Method and system for testing analyte, medium, and device

Abstract

The invention provides an analyte detection method and system, a medium and equipment, and belongs to the field of optical analys.The method comprises the steps that imaging is conducted, specifically, a first area is irradiated through structured light within a first wavelength range and imaged, and a first image of the imaging area is obtained; irradiating the first area through ultraviolet light in a second wavelength range and imaging the first area to obtain a second image of the imaging area; a spectrum acquisition step: acquiring depth information reflecting non-uniform distribution of the analyte in the imaging area from the first image; according to the depth information, acquiring spectral data reflecting non-uniform distribution of the analyte in the imaging area at the required position from the second image; and an analysis step: obtaining the information of the analyte in the imaging area according to the obtained spectral data. The region where the blood vessel is located and the region where the non-blood vessel is located are distinguished through the structured light, accurate selection of a detection point and a reference point can be further achieved, and the detection result is more accurate.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202410951446.6 (22) Application Date 2024.07.16 (71) Applicant Guangzhou Ruixin Microelectronics Co., Ltd. Address 510535, Room 1703, No. 188, Kaitai Avenue, Huangpu District, Guangzhou, Guangdong Province (72) Inventor Zheng Hongzhi (74) Patent Agency Shanghai Duan & Duan Law Firm 31334 Patent Attorney Shi Jiawei (51) Int.Cl. A61B 5 / 1455 (2006.01) A61B 5 / 145 (2006.01) A61B 5 / 00 (2006.01) G06V 40 / 145 (2022.01) G06V 10 / 143 (2022.01) G06V 10 / 145 (2022.01) G06T 7 / 00 (2017.01) G06T 7 / 521 (2017.01) (54) Invention Title: Method, System, Medium and Device for Detection of Analytes (57) Abstract: This invention provides a method, system, medium and device for detecting analytes, belonging to the field of optical analysis. The method includes: an imaging step: irradiating a first region with structured light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; a spectral acquisition step: acquiring depth information reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring spectral data reflecting the non-uniform distribution of the analyte in the imaging region at the required position from the second image based on the depth information; and an analysis step: obtaining information about the analyte in the imaging region based on the acquired spectral data. This application uses structured light to distinguish between regions where blood vessels are located and regions where blood vessels are not located, and further enables accurate selection of detection points and reference points, making the detection results more accurate. Claims 2 pages, Description 13 pages, Drawings 6 pages, CN 121337331 A 2026.01.16 CN 1 21 33 73 31 A 1. A method for detecting an analyte, characterized in that it comprises: an imaging step: illuminating a first region with structured light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and illuminating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; a spectral acquisition step: acquiring depth information reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the second image based on the depth information;Analysis steps: 1. Obtain information about the analyte in the imaging region based on the acquired spectral data, wherein the analyte information includes information relating the analyte to the spectral data. 2. The method for detecting an analyte according to claim 1, wherein the spectral acquisition step includes: dividing the imaging region into a candidate region for detection points and a candidate region for reference points based on the depth information of pixels in the first image; selecting a detection point from the corresponding position in the second image from the candidate region for detection points; selecting a reference point from the corresponding position in the second image from the candidate region for reference points; and acquiring the spectral data of the detection point and the spectral data of the reference point, respectively. 3. The method for detecting an analyte according to claim 2, wherein the spectral acquisition step includes: selecting a pixel with a gray value that meets a preset requirement as a detection point or a combination of the pixel and adjacent pixels as a detection point from the corresponding position in the second image from the candidate region for detection points based on the gray value of pixels in the second image; selecting a pixel with a gray value within a preset deviation range from the selected detection point as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point from the candidate region for reference points in the second image; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. 4. The method for detecting an analyte according to claim 3, characterized in that the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device; the light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device. 5. The method for detecting an analyte according to claim 1, characterized in that the analysis step includes inputting the acquired spectral data into a trained analyte detection model and outputting information about the analyte. 6. A detection system for an analyte, characterized in that it comprises: an imaging module: irradiating a first region with structured light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; a spectral acquisition module: acquiring depth information reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the second image based on the depth information; and an analysis module: obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information relating the analyte to the spectral data. 7. The detection system for an analyte according to claim 6, characterized in that the spectral acquisition module comprises: dividing the imaging region into a candidate region for detection points and a reference region based on the depth information of pixels in the first image.The test point candidate region is selected from the corresponding position in the second image, and the reference point candidate region is selected from the corresponding position in the second image, and the spectral data of the test point and the reference point are obtained respectively. 8. The analyte detection system according to claim 7, wherein the spectral acquisition module includes: selecting a pixel whose gray value meets a preset requirement as a test point or a combination of the pixel and its adjacent pixels as a test point, based on the gray value of the pixels in the second image, selecting a pixel whose gray value is within a preset deviation range from the selected test point as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point, and calculating the fluorescence spectral data of the test point and the fluorescence spectral data of the reference point. 9. The analyte detection system according to claim 8, characterized in that the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device; the light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device. 10. The analyte detection system according to claim 6, characterized in that the analysis module includes inputting acquired spectral data into a trained analyte detection model and outputting analyte information. 11. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the analyte detection method according to any one of claims 1 to 5. 12. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the computer program is executed by the processor, it implements the steps of the analyte detection method according to any one of claims 1 to 5. Claims 2 / 2 Page 3 CN 121337331 A Method, System, Medium and Equipment for Detection of Analytes Technical Field

[0001] This invention relates to the field of optical analysis, specifically to a method, system, medium and equipment for detecting analytes. Background Art

[0002] In the detection technology for analytes, commonly used detection technologies include electrochemical methods and optical methods. Taking the detection of glucose in the human body as an example: Patent document US20100065441A1 discloses an analyte monitoring system, equipment and method, in which a sensor is implanted under the skin of the human body to undergo an electrochemical reaction with glucose under the skin, thereby obtaining the glucose level.The advantage of this approach is that it can collect glucose data in real time throughout the day as needed, without the need for multiple punctures, greatly facilitating user use. However, its disadvantage is that it uses an invasive method, implanting the sensor under the skin. Patent document US20160287147A1 discloses a device for non-invasive in vivo measurement using Raman spectroscopy, which uses Raman spectroscopy to measure in vivo blood glucose concentration. The advantage of this approach is that it has higher accuracy compared to the electrochemical method of US20100065441A1, but its disadvantage is that it currently requires a laboratory-grade Raman spectroscopy system, which is bulky and expensive.

