Analyte testing, information display method and system, medium, and device

Abstract

The invention provides an analyte detection and information display method and system, a medium and equipment, and belongs to the field of optical analysis, the method comprises the following steps: an instruction acquisition step: acquiring an operation instruction from a user, the operation instruction comprising a detection instruction and a statistical instruction; a first display step: when the operation instruction is a detection instruction, starting a detection process of the analyte detection system, displaying the name of the process being executed on a display, and after the detection process is completed, displaying a detection result of the analyte and a preset reference range on the display; and a second display step: when the operation instruction is a statistical instruction, acquiring a historical detection result from a memory of the analyte detection system, processing the acquired historical detection result into a chart form, and displaying the chart form on a display. According to the method, the accurate detection result can be prompted for the user, and the chart is formed by integrating the historical detection results, so that the user can know the real detection result change trend.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202410951420.1 (22) Application Date 2024.07.16 (71) Applicant Guangzhou Ruixin Microelectronics Co., Ltd. Address Room 1703, No. 188, Kaitai Avenue, Huangpu District, Guangzhou City, Guangdong Province 510535 (72) Inventor Zheng Hongzhi (74) Patent Agency Shanghai Duan & Duan Law Firm 31334 Patent Attorney Huang Lei (51) Int.Cl. A61B 5 / 1455 (2006.01) A61B 5 / 145 (2006.01) A61B 5 / 00 (2006.01) G16H 40 / 63 (2018.01) G06V 40 / 145 (2022.01) G06V 10 / 143(2022.01) G06V 10 / 94(2022.01) G06T 7 / 00(2017.01) (54) Invention Title: Analyte Detection, Information Display Method, System, Medium and Device (57) Abstract: This invention provides an analyte detection, information display method, system, medium and device, belonging to the field of optical analysis. The method includes: Instruction acquisition step: acquiring operation instructions from the user, the operation instructions including detection instructions and statistical instructions; First display step: when the operation instruction is a detection instruction, starting the detection process of the analyte detection system and displaying the name of the process being executed on the display; after the detection process is completed, displaying the detection result of the analyte and the preset reference range on the display; Second display step: when the operation instruction is a statistical instruction, acquiring historical detection results from the memory of the analyte detection system, processing the acquired historical detection results into a chart form and displaying it on the display. This application can provide users with accurate detection results and integrate historical detection results to form a chart, so that users can understand the real trend of detection result changes. Claims 1 page, Description 11 pages, Drawings 7 pages, CN 121337328 A 2026.01.16 CN 1 21 33 73 28 A 1. A method for displaying information on analyte detection, characterized in that it includes: an instruction acquisition step: acquiring an operation instruction from a user, the operation instruction including a detection instruction and a statistical instruction; a first display step: when the operation instruction is a detection instruction, starting the detection process of the analyte detection system and displaying the name of the process being executed on the display; after the detection process is completed, displaying the detection result of the analyte and a preset reference range on the display; a second display step: when the operation instruction is a statistical instruction, acquiring historical detection results from the memory of the analyte detection system, processing the acquired historical detection results into a chart form and displaying it on the display.2. The method for displaying information on analyte detection according to claim 1, characterized in that the method of displaying the detection results of the analyte on the display includes displaying them in text and numerical form, with the numbers retained to two decimal places. 3. The method for displaying information on analyte detection according to claim 1, characterized in that the chart form includes a curve showing the change in historical detection results. 4. A method for detecting an analyte, characterized in that it includes the method for displaying information on analyte detection according to any one of claims 1-3. 5. An information display system for analyte detection, characterized in that it includes: an instruction acquisition module: acquiring operation instructions from a user, the operation instructions including detection instructions and statistical instructions; a first display module: when the operation instruction is a detection instruction, initiating the detection process of the analyte detection system and displaying the name of the process being executed on the display, and after the detection process is completed, displaying the detection results of the analyte and a preset reference range on the display; a second display module: when the operation instruction is a statistical instruction, acquiring historical detection results from the memory of the analyte detection system, processing the acquired historical detection results into a chart form and displaying it on the display. 6. The information display system for analyte detection according to claim 5, characterized in that the method of displaying the analyte detection results on the display includes displaying them in text and numerical form, with the numbers retained to two decimal places. 7. The information display system for analyte detection according to claim 5, characterized in that the chart form includes a curve showing the change in historical detection results. 8. An analyte detection system, characterized in that it includes the information display system for analyte detection according to any one of claims 5-7. 9. 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 3. 10. An electronic device, including 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 3. Claims 1 / 1 Page 2 CN 121337328 A Analyte Detection, Information Display Method, System, Medium and Equipment Technical Field

