Analysis method and system for blood glucose fluctuation caused by insulin leakage of nc membrane adsorption

Abstract

The present disclosure describes an analysis method and system for blood glucose fluctuation caused by insulin leakage adsorbed by an NC membrane, the method comprising collecting a leakage image, adsorbing leakage at a target position by a nitrocellulose membrane after a patient injects insulin, and taking a picture of the nitrocellulose membrane after adsorption of the leakage and a reference object to obtain a leakage image; analyzing the leakage image to determine a leakage area in the leakage image; determining a leakage area based on a resolution of the leakage image, the leakage area, and a reference object pattern; determining a leakage volume corresponding to the leakage area based on an adsorption capacity of the nitrocellulose membrane and the leakage area; and determining an impact of the leakage volume on blood glucose fluctuation of the patient based on an injection dose, the leakage volume, a first blood glucose level and a second blood glucose level of the patient, the first blood glucose level and the second blood glucose level being blood glucose indicators before and after the patient injects insulin, respectively. Thus, the leakage volume can be effectively, conveniently and non-invasively obtained to analyze the impact of the leakage volume on blood glucose fluctuation.

Description

Technical Field

[0001] This disclosure generally relates to the medical field of health-related information systems, and specifically to a method and system for analyzing blood glucose fluctuations caused by insulin leakage through NC membrane adsorption. Background Technology

[0002] Diabetes mellitus is a lifelong chronic endocrine disease characterized by insulin resistance and impaired pancreatic function, which can lead to a series of metabolic disorders, including high protein and high fat intake. The incidence of diabetes is increasing year by year and is showing a trend towards affecting younger people. It often induces a series of complications (such as diabetic foot and diabetic nephropathy), seriously affecting patients' quality of life and health, increasing the economic burden on society and families, and has become a global public health problem.

[0003] Currently, researchers have conducted numerous studies on diet, medication, and exercise to find ways to control blood sugar stability in patients. For example, in terms of medication, insulin injection has become an important way to lower blood sugar in the treatment of diabetes (e.g., type 2 diabetes). By injecting a certain dose of insulin, patients can control their blood sugar levels.

[0004] However, relevant medical clinical studies have shown that during insulin injection, problems such as drug residue at the outlet of the injection device or leakage of the drug solution can easily occur. This leads to a reduction in the dose of insulin injected into the patient's body, which can easily cause unstable blood sugar control and thus reduce the effectiveness of blood sugar control. However, there is no relevant research on how to quantitatively determine the leakage situation and whether there is a correlation between the amount of leakage and blood sugar fluctuations. Summary of the Invention

[0005] This disclosure is made in view of the above-mentioned situation, and its purpose is to provide an analytical method and system that can effectively, conveniently and non-invasively obtain leakage volume to analyze the effect of leakage volume on blood glucose fluctuations.

[0006] To this end, the first aspect of this disclosure provides a method for analyzing blood glucose fluctuations caused by insulin leakage adsorbed by a nitrocellulose membrane. The method includes acquiring leakage images, wherein after a patient injects insulin, a nitrocellulose membrane is used to adsorb leakage at a target location; after the leakage is adsorbed, the nitrocellulose membrane after adsorption and a reference object of known size are photographed to obtain leakage images; image analysis is performed on the leakage images to determine the region corresponding to the leakage in the leakage images and designated as the leakage region; the leakage area is determined based on the resolution of the leakage images, the leakage region, and the reference object pattern in the leakage images; the leakage volume corresponding to the leakage area is determined based on the insulin adsorption capacity of the nitrocellulose membrane and the leakage area; and the effect of the leakage volume on the patient's blood glucose fluctuations is determined based on the injection dose, the leakage volume, the patient's first blood glucose level, and the patient's second blood glucose level, wherein the first blood glucose level is the patient's blood glucose level before insulin injection, and the second blood glucose level is the patient's blood glucose level after insulin injection. In this approach, nitrocellulose membranes are used to effectively collect leakage volume, and leakage areas are intelligently and conveniently identified through image analysis. The leakage area corresponding to the leakage region is easily determined based on a reference object, and the leakage situation is quantitatively determined based on the leakage area and the adsorption capacity of the nitrocellulose membrane. Therefore, quantitative leakage information can be obtained effectively, conveniently, and non-invasively to analyze the impact of leakage volume on blood glucose fluctuations.