[0003] In addition, patent document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, achieving non-invasive detection. This document uses absorption spectroscopy, and the spectral signals collected and analyzed include not only the spectral signals of blood glucose, but also the spectral signals of components such as skin tissue. The spectral signals of different wavelengths are superimposed, making it difficult to finely separate and extract the spectral signals related to blood glucose. Meanwhile, factors such as the excitation light source, human skin color, and differences in epidermal thickness can also affect the intensity of the spectral signal, resulting in differences in the intensity of the spectral signal. The final acquired spectral signal is easily affected and cannot be strongly correlated with blood glucose concentration, affecting the accurate measurement of blood glucose concentration. Similarly, patent document CN108542402A also has the same problem.

[0004] Products using electrochemical methods for detection have been developed for more than 10 years, while Raman spectroscopy is still difficult to industrialize because it is difficult to achieve the purpose of real-time detection by portability like patent document US20100065441A1.

[0005] Patent document CN117503123A discloses a non-invasive blood glucose detection system and method based on multi-wavelength near-infrared, which uses multiple sensors and modules to simultaneously acquire multiple biological signals such as fingertip infrared information, facial infrared information, and forehead temperature information, and integrates multiple information for analysis and judgment. Its drawbacks are that it requires the collection of too many biological signals, and the locations are different. Blood glucose concentrations also vary in different body parts, resulting in high costs and an inability to achieve portable real-time detection. Furthermore, the excessive information leads to complex detection algorithms. Summary of the Invention

[0006] To address the shortcomings of the prior art, the purpose of this invention is to provide a method, system, medium, and device for detecting analytes.

[0007] According to the present invention, a method for detecting analytes includes:

[0008] An imaging step: irradiating a first region with structured light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; Specification 1 / 13 pages 4 CN121337331 A

[0009] Spectral acquisition step: Obtain depth information reflecting the non-uniform distribution of the analyte in the imaging region from the first image; based on the depth information, obtain spectral data reflecting the non-uniform distribution of the analyte in the imaging region at the required position from the second image;

[0010] Analysis step: Obtain information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information related to the spectral data.

[0011] Further, the spectral acquisition step includes:

[0012] Dividing the imaging region into a candidate region for detection points and a candidate region for reference points based on the depth information of the pixels in the first image, selecting a detection point from the candidate region for detection points, selecting a reference point from the candidate region for reference points, and obtaining the spectral data of the detection point and the spectral data of the reference point respectively.

[0013] Further, the spectral acquisition step includes:

[0014] Based on the gray values ​​of pixels in the second image, selecting a pixel whose gray value meets a preset requirement from the candidate detection point region as a detection point, or a combination of the pixel and its neighboring pixels as a detection point; selecting a pixel whose gray value is within a preset deviation range from the selected detection point from the candidate reference point region as a reference point, or a combination of the pixel and multiple neighboring pixels as a reference point; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0015] Further, the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device;

[0016] Light in the second wavelength range can excite fluorescence radiation signals in the analyte, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device.

[0017] Further, the analysis step includes inputting the acquired spectral data into the trained analyte detection model after preprocessing, and outputting the information of the analyte.

[0018] A detection system for an analyte according to the present invention includes:

[0019] an imaging module: irradiating a first region with structured light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region;

[0020] a spectral acquisition module: acquiring depth information reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the second image based on the depth information;

[0021] an analysis module: obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information relating the analyte to the spectral data.

[0022] Further, the spectral acquisition module includes:

[0023] dividing the imaging area into a detection point candidate area and a reference point candidate area according to the depth information of the pixels in the first image, selecting a detection point from the detection point candidate area, selecting a reference point from the reference point candidate area, and acquiring the spectral data of the detection point and the spectral data of the reference point respectively.

[0024] Further, the spectral acquisition module includes:

[0025] selecting a pixel whose gray value meets a preset requirement as a detection point or a combination of the pixel and its neighboring pixels as a detection point according to the gray value of the pixels in the second image, selecting a pixel whose gray value is within a preset deviation range from the selected detection point as a reference point or a combination of the pixel and its multiple neighboring pixels as a reference point from the reference point candidate area, and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0026] Further, the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device;

[0027] Light in the second wavelength range can excite fluorescence radiation signals from the analyte, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device.

[0028] Further, the analysis module includes inputting the acquired spectral data into a trained analyte detection model after preprocessing, and outputting information about the analyte.

[0029] According to the present invention, a computer-readable storage medium storing a computer program is provided, wherein when the computer program is executed by a processor, the steps of the analyte detection method are implemented.

[0030] According to the present invention, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the analyte detection method are implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present application uses structured light to distinguish between areas where blood vessels are located and areas where blood vessels are not located, and further enables accurate selection of detection points and reference points, making the detection results more accurate.

[0033] 2. The technical solution of the present application does not require electrochemical reaction with the analyte, making the detection method more convenient, especially when detecting analytes in living organisms, it can achieve the purpose of non-invasive detection.