[0001] This invention relates to the field of optical analysis, specifically, to an analyte detection, information display method, system, medium and equipment. 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, which greatly facilitates user use. However, its disadvantage is that it uses an invasive method to implant 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 can achieve non-invasive detection, but its disadvantage is that it currently relies on a laboratory-grade Raman spectroscopy system, which has limited accuracy and is bulky and cannot be portable.

[0003] In addition, patent document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis. 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 epidermal thickness differences can also affect the intensity of the spectral signal, resulting in differences in spectral signal intensity. 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. Therefore, the detection results are difficult to accurately convey to the user and have little reference value. Summary of the Invention

[0004] To address the deficiencies in the prior art, the purpose of this invention is to provide an analyte detection, information display method, system, medium, and device.

[0005] According to the analyte detection information display method provided by this invention, it includes:

[0006] Instruction acquisition step: acquiring operation instructions from the user, the operation instructions including detection instructions and statistical instructions;

[0007] First display step: when the operation instruction is a detection instruction, starting the detection process of the analyte detection system and displaying the name of the process being executed on the display; after the detection process is completed, displaying the detection results of the analyte and a preset reference range on the display;

[0008] Second display step: when the operation instruction is a statistical instruction, acquiring historical detection results from the memory of the analyte detection system, processing the acquired historical detection results into a chart form, and displaying it on the display.

[0009] Further, the method of displaying the detection results of the analyte on the display includes displaying them in text and numerical form, with the numbers retained to two decimal places.

[0010] Further, the chart form includes a curve showing the change of historical detection results. Specification 1 / 11 page 3 CN 121337328 A

[0011] According to the analyte detection method provided by the present invention, the above-described analyte detection information display method is used.

[0012] According to the analyte detection information display system provided by the present invention, it includes:

[0013] Instruction Acquisition Module: Acquires operation instructions from the user, including detection instructions and statistical instructions;

[0014] First Display Module: When the operation instruction is a detection instruction, initiates the detection process of the analyte detection system and displays the name of the process being executed on the display. After the detection process is completed, displays the detection results of the analyte and the preset reference range on the display;

[0015] Second Display Module: When the operation instruction is a statistical instruction, retrieves historical detection results from the memory of the analyte detection system, processes the retrieved historical detection results into a chart form, and displays it on the display.

[0016] Further, the method of displaying the detection results of the analyte on the display includes displaying them in text and numerical form, with the numbers retaining two decimal places.

[0017] Further, the chart form includes a change curve of historical detection results.

[0018] According to the analyte detection system provided by the present invention, the above-mentioned information display system for analyte detection is adopted.

[0019] 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 displaying information for analyte detection are implemented.

[0020] An electronic device provided by the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the step of displaying information about the detection of the analyte.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present application can provide users with accurate detection results and form charts by integrating historical detection results, so that users can understand the actual trend of the detection results.

[0023] 2. The detection method of the present application does not require electrochemical reaction with the analyte, and the detection method is more convenient. Especially when detecting analytes in living organisms, it can achieve the purpose of non-invasive detection.

[0024] 3. The detection method of the present application can obtain spectral data of different regions by utilizing the non-uniform distribution of the analyte in the imaging region. Since the distribution of other components besides the analyte in the imaging region is relatively uniform, the difference in spectral data of different regions can directly reflect the information of the association between the analyte and the spectral data after basically excluding the influence of non-analytes, such as the concentration of the analyte.

[0025] 4. The detection method of 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. Brief Description of the Drawings

[0026] 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:

[0027] Figure 1 is a flowchart of Embodiment 1;

[0028] Figure 2 is a schematic diagram of the first image acquired in Example 2;

[0029] Figure 3 is a schematic diagram of the second image acquired in Example 2;

[0030] Figure 4 is a schematic diagram of the detection model in Example 2; Specification 2 / 11 pages 4 CN 121337328 A

[0031] Figure 5 is a schematic diagram of the detection point-reference point spectral data obtained in Example 2;

[0032] Figure 6 is the experimental results of the accuracy of the analysis results of the analysis model;

[0033] Figure 7 is a structural schematic diagram of an analytical substance detection device provided in Example 5;

[0034] Figure 8 is a structural schematic diagram of an electronic device provided in Example 6.

