dissolved in ethanol at a concentration of 0.6 mM. RDE: Reichardt's reagent (Sigma) dissolved in ethanol at a concentration of 2 mM. RDM: Reichardt's reagent (Sigma) dissolved in methanol (Fujifilm Wako) at a concentration of 2 mM. RDI: Reichardt's reagent (Sigma) dissolved in isopropyl alcohol (Fujifilm Wako) at a concentration of 2 mM.

[0080] The procedure for the color reaction using feces is described below. After thawing the samples frozen at -80°C in a water bath at 37°C, 20-25 mg was weighed out into a 2 mL Eppendorf tube, 24 μL of PBS was added per 1 mg of the weighed weight, and the samples were suspended by vortexing for 30 seconds. Then, the samples were centrifuged at 9,000 G for 10 minutes, and only the supernatant was collected. On a 96-well plate, except for RDE, RDM, and RDI, 30 uL of the supernatant was mixed with 30 uL of the color reagent, and the samples were stirred on a shaker for 1 minute, and then photographed with a camera to obtain images. For RDE, RDM, and RDI, 54 uL of the supernatant was mixed with 6 uL of the color reagent, and the samples were stirred on a shaker for 1 minute, and then photographed with a camera to obtain images.

[0081] To obtain color scores, the well portion of the 96-well plate in the image obtained above was cropped to 50 pixels x 50 pixels, and the obtained image was used with Pillow (ver. 9.4.0) in Python (ver. 3.9.18, python.org.) to obtain the mean, median, and mode of 2,500 pixels for the RGB, HSV, Lab, CMYK, and gray color spaces.

[0082] The measurement of metabolites (short-chain fatty acids) derived from intestinal bacteria in feces was carried out by the following method. That is, freeze-dried fecal samples were disrupted by vigorous vibration (1,500G, 10min) with 3.0mm zirconia beads using a bead crusher (Shake Master, Biomedical Science). 10mg of the disrupted fecal sample was added and suspended in 1,000μL of crotonic acid, an internal standard sample, and 500μL of hydrochloric acid and 2,000μL of ether were added. Next, the tube was vigorous vibration (1,500G, 10min) using a bead crusher, and then centrifuged at 10,000G for 10min. After centrifugation, 80μL of the upper ether layer was collected and added to an Agilent crimp-cap vial containing 16μL of N-tert-butyldimethylsilyl N-methyltrifluoroacetamide. The vial was sealed and heated in a water bath at 80℃ for 20min. After heating, the mixture was left to stand overnight at room temperature to t-butyldimethylsilylate the hydroxyl groups. Calibration was performed using standard solutions (formic acid, acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, lactic acid, and succinic acid) that were derivatized in the same manner as the test samples. The derivatized samples were analyzed using an Agilent Technologies 7890 Series GC-MS system equipped with a DB-5ms column (0.25 mm × 30 m × 0.25 μm; Agilent Technologies). Measurements were performed using Agilent MassHunter Workstation Data Acquisition software (version 10.0, Agilent Technologies), and the obtained data were analyzed using Agilent MassHunter Quantitative Analysis software (version 10.1, Agilent Technologies).

[0083] A method for analyzing the intestinal flora will be described. First, the DNA (deoxyribonucleic acid) of the intestinal bacteria was extracted from the fecal sample using the 96 MagBead DNA / RNA Kit (Zymo Research). This was amplified by PCR (polymerase chain reaction) using primers (27Fmod: 5'-agrgtttgatymtggctcag-3', 338R: 5'-tgctgcctcccgtaggagt-3') designed to cover the variable region and v1-v2 region of the 16SrRNA gene, and then sequencing was performed with 250 bp paired end using Illumina Miseq (registered trademark). The obtained gene sequence was merged, filtered, trimmed, and clustered along the Qiime2 (ver. 2021.11,) Pipeline to obtain an amplicon sequence variant (ASV). Each ASV was taxonomically classified using the Silva-138-99 database, and bioinformatic bacterial flora analysis was performed.

[0084] The procedure for the color reaction in this example will be described.