[0034] 3. By utilizing the non-uniform distribution of the analyte in the imaging area, the present application can obtain spectral data of different areas. Since the distribution of other components besides the analyte in the imaging area is relatively uniform, the spectral data of different areas can be obtained.The difference can directly reflect the information of the correlation between the analyte and the spectral data after the influence of non-analytes has been basically eliminated, such as the concentration of the analyte.

[0035] 4. This application uses fluorescence spectroscopy for detection, avoiding the traditional method of measuring analytes using Raman spectroscopy, thereby achieving low cost and miniaturization of the detection system, and achieving the purpose of real-time detection.

[0036] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0037] FIG1 is a flowchart of Embodiment 1;

[0038] FIG2 is a schematic diagram of a first image acquired in Embodiment 3;

[0039] FIG3 is a schematic diagram of a second image acquired in Embodiment 3;

[0040] FIG4 is a schematic diagram of the detection model of Embodiment 3;

[0041] FIG5 is a schematic diagram of the detection point-reference point spectral data obtained in Embodiment 3;

[0042] FIG6 is the experimental results of the accuracy of the analysis results of the analysis model;

[0043] FIG7 is a structural schematic diagram of an analyte detection device provided in Embodiment 6;

[0044] FIG8 is a structural schematic diagram of an electronic device provided in Embodiment 7;

[0045] FIG9 is a structural schematic diagram of an analyte detection watch provided in Embodiment 5;

[0046] FIG10 is a schematic diagram of the back of the analyte detection watch;

[0047] FIG11 is an exploded view of the analyte detection watch;

[0048] Figure 12 is a schematic diagram of the analytical substance detection watch in use. Instruction manual 3 / 13 pages 6 CN 121337331 A

[0049] In the figure:

[0050] 100: Imaging area; 200: Detection device;

[0051] 201: Light source; 202: Imaging spectral detection device;

[0052] 203: Controller; 204: First bandpass filter;

[0053] 205: Lens; 206: Second bandpass filter;

[0054] 207: Circuit board; 501: Processor;

[0055] 502: Memory. Detailed Description

[0056] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0057] Example 1

[0058] Figure 1 is a flowchart of this embodiment. This embodiment provides a method for detecting an analyte, including:

[0059] Imaging step: Irradiating a first region with light within a preset wavelength range provided by a light source, and imaging the first region using an imaging spectral detection device to obtain an image of the imaging region. Irradiation with light within a preset wavelength range...The image obtained can reflect the distribution data and spectral data of the reflected or excitation signals generated by the analyte under light illumination in the imaging area. The first area can be a region on the surface of human skin. To avoid the influence of external light such as ambient light on the detection, the acquisition window of the imaging spectral detection device needs to be tightly attached to the surface of human skin in the first area. The imaging area refers to the area within the lens range of the imaging spectral detection device. In general, the imaging area can be a part of the first area or the same area as the first area.

[0060] Since different wavelength ranges of light are needed to irradiate the analyte to obtain the distribution data and spectral data, there are two ways to achieve this: the light is a light with a large wavelength range provided by one light source or light with a small wavelength range provided by two light sources. When there is one light source, the wavelength range of the light provided by the light source needs to cover both the wavelength range that can acquire the distribution data of the analyte and the wavelength range that can acquire the spectral data of the analyte. When there are two light sources, the two light sources provide different light, one light whose wavelength covers the wavelength range that can acquire the distribution data of the analyte, and the other light whose wavelength covers the wavelength range that can acquire the spectral data of the analyte. Meanwhile, when there is only one light source, the image is one; when there are two light sources, the image is two. For ease of processing, the imaging areas of the two images must be the same, that is, the acquisition window of the imaging spectral detection device must not move on the surface of the human skin.

[0061] In this application, the analyte can be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, or troponin, or it can be a drug such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline, or warfarin. In embodiments that detect two or more analytes, the analytes can be monitored at the same or different times. In other embodiments, the analyte can also be other substances in the body surface, and non-invasive detection can be achieved through this invention.

[0062] Spectral acquisition step: The imaging spectral detection device acquires spectral data from the image reflecting the uneven distribution of the reflected or excitation signals generated by the analyte under light illumination in the imaging area. Specifically, the imaging area can be divided into zones according to different distribution patterns to facilitate the selection of locations for acquiring spectral data from different zones.

[0063] Analysis step: Based on the acquired spectral data, information about the analyte in the imaging area is obtained. The analyte information package...This includes information about the correlation between the analyte and the spectral data. Because the distribution of analytes varies in different regions, the reflected or excitation signals produced by the analyte when exposed to light will also differ. Taking human skin as an example, it is divided into three parts: the epidermis, dermis, and subcutaneous tissue, with veins and other blood vessels located in the subcutaneous tissue. Irradiating skin areas with and without blood vessels with ultraviolet light will yield corresponding spectral data, as will skin areas with thicker and thinner blood vessels. The difference between these two spectral data points reflects information about the correlation between the analyte in the blood vessels and the spectral data, such as the degree of influence of the analyte on the spectral data, providing intermediate information for further analysis, or directly obtaining information such as the concentration of the analyte through an analytical model.

[0064] Example 2

[0065] This example is based on Example 1, using structured light and ultraviolet light as light sources, to provide a method for detecting an analyte, including:

[0066] Imaging step: Irradiating a first region with structured light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region;

[0067] Spectral acquisition step: Obtaining depth information reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and obtaining spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the second image based on the depth information;

[0068] Analysis step: Obtaining information about the analyte in the imaging region based on the acquired spectral data, wherein the information about the analyte includes information related to the analyte and the spectral data.

[0069] The spectral acquisition step includes:

[0070] Dividing the imaging area into a candidate detection point area and a candidate reference point area according to the depth information of the pixels in the first image, selecting a detection point from the candidate detection point area, selecting a reference point from the candidate reference point area, and acquiring the spectral data of the detection point and the spectral data of the reference point respectively.