[0035] Figure 9 is a schematic diagram of the information display of analytical substance detection;

[0036] Figure 10 is a structural schematic diagram of an analytical substance detection watch provided in Example 5;

[0037] Figure 11 is a schematic diagram of the back of the analytical substance detection watch;

[0038] Figure 12 is an exploded view of the analytical substance detection watch;

[0039] Figure 13 is a schematic diagram of the usage state of the analytical substance detection watch.

[0040] In the figure:

[0041] 100: Imaging area; 200: Detection device;

[0042] 201: Light source; 202: Imaging spectral detection device;

[0043] 203: Controller; 204: First bandpass filter;

[0044] 205: Lens; 206: Second bandpass filter;

[0045] 207: Circuit board; 501: Processor;

[0046] 502: Memory. Detailed Embodiments

[0047] 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.

[0048] Example 1

[0049] Figure 1 is a flowchart of this embodiment. This embodiment provides a method for detecting an analyte, including:

[0050] Imaging step: A first region is irradiated with light within a preset wavelength range provided by a light source, and the first region is imaged by an imaging spectral detection device to obtain an image of the imaging region. Irradiation with light within a preset wavelength range allows the image to reflect the distribution data and spectral data of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging region. The first region can be a certain area 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 region. The imaging region refers to the area within the lens range of the imaging spectral detection device. In general, the imaging region can be a part of the first region or the same region as the first region.

[0051] Since acquiring analyte distribution and spectral data requires illumination from light sources with different wavelength ranges, there are two possible implementation methods: using a single light source with a wide wavelength range, or using two light sources, each with a smaller wavelength range. When using a single light source, the wavelength range of the light provided must simultaneously cover both the wavelength range needed to acquire analyte distribution data and the wavelength range needed to acquire analyte spectral data. When using two light sources, the two sources provide different wavelengths; one wavelength covers the wavelength range needed to acquire analyte distribution data, and the other covers the wavelength range needed to acquire analyte spectral data. Furthermore, when using a single light source, only one image is formed; when using two light sources, two images are formed. For ease of processing, the imaging areas of the two images must be identical, meaning the acquisition window of the imaging spectral detection device must remain stationary on the human skin surface. (Instruction manual page 3 / 11, 5 CN 121337328 A)

[0052] In this application, the analyte may 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 may 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 may be monitored at the same or different times. In other embodiments, the analytes may also be other substances within the body surface, and non-invasive detection can be achieved through this invention.

[0053] Spectral acquisition step: Spectral data reflecting the uneven distribution of reflected or excitation signals generated by the analyte under light irradiation in the imaging area is acquired from the image using an imaging spectral detection device. Specifically, the imaging area can be divided into partitions based on different distribution data to facilitate the selection of locations from different partitions for obtaining spectral data.

[0054] Analysis steps: Based on the acquired spectral data, information about the analytes in the imaging area is obtained. The information about the analytes includes information related to the analytes and the spectral data. Since the distribution of analytes in different partitions is different, the reflected or excitation signals generated by the analytes when irradiated by light will also be different. Taking human skin as an example, it is divided into three parts: epidermis, dermis, and subcutaneous tissue. Veins and other blood vessels are located in the subcutaneous tissue. Ultraviolet light irradiation can obtain corresponding spectral data for both vascular and non-vascular skin areas, or for skin areas with thicker blood vessels and skin areas with thinner blood vessels. The difference between the two spectral data can reflect the presence of blood vessels in the vascular tissue.Information related to the analyte and spectral data, such as the degree of influence of the analyte on the spectral data, is used for further analysis, or the concentration of the analyte can be obtained directly through the analysis model.

[0055] The detection results are displayed in text and number form, with the numbers retained to two decimal places, for example, blood glucose level: 5.06 mmol / L.

[0056] The display interface is executing the display steps, and the display may be in the form of an instrument or a watch.