[0007] Additionally, in the analytical method according to the first aspect of this disclosure, optionally, the target location includes the insulin outlet of an injection device for injecting insulin and the skin surface surrounding the patient's injection site, wherein the injection device includes at least one of a needle-free injection device and a needle injection device. In this case, leakage at the target location can substantially encompass the medication that was not injected into the patient's body, thereby enabling subsequent effective assessment of the impact of leakage volume on the patient's blood glucose fluctuations and / or assessment of the effectiveness of the injection procedure.

[0008] Furthermore, in the analytical method involved in the first aspect of this disclosure, optionally, after the nitrocellulose membrane has been used to continuously adsorb the leakage for a first preset time to determine that the adsorption of the leakage is complete, an image of the leakage is acquired within a second preset time. The first preset time is determined by the adsorption capacity, and the second preset time is determined by the evaporation time of insulin on the nitrocellulose membrane. In this case, based on stricter time control, the leakage can be effectively adsorbed and the probability of leakage evaporation can be reduced, thereby obtaining a more accurate image of the leakage situation, which is beneficial to improving the accuracy of leakage area calculation.

[0009] Furthermore, in the analytical method involved in the first aspect of this disclosure, optionally, in determining the impact of the leakage volume on the patient's blood glucose fluctuations, multiple target data points for multiple insulin injections are obtained. Each of the multiple target data points includes the injection dose, the leakage volume, the first blood glucose level, and the second blood glucose level. Based on the multiple target data points, the correlation between changes in leakage volume and the patient's blood glucose fluctuations is determined. In this case, the impact of leakage volume on blood glucose fluctuations can be determined by comparing the leakage volume corresponding to multiple injections and the corresponding blood glucose indicators.

[0010] Furthermore, in the analysis method according to the first aspect of this disclosure, optionally, the number of pixels in the leakage region is determined based on the resolution of the leakage image, the pixel area is determined based on the reference pattern in the leakage image, and the leakage area is determined based on the number of pixels and the pixel area. Thus, the leakage area can be determined based on a reference object.

[0011] Furthermore, in the analytical method involved in the first aspect of this disclosure, optionally, the analytical method further includes determining the effectiveness of the injection behavior based on the leakage volume, thereby creating a first guidance message and outputting the first guidance message to the subject performing the injection behavior to standardize the injection behavior; and / or the analytical method further includes determining the correlation between the leakage volume and the patient's blood glucose fluctuations based on the leakage volume, thereby creating a second guidance message and outputting the second guidance message to the subject performing the injection behavior to standardize the injection behavior. In this case, determining the effectiveness of the injection behavior can reduce the risk of patients not injecting sufficient insulin, reduce the occurrence of adverse reactions, and improve the effectiveness of subsequent injection behaviors by timely standardizing the injection behavior, reducing the economic problems caused by insufficient insulin injection, which has important clinical value. In addition, determining the correlation between the leakage volume and the patient's blood glucose fluctuations can have practical guiding significance for the blood glucose fluctuations of patients with poor pancreatic function.

[0012] Furthermore, the analytical method disclosed in the first aspect may optionally include analyzing the leakage volume of different injection devices or different injection doses. In this case, the analysis can identify injection devices with relatively low leakage, thereby guiding the patient's injection behavior. Additionally, the relationship between injection dose and leakage volume can be obtained, thereby guiding the patient's injection dosage.

[0013] Additionally, in the analysis method according to the first aspect of this disclosure, the reference object may optionally be a scale. In this case, the pixel area can be conveniently determined by the scale markings on the scale.

[0014] Furthermore, in the analytical method according to the first aspect of this disclosure, optionally, the nitrocellulose membrane is white. In the image analysis, the leakage image is contrast-enhanced to obtain an enhanced image. The pixels in the enhanced image are classified to obtain a first type of pixel and a second type of pixel. The region corresponding to the pixel with the larger grayscale mean of the first type of pixel and the second type of pixel is designated as the background region, and the region corresponding to the other type of pixel is designated as the leakage region. In this case, the leakage region can be determined based on the color change after insulin is adsorbed by the nitrocellulose membrane, thereby obtaining a more accurate leakage region.

[0015] The second aspect of this disclosure provides an analysis system for blood glucose fluctuations caused by insulin leakage adsorbed by a nitrocellulose membrane, comprising: a processor; and a memory for storing instructions, the processor executing the instructions to perform the analysis method involved in the first aspect of this disclosure.

[0016] According to this disclosure, an analytical method and system can be provided that can effectively, conveniently and non-invasively obtain leakage volume to analyze the impact of leakage volume on blood glucose fluctuations. Attached Figure Description

[0017] This disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings, in which:

[0018] Figure 1 This is a schematic diagram illustrating an example of the analysis environment involved in this disclosure.