[0071] The spectral acquisition step includes:

[0072] Based on the gray value of the pixels in the second image, selecting a pixel whose gray value meets a preset requirement from the candidate detection point area as a detection point or a combination of the pixel and its neighboring pixels as a detection point, selecting a pixel whose gray value is within a preset deviation range from the selected detection point from the candidate reference point area as a reference point or a combination of the pixel and its multiple neighboring pixels as a reference point, and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0073] The second wavelength range is 300-390 nanometers, which is outside the effective response range of the imaging spectral detection device;

[0074] The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is located within the effective response range of the imaging spectral detection device.

[0075] The analysis step includes inputting the acquired spectral data into the trained analyte detection model after preprocessing, and outputting the information of the analyte.

[0076] The imaging step includes: acquiring a first image under structured light irradiation, and acquiring a second image under ultraviolet light irradiation within the wavelength range of 300-390 nanometers;

[0077] The first image includes depth information reflecting the uneven distribution of the analyte in the imaging region;

[0078] The second image includes spectral data reflecting the uneven distribution of the fluorescence radiation signal excited by the analyte under ultraviolet light irradiation in the imaging region;

[0079] The spectral acquisition step includes: dividing the imaging region into a candidate region for detection points with smaller depth values ​​and a candidate region for reference points with larger depth values ​​according to the depth information; selecting a pixel with a gray value that meets a preset requirement from the second image as a detection point or a combination of the pixel and adjacent pixels as a detection point according to the candidate region for detection points; selecting a pixel with a gray value within a preset deviation range from the gray value of the detection point from the second image as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point according to the candidate region for reference points;

[0080] Substitute the gray values ​​of the detection points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the detection points, and substitute the gray values ​​of the reference points in the second image into the spectral reconstruction algorithm to obtain the spectral data of the reference points;

[0081] The analysis steps include: inputting the spectral data of the detection points and the spectral data of the reference points into the trained analyte detection model after preprocessing, and outputting the information related to the analyte and the spectral data;

[0082] The method of training the analyte detection model includes: simultaneously acquiring the spectral data of the tested object and the accurate test result, using the preprocessed spectral data as the input of the detection model and the accurate test result as the output of the detection model, and training the detection model.

[0083] Example 3

[0084] This example is based on Example 1, taking the detection of glucose in human blood vessels as an example, and provides a non-invasive glucose detection method, including:

[0085] Imaging step: irradiating the skin at the location of the vein, such as the wrist or back of the hand, with infrared light in the first wavelength range of 800-1000 nanometers, and acquiring the first image of the imaging area, preferably the near-infrared band. And by irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nanometers, a second image of the imaging area is acquired.

[0086] As shown in Figure 2, where the horizontal axis is the horizontal coordinate of the first image and the vertical axis is the vertical coordinate of the first image,The white boxes represent the selected detection point pixels on the veins, and the black boxes represent the selected reference point pixels on the surrounding skin. In the first image, part of the infrared light penetrates the human skin and part is absorbed by the skin. At the same time, it is also largely absorbed by the veins. Therefore, the pixel gray values ​​in the vein area are smaller, and the pixel gray values ​​in the non-vein area are larger. This makes it easy to divide the imaging area into the vein area and the non-vein area.

[0087] As shown in Figure 3, the horizontal axis is the horizontal coordinate of the second image, and the vertical axis is the vertical coordinate of the second image. The white boxes represent the selected detection point pixels on the veins, and the black boxes represent the selected reference point pixels on the surrounding skin. In the second image, it is difficult to distinguish between the vein area and the non-vein area, so the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain high-quality effective fluorescence spectrum signals. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. When the excitation wavelength is less than 300 nm, the main peak of the excited fluorescence radiation signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. When the excitation wavelength is greater than 390 nm, the excitation light itself is also visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to eliminate the interference of the excitation light spectral signal and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the 300-390 nm wavelength range, glucose in venous blood vessels can emit fluorescence radiation signals in the 400-800 nm visible light band. This band is within the effective response range of the imaging spectral detection device, and the characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the glucose concentration, exhibiting high fluorescence excitation efficiency.

[0088] Spectral acquisition steps: Based on the grayscale distribution of pixels in the first image, the imaging area is divided into areas where veins are located and areas where non-vein vessels are located. Detection points are selected from the locations in the second image corresponding to the locations in the vein areas, and reference points are selected from the locations in the second image corresponding to the locations in the non-vein areas. Spectral data of the detection points and reference points in the second image are acquired respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel whose grayscale value meets preset requirements is selected from the vein areas as a detection point, or a combination of that pixel and adjacent pixels is selected as a detection point. A pixel whose grayscale value is within a preset deviation range from the selected detection point is selected from the non-vein areas as a reference point, or a combination of that pixel and multiple adjacent pixels is selected as a reference point. The spectral data of the detection points and reference points in the second image are then acquired.The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are calculated. The spectral data are taken from one pixel or the average of multiple pixels of the detection point and the reference point, which can be appropriately selected according to the width of the blood vessel. The average of multiple pixels can improve the signal-to-noise ratio, but it is limited by the width of the blood vessel and avoids taking data from areas outside the blood vessel. Selecting a single pixel has high spatial resolution and is suitable for cases with thin blood vessels, but the signal-to-noise ratio is low. The preset requirement for grayscale value can be to take the point with the smallest grayscale value as the detection point, but this application does not impose this restriction. The calculation results are shown in Figure 5, where the horizontal axis is wavelength (in nm) and the vertical axis is relative radiance (in W / nm). The solid line is the spectral data of the detection point and the dashed line is the spectral data of the reference point. The reason why the gray value of the reference point and the gray value of the selected detection point are within the preset deviation range is that the skin in the imaging area may have influencing factors such as skin color, spots, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot distinguish the areas with these influencing factors. By setting the preset deviation range of the gray value, these influencing factors can be effectively eliminated. In addition, the fact that the gray value and the gray value of the selected detection point are within the preset deviation range can ensure that the selection of the reference point is close to the detection point. For example, if it is selected at the edge of the vein, it can ensure that the color, thickness and other parameters of the epidermis, dermis and subcutaneous tissue are as close as possible, except for the blood vessels, so that the deviation between the spectral data of the detection point and the spectral data of the reference point can minimize the influence of non-analytes.