[0057] Referring to Figure 9, it is a schematic diagram of the information display for analyte detection; the specific steps are as follows:

[0058] Instruction acquisition step: Acquire operation instructions from the user, including detection instructions and statistical instructions;

[0059] First display step: When the operation instruction is a detection instruction, start the detection process of the analyte detection system and display the name of the process being executed on the display. After the detection process is completed, display the detection results of the analyte and the preset reference range on the display; the way to display the detection results of the analyte on the display includes displaying them in text and number form, with the numbers retained to two decimal places.

[0060] Second display step: When the operation instruction is a statistical instruction, historical detection results are retrieved from the memory of the analyte detection system, and the retrieved historical detection results are processed into a chart form and displayed on the display. The chart form includes the change curve of the historical detection results.

[0061] Example 2

[0062] 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:

[0063] Imaging step: The skin at the location of the vein, such as the wrist or back of the hand, is irradiated with infrared light in the first wavelength range of 800-1000 nanometers, and a first image of the imaging area is acquired. The first wavelength range is preferably the near-infrared band. And the same location is irradiated with ultraviolet light in the second wavelength range of 300-390 nanometers, and a second image of the imaging area is acquired. Instruction manual, page 4 / 11, CN 121337328 A

[0064] As shown in Figure 2, 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 box represents the selected detection point pixel block on the vein, and the black box represents the selected reference point pixel block on the surrounding skin. In the first image, part of the infrared light will penetrate the human skin and part will be absorbed by the human skin. At the same time, it will also be absorbed in large quantities by the vein in the vein area. Therefore, the pixel gray value in the vein area is small, and the pixel gray value in the non-vein area is large. In this way, the imaging area can be easily divided into the vein area and the non-vein area.

[0065] 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 vein, and the black boxes represent the selected reference point pixels on the surrounding skin. In the second image, it is difficult to distinguish between areas containing veins and areas not containing veins; therefore, the first image is needed for differentiation. An excitation light in the 300-390 nm wavelength range is used to obtain high-quality effective fluorescence spectral 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 light 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 high-quality effective fluorescence spectral signals. When the excitation light 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's spectral signal and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the wavelength range of 300-390 nm, glucose in veins can emit fluorescence radiation signals in the visible light band of 400-800 nm. 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 concentration of glucose and has high fluorescence excitation efficiency.

[0066] Spectral acquisition steps: According to the gray-scale distribution of pixels in the first image, the imaging area is divided into the area where veins are located and the area where non-vein vessels are located. Detection points are selected from the positions in the second image corresponding to the areas where veins are located, and reference points are selected from the positions in the second image corresponding to the areas where non-vein vessels are located. The spectral data of the detection points and the spectral data of the reference points in the second image are acquired respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value meeting preset requirements is selected from the region where the vein is located as a detection point, or a combination of that pixel and its neighboring pixels is selected as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point is selected from the region where the vein is located, or a combination of that pixel and multiple neighboring pixels is selected as a reference point. The fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are calculated in the second image. The spectral data is taken from a single pixel of the detection point or reference point, or an average of multiple pixels, which can be appropriately selected based on the width of the blood vessel. Averaging multiple pixels can improve the signal-to-noise ratio, but is limited by the width of the blood vessel, avoiding the area outside the blood vessel. Selecting a single pixel has high spatial resolution and is suitable for thinner blood vessels, but the signal-to-noise ratio is lower. The preset requirement for grayscale value can be to select the point with the smallest grayscale value as the detection point, but this application does not impose this limitation. The calculation results are shown in Figure 5, where the horizontal axis represents wavelength (in nm), the vertical axis represents relative radiance (in W / nm), the solid line represents the spectral data of the detection point, and the dashed line represents the light at the reference point.Spectral data. 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 gray values, these influencing factors can be effectively eliminated. In addition, the gray value and the gray value of the selected detection point are within the preset deviation range, which 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 closest, 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.

[0067] In addition to the spectral reconstruction algorithm, the spectral data can also be obtained by generating radiometric calibration coefficients through the previous radiometric calibration, and the spectral lines are obtained by calculating the gray value * the radiometric calibration coefficient.

[0068] When selecting a combination of multiple pixels for detection, 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.