[0085] Machine learning evaluation will be explained. All combinations of reagent conditions were evaluated by double cross validation. For double cross validation, the training data and test data of all samples were divided into 9:1, and the training data was further divided into 5 parts, and learning was performed using 4 parts, and testing was performed using the remaining 1 part to calculate the coefficient of determination. This was performed for all 5 combinations while changing the hyperparameters, and the hyperparameters were determined. Using the hyperparameters determined here, training was performed with the training data and testing was performed with the test data. This was performed for all 10 combinations, the coefficient of determination R^2 was calculated, and the median was evaluated.

[0086] Table 1 shows the verification results regarding color reagents and color spaces.

[0087] [Table 1]

[0088] "Reagent conditions" is an item that indicates the type of color reagent used in the color reaction with the feces. Although Table 1 lists some combinations of multiple color reagents, in reality, a machine learning model was constructed using combinations of color change data of all color reagents (3,628,800 combinations) and its accuracy was verified.

[0089] The "change factor" is an item indicating the factor that causes the color reagent used to change color, and corresponds to at least one of "pH" and "polarity." Factors that contribute to pH include short-chain fatty acids, lactic acid, ammonia, hydrogen sulfide, bilirubin metabolites, bile acids, hydrogen gas, and methane gas. Factors that contribute to polarity include short-chain fatty acids, bile acids, lipid metabolites, amino acids, amino acid metabolites, polysaccharides, oligosaccharides, and polyphenols.

[0090] "Color space" is an item related to the number of types of color space used to calculate the color score. "Five types" indicates that the color score is calculated using five types of color space: RGB color space, Lab color space, CMYK color space, HSV color space, and Gray color space. "One type" indicates that the color score is calculated using only the RGB color space.

[0091] The "target variable" is an item indicating the components in the biological sample to be analyzed. Specifically, it corresponds to at least one of the following: metabolic products produced by intestinal bacteria in feces, the Bifidobacterium genus, and the Shannon index. The "metabolites" correspond to at least one of acetic acid, succinic acid, lactic acid, propionic acid, formic acid, butyric acid, isobutyric acid, isovaleric acid, and valeric acid. The "Bifidobacterium genus" corresponds to the abundance ratio or abundance of bacteria of the Bifidobacterium genus. The "Shannon index" corresponds to the Shannon index of intestinal bacteria (a diversity index of intestinal microflora).

[0092] "R2 value in regression" is an item that indicates the evaluation result of the regression model, and corresponds to the value of the coefficient of determination R^2. R2 values ​​of 0.5 or more are indicated as "A", those between 0.3 and less than 0.5 are indicated as "B", and those less than 0.3 are indicated as "C".

[0093] As shown in Table 1, in Examples 1 and 1', a machine learning model was constructed to predict the amount of acetic acid in feces using five types of color space, and the prediction results were evaluated using the coefficient of determination R2 value. Verification was performed for all combinations of color reagents, and particularly good evaluation results were obtained. In Table 1, the reagent conditions for Example 1 were a combination of BG, MR, RDE, and RDI, and the reagent conditions for Example 1' were a combination of PR and RDE. The R2 values ​​for both Examples 1 and 1' were rated "A", but Example 1 had a particularly good evaluation result.

[0094] On the other hand, compared to Example 1, Example 2, which used one type of reagent condition (RDE), Example 3, which used 10 reagent conditions (CR, BB, BG, BP, MR, PR, BR, RDE, RDM, and RDI), Example 4, which combined reagents due to pH alone, Example 5, which combined reagents due to polarity alone, and Example 6, which used only the RGB color space, all had R2 values ​​of "B". As a result of Table 1, it can be said that the most suitable combination of conditions for predicting (estimating) acetic acid in feces by machine learning is the condition of Example 1.

[0095] Tables 2 and 3 verify the optimal combination of color reagents for each of the other analytes. By judging the evaluation results using machine learning, multiple reagent combinations can be selected for the analyte and analyzed. As a result, when measuring multiple analytes, it is possible to measure multiple analytes with the minimum number of reagents by optimizing the reagent combination. In addition, depending on the desired detection accuracy and the performance of the imaging device (for example, selecting from several reagent combinations rated "A"), it is also possible to reduce costs by selecting the number of inexpensive reagents.