[0089] In addition to the spectral reconstruction algorithm, the spectral data can also be obtained by forming a radiometric calibration coefficient through the previous radiometric calibration, and the spectral line is obtained by calculating the gray value * the radiometric calibration coefficient.

[0090] When a combination of multiple pixels is selected as the detection point, the fluorescence spectral data of the detection point can be the average value of the fluorescence spectral data of these pixels. Meanwhile, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average value of the fluorescence spectral data of all detection points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0091] Analysis steps: After preprocessing, the spectral data of the acquired detection points and reference points are input into the trained detection model, and the glucose concentration or intermediate result of the correlation between glucose and spectral data is output. When the detection model is trained, it is necessary to simultaneously acquire the spectral data of the tested object and the accurate test result, such as the blood test result. The spectral data is used as the input of the detection model, and the blood test result is used as the output of the detection model to train the detection model.

[0092] The detection model can adopt a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer and an output layer. The convolutional layers and the activation function layers are...The activation function layers are spaced out; the activation function used in the activation function layers is the ReLU function.

[0093] In the convolutional neural network model, the kernel size of each layer is 1, the number of kernels in the first convolutional layer is 32, and the number of kernels in the second convolutional layer is 64. They are all used to extract blood glucose features, and the output of the convolutional layer is nonlinearly transformed by the activation function. The flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connecting the subsequent fully connected layers. The final output dimension is 1. The Adam optimizer is used to train the model during the model training process, and the mean squared error is used as the loss function. At the same time, the mean absolute error is calculated as the performance index for model evaluation. Specification 7 / 13 pages 10 CN 121337331 A

[0094] When the output result of the detection model is glucose concentration, if the error between the output result and the measured standard glucose concentration value meets the preset condition, the training is stopped and the detection model is obtained. When the output result of the detection model is the intermediate result of glucose and spectral data association, such as the intermediate neuron result, if the error between the output result and the intermediate neuron result meets the preset condition, the training is stopped and the detection model is obtained. Further model correction processing is performed on the intermediate neuron results to obtain the glucose concentration.

[0095] As shown in Figure 4, the Input layer is the spectral data input layer, which is obtained after preprocessing the original spectral data. The Hidden layer is the intermediate hidden layer, which outputs the final predicted blood glucose concentration value after feature combination through convolution operation deep learning. Alternatively, a neuron Output1 can be output as an intermediate result value after feature combination through convolution operation deep learning. The intermediate result value Output1 and two infrared IR feature brightness values ​​are used for model training again to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value Output2. The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the setting of different parameters until the error between the output result and the standard glucose value of the above label value meets the requirements, then the training stops and the detection model is obtained.

[0096] Through multiple iterations of training, the neurons learn the corresponding change law between different glucose concentrations and glucose spectral features of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentration of different users.

[0097] The entire glucose detection process does not require puncturing the skin to collect blood or implant a needle. It acquires the spectral information of the subject based on fluorescence spectroscopy and obtains the glucose detection result based on this spectral information, avoiding pain and discomfort and improving the comfort and convenience of the test. This method can precisely distinguish the spectral signals of blood vessels and skin.This provides the possibility for accurate extraction of glucose signals in the future, and also makes the spectral signal strongly correlated with glucose concentration, realizing accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0098] Figure 6 shows a schematic diagram of the experimental effect of the trained detection model. The horizontal axis is the reference blood glucose concentration collected by the blood glucose meter (unit: mmol / L), and the vertical axis is the blood glucose concentration predicted by the method of this patent (unit: mmol / L). The total sample size of the test subjects was 2037, of which 1537 were training set samples and 500 were prediction set samples. As can be seen from the figure, the distribution of the detection results of the detection model is as follows: the MARD value of the predicted samples is 11.32%, and the vast majority of samples fall in areas A and B. Among them, the proportion of samples falling in area A is 87.03%, and the proportion of samples falling in area B is 12.77%, indicating that the detection accuracy of the detection model is high.

[0099] Example 4

[0100] This example is based on Example 3, replacing infrared light with visible light to provide another non-invasive glucose detection method, including:

[0101] Imaging steps: Irradiating the skin at the location of the vein, such as the wrist or back of the hand, with visible light to acquire a first image of the imaging area. Irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nm to acquire a second image of the imaging area.

[0102] In the first image, since the color of the area where the vein is located differs from that of the area where the vein is located, the imaging area can be easily divided into the area where the vein is located and the area where the vein is located.

[0103] In the second image, since it is difficult to distinguish between the area where the vein is located and the area where the vein is located, the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain a high-quality effective fluorescence spectral signal. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. When the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence radiation signal is located in the <400 nm band. Therefore, the imaging spectral detection device cannot obtain a high-quality effective fluorescence spectrum signal. When the wavelength of the excitation light used is >390 nm, the excitation light itself is also visible light. The spectral signal of the excitation light is superimposed on the fluorescence spectrum signal, making it difficult to eliminate the interference of the excitation light's spectral signal and extract the effective fluorescence spectrum signal. After absorbing ultraviolet light in the 300-390 nm wavelength range, glucose in venous blood vessels can emit a fluorescence radiation signal in the 400-800 nm visible light band. This band is within the effective response range of the imaging spectral detection device. The characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the glucose concentration, exhibiting high fluorescence excitation efficiency.