[0069] Analysis steps: After preprocessing, the acquired spectral data of the detection points and reference points are input into the trained detection model, outputting the glucose concentration or intermediate results relating glucose to the spectral data. 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 to the detection model, and the blood test results are used as the output to train the detection model.

[0070] 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.

[0071] 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 mean absolute error is also calculated as a performance metric for model evaluation.

[0072] When the output of the detection model is glucose concentration, if the error between the output and the measured standard glucose concentration value meets the preset condition, training is stopped and the detection model is obtained. When the output of the detection model is an intermediate result of glucose and spectral data association, such as the result of an intermediate neuron, if the error between the output and the result of the intermediate neuron meets the preset condition, training is stopped and the detection model is obtained. Further model correction processing is performed on the result of the intermediate neuron to obtain the glucose concentration.

[0073] 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 performs deep learning through convolution operation, performs feature combination, and outputs the final predicted blood glucose concentration value. Alternatively, deep learning through convolution operation can be used to perform feature combination and output a neuron Output1 as an intermediate result value. 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 level of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the different parameter settings 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.

[0074] Through multiple iterations of training, the neurons learn the corresponding change rules between different glucose concentrations and glucose spectral characteristics of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentrations of different users.

[0075] The entire glucose detection process does not require puncturing the skin to collect blood or puncturing the skin for implantation. It obtains the spectral information of the subject based on fluorescence spectroscopy and obtains the glucose detection result of the subject based on the spectral information, avoiding pain and discomfort and improving the comfort and convenience of the detection. This method can finely distinguish the spectral signals of blood vessels and skin, providing the possibility for accurate extraction of glucose signals in the future. At the same time, it also makes the spectral signal strongly correlated with the glucose concentration, realizing accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0076] Figure 6 shows a schematic diagram of the experimental results of the trained detection model. The horizontal axis represents the reference blood glucose concentration (in mmol / L) collected by the blood glucose meter (page 6 / 11 of the instruction manual, CN 121337328 A), and the vertical axis represents the blood glucose concentration (in mmol / L) predicted using the method of this patent. The total sample size of the test subjects was 2037, including 1537 training set samples and 500 prediction set samples. As can be seen from the figure, the distribution of the detection results of the detection model shows that the MARD value of the predicted samples is 11.32%, and the vast majority of samples fall in areas A and B.Of these, 87.03% of the samples fell into area A, and 12.77% fell into area B, indicating that the detection model has high accuracy.

[0077] Example 3

[0078] This example is based on Example 2, replacing infrared light with visible light to provide another non-invasive glucose detection method, including:

[0079] 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. And 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.

[0080] In the first image, since the color of the area where the vein is located differs from that of the area where the non-vein is located, the imaging area can be easily divided into the area where the vein is located and the area where the non-vein is located.

[0081] In the second image, since it is difficult to distinguish the area where the vein is located and the area where the non-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 spectrum signal. Because the main response band of imaging spectral detection devices is located in the 400-800 nm range. When the excitation light wavelength used 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 light wavelength used 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. Glucose in venous blood vessels, after absorbing ultraviolet light in the 300-390 nm wavelength range, 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.

[0082] 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. The spectral data of the detection points and the spectral data of the reference points are acquired respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value that meets a preset requirement or a combination of that pixel and its adjacent pixels is selected from the vein areas 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 multiple adjacent pixels is selected from the non-vein areas as a reference point. The fluorescence spectral data of the detection points and the fluorescence spectral data of the reference points are calculated.Optical spectral data. The reason why the gray values ​​of the reference points and the selected detection points are within a preset deviation range is that the skin in the imaging area may have influencing factors such as skin color, blemishes, and cosmetics, which will directly affect the spectral data of the reference points. The first image cannot simultaneously distinguish all influencing factors. By setting a preset deviation range for the gray values, these influencing factors can be effectively eliminated.

[0083] When selecting a combination of multiple pixels as the detection point, the fluorescence spectral data of that detection point can be the average of the fluorescence spectral 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 spectral data of all detection points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0084] Analysis steps: After preprocessing, the acquired spectral data of the detection points and reference points are input into the trained detection model (page 7 / 11, CN 121337328 A), and the glucose concentration is output. 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.

[0085] 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.