[0096] [Table 2]

[0097] [Table 3]

[0098] As shown in Tables 2 and 3, in Examples 7 to 16', the change factors and color space conditions are the same as in Example 1, all combinations of color reagents are verified for other analytes, and combinations of reagent conditions with good R2 values ​​are verified, and main combinations are identified. Examples 7 to 16' shown in Tables 2 and 3 show combinations of color reagents that gave good evaluation results for each analyte (target variable). [Explanation of symbols]

[0099] 1,2,3 Information Processing System 10,30 Information processing device 11 CPU 12 ROM 13 RAM 14 Auxiliary storage 15 Input Devices 16 Display device 17 External I / F 20,50 Imaging device 101,301,401 Acquisition Department 102,302,402 Calculation section 103,303 Judgment section 104,304 Evaluation Department 105,305,404 Output control section 403 Learning Department

Claims

1. An acquisition unit that acquires images capturing the color of the color reagent in a color reaction using the target feces, A determination unit that determines intestinal environment information relating to the state of the intestinal environment of the target based on the captured image. Equipped with, The determination unit determines the concentration or amount of at least one intestinal metabolite from among acetic acid, succinic acid, lactic acid, propionic acid, formic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid as intestinal environment information. Information processing system.

2. The system further includes a calculation unit that calculates a feature quantity that quantifies the color based on the captured image. The information processing system according to claim 1.

3. The determination unit determines the intestinal environment information of a target by inputting the target's features to a trained model that has been trained using the features of the subjects and intestinal environment information relating to the state of the subjects' intestinal environment, for a plurality of subjects that provide training data. The information processing system according to claim 2.

4. The determination unit further determines, as intestinal environment information, at least one of the following: the concentration or amount of intestinal metabolites, the proportion or amount of intestinal bacteria, and the Shannon index. The information processing system according to claim 1.

5. The determination unit determines, as the intestinal bacteria, the relative abundance or amount of bacteria of the genus Bifidobacterium, and at least one of the Shannon index. The information processing system according to claim 4.

6. The subject is an infant under one year of age. The information processing system according to claim 1.

7. The aforementioned color-changing reagent is a reagent whose color changes depending on the pH or polarity of the biological sample based on feces. The information processing system according to claim 1.

8. The aforementioned color is represented by pixel values ​​in at least one of the following color spaces: RGB color space, Lab color space, CMYK color space, HSV color space, and Gray color space. The information processing system according to claim 1.

9. The aforementioned color reagent consists of three to five color reagents. The information processing system according to claim 1.

10. The aforementioned color reagents are three to five color reagents selected from bromocresol green, methyl red, Reyhardt's reagent, bromocresol purple, bromopyrogallol red, bromothymol blue, phenol red, and chlorophenol red. The information processing system according to claim 1.

11. The system further includes an evaluation unit that evaluates health indicators related to the health of the subject based on the aforementioned intestinal environment information. The information processing system according to claim 1.

12. The acquisition unit acquires the captured image, which shows the color of the color reagents after the color reaction has been performed, using a color sensor array in which each of the multiple color reagents is arranged at an individual position. The information processing system according to claim 1.

13. We obtained images capturing the color of the color reagent in a color reaction using the target feces. Based on the captured images, intestinal environment information relating to the state of the intestinal environment of the subject is determined. This includes, The process for making the determination involves determining the concentration or amount of at least one intestinal metabolite from among acetic acid, succinic acid, lactic acid, propionic acid, formic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid as intestinal environment information. Information processing methods.

14. Based on the aforementioned intestinal environment information, health indicators related to the health of the subject are evaluated. The information processing method according to claim 13.

15. The process of acquiring the captured image involves acquiring the captured image using a color sensor array in which multiple color reagents are arranged at separate positions. The information processing method according to claim 13.

16. The aforementioned color reagents are three to five color reagents selected from bromocresol green, methyl red, Reyhardt's reagent, bromocresol purple, bromopyrogallol red, bromothymol blue, phenol red, and chlorophenol red. The information processing method according to claim 13.

17. An article comprising a color reagent for use in a color reaction using the target feces, wherein the color change of multiple color reagents can be observed, The aforementioned color reagent is one or more color reagents selected from bromocresol green, methyl red, Reyhardt's reagent, bromocresol purple, bromopyrogallol red, bromothymol blue, phenol red, and chlorophenol red. Based on the aforementioned color change, the concentration or amount of at least one intestinal metabolite from among acetic acid, succinic acid, lactic acid, propionic acid, formic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid is used to determine the intestinal environment information of the subject. Goods.

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