[0104] Spectral acquisition steps: Based on the grayscale distribution of pixels in the first image, the imaging area is divided into regions containing veins and regions not containing veins. Detection points are selected from the second image corresponding to the regions containing veins, and reference points are selected from the second image corresponding to the regions not containing veins. The spectral data of the detection points and the spectral data of the reference points are then acquired. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value that meets preset requirements, or a combination of that pixel and its neighboring pixels, is selected from the regions containing veins as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point, or a combination of that pixel and several neighboring pixels, is selected from the regions not containing veins as a reference point. The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are then calculated. The reason why the grayscale value of the reference point is within the preset deviation range from the grayscale value of the selected detection point is that the skin in the imaging area may have influencing factors such as skin color, pigmentation, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot simultaneously distinguish regions with all influencing factors. By setting a preset deviation range for the grayscale values, these influencing factors can be effectively eliminated.

[0105] When selecting a combination of multiple pixels as the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixels. Simultaneously, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average value of the fluorescence spectrum data of all detection points and the average value of the fluorescence spectrum data of all reference points can be calculated separately.

[0106] Analysis steps: After preprocessing, the acquired spectral data of the detection points and reference points are input into the trained detection model, which outputs the glucose concentration. During training, the detection model needs to simultaneously acquire the spectral data of the tested object and accurate test results, such as blood test results. The spectral data is used as the input of the detection model, and the blood test results are used as the output of the detection model to train the detection model.

[0107] The detection model can adopt a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer, and an output layer. The convolutional layers and the activation function layers are distributed alternately; the activation function used in the activation function layer is the ReLU function.

[0108] In the convolutional neural network model, each convolutional kernel has a size of 1. The first convolutional layer has 32 kernels, and the second convolutional layer has 64 kernels, both used to extract blood glucose features. The output of the convolutional layer is non-linearly transformed through an activation function. The flatten layer flattens the output of the convolutional layer into a one-dimensional vector, facilitating connection to subsequent fully connected layers, resulting in a final output dimension of 1. During model training, the Adam optimizer is used for model training, and the mean squared error is used as the loss function.The average absolute error is calculated as a performance indicator for model evaluation.

[0109] If the error between the output result of the detection model and the standard glucose value meets the preset condition, the training is stopped and the detection model is obtained.

[0110] The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the setting of different parameters until the error between the output result and the standard glucose value of the above label value meets the requirements, then the training is stopped and the detection model is obtained.

[0111] Through multiple iterations of training, the neurons learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentrations of different users.

[0112] The entire glucose detection process does not require blood collection or skin puncture. It obtains the spectral information of the test subject based on fluorescence spectroscopy and obtains the glucose detection result of the test subject based on the spectral information, avoiding pain and discomfort and improving the comfort and convenience of the detection. This method can precisely distinguish the spectral signals of blood vessels and skin, providing the possibility for accurate extraction of glucose signals in the future. It also strongly correlates the spectral signals with glucose concentration, achieving accurate measurement of glucose concentration, resulting in more accurate detection and easier processing.

[0113] Example 5

[0114] This example provides an analyte detection system. The analyte detection system can be implemented by executing the steps of the analyte detection method. Those skilled in the art can understand the analyte detection method as a preferred embodiment of the analyte detection system. The analyte detection system includes:

[0115] An imaging module: A light source provides light within a preset wavelength range to irradiate a first region, and an imaging spectral detection device images the first region to obtain an image of the imaging region. Through irradiation with light within a preset wavelength range, the image reflects the distribution data and spectral data of the reflected or excitation signals generated by the analyte under light irradiation in the imaging region. Since different wavelength ranges are required to obtain the analyte distribution data and spectral data, the light can be two corresponding wavelength ranges, or a light with a larger wavelength range covering both required wavelength ranges. When there are two types of light, two images are obtained. For ease of processing, it is usually required that the imaging areas of the two images are the same.

[0116] In this application, the analytes may be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, etc., from the blood vessels of animals.Creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, troponin, and may also be drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline, and warfarin. In embodiments that detect more than one analyte, the analytes may be monitored at the same or different times. In other embodiments, the analytes may also be other substances in a liquid.

[0117] Spectrum acquisition module: Acquires spectral data from an image using an imaging spectral detection device, reflecting the uneven distribution of the reflection or excitation signals generated by the analyte under light irradiation in the imaging area. Specifically, the imaging area can be partitioned according to different distribution data to facilitate the selection of locations for acquiring spectral data from different partitions.

[0118] Analysis module: Obtains information about the analytes in the imaging area based on the acquired spectral data. The information about the analytes includes information related to the analytes and the spectral data. Since the distribution of the analytes in different partitions is different, the reflection or excitation signals generated by the analytes under light irradiation will also be different. Using this characteristic, the difference in spectral data between the two can be obtained, thereby accurately reflecting the information related to the analyte and the spectral data, such as the concentration of the analyte.

[0119] Those skilled in the art know that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, the same functions can be achieved by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as both software modules for implementing the method and structures within the hardware component.

[0120] Example 6

[0121] Figure 7 shows an electronic device of this embodiment, specifically an analyte detection device 200. The detection device 200 is a portable, non-invasive detection device for the human body. It can be a standalone detection device or integrated into a watch or mobile phone, thereby enabling convenient and quick detection of analytes on the body surface.

[0122] The detection device 200 includes: a light source 201, an imaging spectral detection device 202, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes connections with the light source 201 and the imaging spectral detection device 202 respectively.Electrical or communication connection.