[0086] In the convolutional neural network model, the kernel size of each convolutional 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. Both are 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 to the subsequent fully connected layers. The final output dimension is 1. The Adam optimizer is used for model training during the model training process, and the mean squared error is used as the loss function. The mean absolute error is calculated as the performance metric for model evaluation.

[0087] If the error between the output of the detection model and the standard glucose value meets the preset conditions, training is stopped to obtain the detection model.

[0088] The training level of the detection model needs to be set with different parameters as required. The extracted glucose feature values ​​will continuously learn according to the different parameter settings until the error between the output and the standard glucose value of the above label value meets the requirements, at which point training is stopped to obtain the detection model.

[0089] Through multiple iterative training iterations, the neurons learn different glucose concentrations and glucose spectra of different samplers.The corresponding change patterns between features improve the universality of the detection model and enable the prediction of glucose concentration for different users.

[0090] 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 finely distinguish the spectral signals of blood vessels and skin, providing the possibility for the accurate extraction of glucose signals in the future. At the same time, it also makes the spectral signal strongly correlated with the glucose concentration, realizing the accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0091] Example 4

[0092] This embodiment provides an analyte detection system. The analyte detection system can be implemented by executing the process steps of the analyte detection method. That is, those skilled in the art can understand the analyte detection method as the preferred embodiment of the analyte detection system. The analyte detection system includes:

[0093] Imaging module: The light source provides light within a preset wavelength range to irradiate the first area, and the imaging spectral detection device images the first area to obtain an image of the imaging area. By illuminating the image with light within a preset wavelength range, the image can reflect the distribution and spectral data of the reflected or excitation signals generated by the analyte under light illumination within the imaging area. Since different wavelength ranges are required to obtain the analyte distribution and spectral data, the light source can be two corresponding wavelength ranges, or a single light source with a larger wavelength range covering both required wavelengths. When two types of light are used, two images are obtained. For ease of processing, it is usually required that the imaging areas of the two images are identical.

[0094] In this application, the analyte may 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, troponin, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis toxins, 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 analyte may also be other substances in a liquid.

[0095] Spectral acquisition module: Acquires from an image using an imaging spectral detection device a reflection of the analyte being exposed to light.The generated reflected or excitation signals are spectral data that are not uniformly distributed in the imaging area. Specifically, the imaging area can be divided into partitions according to different distribution patterns, so as to select the location for obtaining spectral data from different partitions.

[0096] Analysis module: Based on the acquired spectral data, information about the analytes in the imaging area is obtained. The information about the analytes includes information related to the analytes and the spectral data. Since the distribution of analytes in different partitions is different, the reflected or excitation signals generated by the analytes when irradiated by light 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 analytes and the spectral data, such as the concentration of the analytes.

[0097] The detection results are displayed in text and number form, and the numbers will be retained to two decimal places. For example, blood glucose value: 5.06 mmol / L.

[0098] Display interface: The display module is running. The display may be in the form of an instrument or a watch.

[0099] Referring to Figure 9, which is a schematic diagram of information display for analyte detection; the specific modules are:

[0100] Instruction acquisition module: acquires operation instructions from the user, including detection instructions and statistical instructions;

[0101] First display module: when the operation instruction is a detection instruction, starts the detection process of the analyte detection system and displays the name of the process being executed on the display. After the detection process is completed, the detection results of the analyte and the preset reference range are displayed on the display. The method of displaying the detection results of the analyte on the display includes displaying them in text and numerical form, with the numbers retained to two decimal places.

[0102] Second display module: when the operation instruction is a statistical instruction, retrieves historical detection results from the memory of the analyte detection system, processes the retrieved historical detection results into a chart form and displays it on the display. The chart form includes the change curve of the historical detection results.

[0103] 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 pure computer-readable program code, the same functions can be achieved by logically programming the method steps, making the system and its various devices, modules, and units provided by the present invention implement the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. 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 considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules for implementing the method and structures within the hardware component.

[0104] Embodiment 5

[0105] Figure 7 shows an electronic device of this embodiment, specifically an analytical substance detection device 200.200 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.

[0106] 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 an electrical connection or a communication connection with the light source 201 and the imaging spectral detection device 202, respectively. Specification 9 / 11 pages 11 CN 121337328 A

[0107] The light source 201 is capable of providing light within a preset wavelength range. Since different wavelength ranges of light are needed to irradiate the analyte to obtain distribution data and spectral data, there are two implementation methods: one is a light source that can provide light with a larger wavelength range; or, two light sources that each provide light with a smaller wavelength range. When there is only one type of light source, the wavelength range of the light provided by the light source needs to cover both the wavelength range that can acquire data on the distribution of the analyte and the wavelength range that can acquire the spectral data of the analyte, such as a halogen lamp. When there are two types of light sources, the two light sources provide different light, one light whose wavelength covers the wavelength range that can acquire data on the distribution of the analyte, and the other light whose wavelength covers the wavelength range that can acquire the spectral data of the analyte, such as an infrared lamp combined with an ultraviolet lamp, or a visible light lamp combined with an ultraviolet lamp.

[0108] In order to make the illumination of the imaging area 100 uniform, a ring light source can be used, which has multiple light-emitting modules evenly distributed on the same circumference. When there are two types of light sources, the light-emitting modules of the two light sources are arranged alternately.

[0109] The imaging spectral detection device 202 can image the imaging area 100 according to the instructions to obtain the corresponding image, and can obtain the corresponding spectral data according to the instructions. The imaging spectral detection device 202 includes a sensor and a periodic pixel-level filter structure disposed on the sensor surface. The periodic pixel-level filter structure is used to perform spectral modulation on the incoming light signal so that the sensor can generate an image containing the spectral information to be measured.

[0110] The periodic pixel-level filter structure includes multiple filter pixel channels with different shapes. These multiple filter pixel channels have the same dimensions and are uniformly arranged, with their length and width being integer multiples of the pixel size within the image sensor. Different shapes of pixel-level filter channels correspond to different spectral filtering coefficients. Pixel-level filter structures with different spectral filtering coefficients are periodically arranged after being combined in a fixed order. The sensor modulates the received first detection light through the periodic pixel-level filter structure on its surface, forming a mosaic image containing spectral information. Subsequently, the spectral data is reconstructed using an algorithm.

[0111] The controller 203 is configured to control the light source to provide light within a preset wavelength range to illuminate the first area, and to control...The imaging spectral detection device images the first region to obtain an image of the imaging region. The imaging spectral detection device is controlled to acquire spectral data from the image that reflects the uneven distribution of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging region. Based on the acquired spectral data, information about the analyte in the imaging region is obtained. The information about the analyte includes information related to the analyte and the spectral data. When there is one type of light source 201, one image is captured. When there are two types of light sources 201, two images are captured. When the first type of light source is turned on, the second type of light source is turned off. Similarly, when the second type of light source is turned on, the first type of light source is turned off, and the two do not interfere with each other.

[0112] The first bandpass filter 204 is located between the light source 201 and the imaging region 100. Its function is to allow light within a preset wavelength range to pass through, while blocking light outside the preset wavelength range, thereby reducing the influence of other external light on the detection results.

[0113] 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 analyte when it is irradiated to pass through, while light in other wavelength ranges is blocked, thereby reducing the influence of the reflected signal or excitation signal of non-analytes on the detection results.

[0114] 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.

[0115] According to the above description, FIG10 shows an analyte detection watch provided in this embodiment. The front of the watch is a display, as shown in FIG11, and the back of the watch has a light-transmitting window and a built-in detection device 200. As shown in FIG12, 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 the required wavelength. It shines on 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 and enters the second bandpass filter 206. After being filtered by the second bandpass filter 206, it enters the imaging spectrum detection device 202. The imaging spectrum 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 13, in order to more accurately identify the location of veins, the watch can be worn on the inside of the wrist.

[0116] Embodiment 6

[0117] Figure 8 is a schematic diagram of the structure of an electronic device provided in the embodiment of this application. As shown in Figure 8, it includes at least oneA processor 501; and a memory 502 communicatively connected to at least one processor 501; wherein the memory 502 stores instructions executable by at least one processor 501, the instructions being executed by at least one processor 501 to enable at least one processor 501 to perform the above-described method for detecting the analyte.

[0118] The memory 502 and the processor 501 are connected via a bus, which may include any number of interconnected buses and bridges, connecting various circuits of one or more processors 501 and the memory 502 together. The bus may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be further described in this invention. The bus interface provides an interface between the bus and the transceiver. The transceiver may 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 the processor 501 is transmitted over a wireless medium via an antenna, which further receives data and transmits the data to the processor 501.