[0123] Light source 201, capable of providing light within a preset wavelength range. Since different wavelength ranges of light are required to irradiate the analyte to obtain distribution data and spectral data, there are two implementation methods: one light source capable of providing light with a larger wavelength range; or two light sources that each provide light with a smaller wavelength range. When there is only one light source, the wavelength range of the light provided by the light source needs to cover both the wavelength range capable of obtaining distribution data and the wavelength range capable of obtaining spectral data, such as a halogen lamp. When there are two light sources, the two light sources provide different light, one light wavelength covers the wavelength range capable of obtaining distribution data and the other light wavelength covers the wavelength range capable of obtaining spectral data, such as an infrared lamp combined with an ultraviolet lamp, or a visible light lamp combined with an ultraviolet lamp.

[0124] To ensure uniform illumination of the imaging area 100, a ring light source can be used, which has multiple light-emitting modules evenly distributed on the same circumference. When there are two light sources, the light-emitting modules of the two light sources are arranged alternately.

[0125] The imaging spectral detection device 202 can image the imaging area 100 according to instructions to obtain the corresponding image, and can obtain the corresponding spectral data according to instructions. The imaging spectral detection device 202 includes a sensor and a periodic pixel-level filter structure disposed on the surface of the sensor. The periodic pixel-level filter structure is used to spectrally modulate the incoming light signal so that the sensor can generate an image containing the spectral information to be measured.

[0126] The periodic pixel-level filter structure includes multiple filter pixel channels with different shapes of pixel-level structures. The multiple filter pixel channels have the same size and are uniformly arranged. Their length and width are integer multiples of the pixel size in the image sensor. The filter pixel channels of different shapes of pixel-level filter structures correspond to different spectral filtering coefficients. The pixel-level filter structures with different spectral filtering coefficients are combined in a fixed order and arranged periodically. The sensor modulates the received first detection light through the periodic pixel-level filter structure disposed on its surface to form a mosaic image containing spectral information. Then, an algorithm is used to reconstruct a grayscale image containing the spectral information to be measured.

[0127] The controller 203 is configured to control the light source to provide light within a preset wavelength range to illuminate the first region, and to control the imaging spectral detection device to image the first region to obtain an image of the imaging region. The controller also controls the imaging spectral detection device to acquire spectral data from the image reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light illumination within the imaging region. Information about the analyte in the imaging region is obtained based on the acquired spectral data. This information includes information related to the analyte and the spectral data. When there is one type of light source 201, one image is formed; when there are two types of light sources 201, two images are formed. When the first type of light source is on, the second type of light source is off. Similarly, when the second type of light source is on...When the light source is turned on, the first light source is turned off, and the two do not interfere with each other.

[0128] The first bandpass filter 204 is located between the light source 201 and the imaging area 100. Its function is to allow light within a preset wavelength range to pass through, while light outside the preset wavelength range is blocked, thereby reducing the influence of other external light on the detection results.

[0129] The second bandpass filter 206 is located between the imaging spectral detection device 202 and the lens 205. Its function is to allow light within the wavelength range of the reflected signal or excitation signal generated by the analysis object being irradiated by light to pass through, while light in other wavelength ranges is blocked, thereby reducing the influence of the reflected signal or excitation signal of non-analyte on the detection results.

[0130] The lens 205 can be used for focusing in order to obtain a clear image. In other embodiments, the second bandpass filter 206 can also be located on the side of the lens 205 away from the imaging spectral detection device 202, and the present invention does not limit this.

[0131] As described above, Figure 9 shows an analytical substance detection watch provided in this embodiment. The front of the watch is a display, as shown in Figure 10. The back of the watch has a light-transmitting window and a built-in detection device 200. As shown in Figure 11, the light source 201 and the first bandpass filter 204 are both annular structures. The light-emitting modules of the light source 201 are arranged in a ring. The emitted light is filtered by the first bandpass filter 204 and outputs light with a wavelength that meets the requirements. It is then irradiated onto the human body through the light-transmitting window on the back of the watch. The reflected signal or excitation signal of the human body enters the light-transmitting window, passes through the hollow part in the middle of the light source 201 and the first bandpass filter 204, passes through the lens 205, enters the second bandpass filter 206, and after being filtered by the second bandpass filter 206, enters the imaging spectral detection device 202. The imaging spectral detection device 202 is mounted on the circuit board 207. At the same time, the controller 203 (not shown in the figure) of the detection device 200 is also mounted on the circuit board 207. As shown in Figure 12, in order to more accurately identify the location of veins, the watch can be worn on the inside of the wrist.

[0132] Embodiment 7

[0133] Figure 8 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 8, it includes at least one processor 501; and a memory 502 communicatively connected to at least one processor 501; wherein, the memory 502 stores instructions that can be executed by at least one processor 501, and the instructions are executed by at least one processor 501 to enable at least one processor 501 to perform the above-mentioned method for detecting analytes.

[0134] Wherein, the memory 502 and the processor 501 are connected by a bus, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 501 and memory 502 together. The bus may alsoVarious other circuits, such as peripheral devices, voltage regulators, and power management circuits, can be connected together, as is well known in the art, and therefore will not be described further in this invention. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 501 is transmitted over a wireless medium via an antenna, which further receives and transmits data to processor 501.

[0135] Processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 502 can be used to store data used by processor 501 during operation.

[0136] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting analytes.

[0137] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program code.