[0119] The processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces,

[0120] voltage regulation, power management and other control functions. The memory 502 can be used to store the data used by the processor 501 when performing operations.

[0121] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting the analyte.

[0122] 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.

[0123] Those skilled in the art will understand that the above embodiments are specific examples of implementing the present invention, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of the present invention.

[0124] 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 above, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the...This does not affect the essential content of the present invention. Unless otherwise specified, the embodiments and features described herein can be combined arbitrarily. Instruction Manual 11 / 11 Page 13 CN 121337328 A Figure 1 Figure 2 Instruction Manual Drawings 1 / 7 Page 14 CN 121337328 A Figure 3 Figure 4 Instruction Manual Drawings 2 / 7 Page 15 CN 121337328 A Figure 5 Figure 6 Instruction Manual Drawings 3 / 7 Page 16 CN 121337328 A Figure 7 Figure 8 Instruction Manual Drawings 4 / 7 Page 17 CN 121337328 A Figure 9 Figure 10 Instruction Manual Drawings 5 / 7 Page 18 CN 121337328 A Figure 11 Instruction Manual Drawings 6 / 7 Page 19 CN 121337328 A Figure 12 Figure 13 Instruction Manual Drawings 7 / 7 Page 20 CN 121337328 A Abstract The present invention relates to the field of optical analysis, and provides an information display method and system in analytical testing, a medium, and a device. The method includes: instruction obtaining: obtaining an operation instruction from a user, where the operation instruction includes a testing instruction and a statistical instruction; first display: when the operation instruction is the testing instruction, starting a testing process of an analyze testing system and displaying a name of a process being performed on a display, and after the testing process is completed, displaying a test result and a presetreference range of an analyte on the display; and second display: when the operation instruction is the statistical instruction, obtaining historical test results from a memory of the analyte testing system, processing the obtained historical test results into a chart form, and displaying a chart on the display. In this application, a user can be prompted with an accurate test result, and a chart is formed in combination with the historical test results, so that the user can understand a real change trend of the test result.

Claims

1. An information display method for analyte detection, characterized by, The method comprises the following steps: an instruction obtaining step of obtaining an operation instruction from a user, the operation instruction comprising a detection instruction and a statistics instruction; a first display step of, when the operation instruction is the detection instruction, starting a detection process of an analyte detection system and displaying a name of the process being executed on a display, and after the detection process is completed, displaying a detection result of the analyte and a preset reference range on the display; a second display step of, when the operation instruction is the statistics instruction, obtaining historical detection results from a memory of the analyte detection system, and processing the obtained historical detection results into a chart form and displaying on the display.

2. The information display method of the analysis object detection according to claim 1, characterized by, The detection result of the analyte is displayed on the display in a form of words and numbers, and the numbers are kept to two decimal places.

3. The information display method of the analysis object detection according to claim 1, characterized by, The chart form comprises a change curve of the historical detection results.

4. A method of detecting an analyte, characterized by, The method comprises the information display method for analyte detection according to any one of claims 1-3.

5. An information display system for analyte detection, characterized by The method comprises the following steps: an instruction obtaining module of obtaining an operation instruction from a user, the operation instruction comprising a detection instruction and a statistics instruction; a first display module of, when the operation instruction is the detection instruction, starting a detection process of an analyte detection system and displaying a name of the process being executed on a display, and after the detection process is completed, displaying a detection result of the analyte and a preset reference range on the display; a second display module of, when the operation instruction is the statistics instruction, obtaining historical detection results from a memory of the analyte detection system, and processing the obtained historical detection results into a chart form and displaying on the display.

6. The information display system for the analysis of an analyte according to claim 5, wherein The detection result of the analyte is displayed on the display in a form of words and numbers, and the numbers are kept to two decimal places.

7. The information display system for the analysis of an analyte according to claim 5, wherein The chart form comprises a change curve of the historical detection results.

8. A system for detecting an analyte, characterized by The system comprises the information display system for analyte detection according to any one of claims 5-7.

9. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by a processor to realize the steps of the analyte detection method according to any one of claims 1-3.

10. 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 a processor to realize the steps of the analyte detection method according to any one of claims 1-3.

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