[0138] Those skilled in the art will understand that the above embodiments are specific embodiments of the present invention. In practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

[0139] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described on pages 12 / 13 of the above specification, CN 121337331 A. Those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the substantive content of the present invention. Where there is no conflict, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Specification 13 / 13 pages 16 CN 121337331 A Figure 1 Figure 2 Specification Drawings 1 / 6 pages 17 CN 121337331 A Figure 3 Figure 4 Specification Drawings 2 / 6 pages 18 CN 121337331 A Figure 5 Figure 6 Specification Drawings 3 / 6 pages 19 CN 121337331 A Figure 7Figure 8 Sheet 4 / 6 of the drawings of the specification 20 CN 121337331 A Figure 9 Figure 10 Sheet 5 / 6 of the drawings of the specification 21 CN 121337331 A Figure 11 Figure 12 Sheet 6 / 6 of the drawings of the specification 22 CN 121337331 A Abstract Abdominal ultrasound examination method, system and device Abstract The present invention relates to the field of optical analysis, and provides a method and a system for testing an analyte, a medium, and a device. The method includes: imaging: irradiating a first area by structural light within a first wavelength range, and imaging the first area, to obtain a first image of an imaging area; and irradiating the first area by ultraviolet light within a second wavelength range, and imaging the first area, to obtain a second image of the imaging area; spectral obtaining: obtaining depth information that demonstrates uneven distribution of the analyte in the imaging area from the first image; and based on the depth information, obtaining spectral data, at a desired position from the second image, that demonstrates uneven distribution of the analyte in the imagingarea; and analysis: obtaining information about the analyte in the imaging area based on the obtained spectral data. According to the present invention, distinction between an area at which a blood vessel is located and an area at which a blood vessel is not located can be implemented through structured light, which can further implement accurate selection of a testing point and reference point, and achieve a more accurate test result.

Claims

1. A method of detecting an analyte, characterized by, The method comprises: an imaging step of irradiating a first region with structured light in a first wavelength range and imaging the first region to obtain a first image of the imaged region; and irradiating the first region with ultraviolet light in a second wavelength range and imaging the first region to obtain a second image of the imaged region; a spectrum acquisition step of acquiring, from the first image, depth information reflecting uneven distribution of an analyte in the imaged region; and acquiring, from the second image, spectrum data of a required position reflecting uneven distribution of the analyte in the imaged region according to the depth information; an analysis step of obtaining information of the analyte in the imaged region according to the acquired spectrum data, the information of the analyte comprising information associated with the spectrum data.

2. The method of claim 1, wherein The spectrum acquisition step comprises: dividing the imaged region into a detection point candidate region and a reference point candidate region according to the depth information of the pixel points in the first image, selecting a detection point from a position corresponding to the detection point candidate region in the second image, selecting a reference point from a position corresponding to the reference point candidate region in the second image, and acquiring spectrum data of the detection point and spectrum data of the reference point, respectively.

3. The method of claim 2, wherein the analyte is a protein. The spectrum acquisition step comprises: selecting, as a detection point, a pixel point with a gray value meeting a preset requirement from a position corresponding to the detection point candidate region in the second image or a combination of the pixel point and adjacent pixel points, and selecting, as a reference point, a pixel point with a gray value within a preset deviation range from the selected detection point from a position corresponding to the reference point candidate region in the second image or a combination of the pixel point and multiple adjacent pixel points, and calculating fluorescence spectrum data of the detection point and fluorescence spectrum data of the reference point.

4. The method of claim 3, wherein the analyte is a protein. The second wavelength range is 300-390 nm, and the second wavelength range is outside the effective response range of the imaging spectrum detection device. The light in the second wavelength range can cause the analyte to emit a fluorescent radiation signal, and a main peak of a fluorescence spectrum of the fluorescent radiation signal is within the effective response range of the imaging spectrum detection device.

5. The method of claim 1, wherein The analysis step comprises inputting the acquired spectrum data into a trained analyte detection model and outputting information of the analyte.

6. A system for detecting an analyte, characterized by The method comprises: an imaging step of irradiating a first region with structured light in a first wavelength range and imaging the first region to obtain a first image of the imaged region; and irradiating the first region with ultraviolet light in a second wavelength range and imaging the first region to obtain a second image of the imaged region; a spectrum acquisition step of acquiring, from the first image, depth information reflecting uneven distribution of an analyte in the imaged region; and acquiring, from the second image, spectrum data of a required position reflecting uneven distribution of the analyte in the imaged region according to the depth information; an analysis step of obtaining information of the analyte in the imaged region according to the acquired spectrum data, the information of the analyte comprising information associated with the spectrum data.

7. The system for detecting an analyte according to claim 6, wherein The spectrum acquisition step comprises: The imaging region is divided into a detection point candidate region and a reference point candidate region according to depth information of pixels in the first image, a detection point is selected from a position corresponding to the detection point candidate region in a second image, a reference point is selected from a position corresponding to the reference point candidate region in the second image, and spectral data of the detection point and spectral data of the reference point are obtained respectively.

8. The system for detecting an analyte according to claim 7, wherein The spectral acquisition module comprises: According to the gray value of the pixel in the second image, one pixel point with a gray value meeting a preset requirement is selected as a detection point or a combination of the pixel point and adjacent pixel points from a position corresponding to the detection point candidate region in the second image, and one pixel point with a gray value within a preset deviation range from the selected detection point is selected as a reference point or a combination of the pixel point and multiple adjacent pixel points from a position corresponding to the reference point candidate region in the second image, and fluorescence spectral data of the detection point and fluorescence spectral data of the reference point are calculated.

9. The system for detection of an analyte according to claim 8, wherein, The second wavelength range is 300-390 nanometers, and the second wavelength range is located outside the effective response range of the imaging spectral detection device; The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and a fluorescence spectral main peak of the fluorescence radiation signal is located within the effective response range of the imaging spectral detection device.

10. The analyte detection system of claim 6, wherein, The analysis module comprises inputting the obtained spectral data into a trained analyte detection model and outputting information of the analyte.

11. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the analyte detection method in any one of claims 1 to 5.

12. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the steps of the analyte detection method in any one of claims 1 to 5.

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