[0019] Figure 2 This is an exemplary flowchart illustrating an analytical method for blood glucose fluctuations as described in this disclosure.

[0020] Figure 3 This is an exemplary flowchart illustrating the acquisition of leakage images based on a reference object, as described in this disclosure.

[0021] Figure 4 This is a schematic diagram showing a leakage image using a reference object, as illustrated in the examples of this disclosure.

[0022] Figure 5 This is an exemplary flowchart illustrating the image analysis method involved in the examples of this disclosure.

[0023] Figure 6A This shows a histogram of the gray values ​​of the lightness component involved in the example of this disclosure before histogram equalization.

[0024] Figure 6B This is a histogram showing the gray values ​​of the lightness component involved in the example of this disclosure after histogram equalization.

[0025] Figure 7A This is a comparison diagram showing the leakage area with and without needles as described in the examples of this disclosure.

[0026] Figure 7B This is a comparison graph showing the leakage area with and without needles at different injection doses involved in the examples of this disclosure. Detailed Implementation

[0027] The preferred embodiments of this disclosure are described in detail below with reference to the accompanying drawings. In the following description, the same reference numerals are used for the same components, and repeated descriptions are omitted. Furthermore, the drawings are merely schematic diagrams, and the proportions of the components or the shapes of the components may differ from actual figures. It should be noted that the terms "comprising" and "having," and any variations thereof, in this disclosure, do not necessarily limit the process, method, system, product, or apparatus to the explicitly listed steps or units, but may include or have other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. All methods described in this disclosure may be performed in any suitable order unless otherwise indicated herein or clearly contradicted by the context.

[0028] As mentioned above, during insulin injection, problems such as drug residue remaining at the injection device's outlet (i.e., the drug outlet) or leakage can easily occur. The main reasons for this problem are: the outlet of the injection device is relatively narrow, resulting in a relatively long injection time; moreover, as the insulin dosage increases, the absorption rate of the drug at the injection site gradually slows down; and if the injection components (e.g., the needle of a needle-based injection device or the nozzle of a needleless injection device) are not removed in time after insulin injection, leakage may occur at the outlet when the external temperature changes (from cold to hot).

[0029] The inventors discovered through research that the leakage volume is closely related to both the injection device and the injection dosage. Furthermore, the leakage volume can affect blood glucose fluctuations. Therefore, a method and system for analyzing blood glucose fluctuations caused by insulin leakage from an NC membrane are proposed. The method and system for analyzing blood glucose fluctuations caused by insulin leakage from an NC membrane disclosed herein can effectively, conveniently, and non-invasively obtain the leakage volume. In addition, it can analyze the impact of the leakage volume on blood glucose fluctuations. The blood glucose fluctuation analysis method disclosed herein may sometimes be referred to simply as an analytical method, a guidance method, or an evaluation method, etc., and will be simply referred to as an analytical method below. The blood glucose fluctuation analysis system disclosed herein may sometimes be referred to simply as an analytical system, a guidance system, or an evaluation system, etc., and will be simply referred to as an analytical system below.

[0030] Figure 1This is a schematic diagram illustrating an example of the analysis environment involved in the examples of this disclosure. The scenarios described in the examples of this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided in this disclosure.

[0031] like Figure 1 As shown, the analysis environment may include an adsorbent material 10 for adsorbing insulin. The adsorbent material 10 can be any material capable of adsorbing insulin, a protein. After the patient has injected insulin, the adsorbent material 10 can be applied to the target site. Figure 1 The diagram shows an adsorbent material 10 attached to the skin surface around the patient's injection site to collect the leakage. The adsorbent material 10 with the collected leakage is placed near a reference object 20 located on the same plane as the adsorbent material 10, and the image of the adsorbent material 10 and the reference object 20 can be acquired by the computing device 30 as a leakage image.

[0032] Continue to refer to Figure 1 The analysis environment may include a computing device 30. The computing device 30 can implement an analysis method that acquires images of the adsorbent material 10 and the reference object 20 as leakage images, obtains the leakage volume based on the leakage images, and determines the impact of the leakage volume on the patient's blood glucose fluctuations. In some examples, the computing device 30 can also receive blood glucose data transmitted from a continuous glucose monitoring system 40. Thus, it is possible to determine the impact of the leakage volume on the patient's blood glucose fluctuations by combining the blood glucose data.

[0033] In some examples, the analysis method may be stored as computer program instructions on the computing device 30 and executed by the computing device 30. In some examples, the computing device 30 may include one or more processors and one or more memories. The processor can execute computer program instructions. The memory can be used to store computer program instructions. The processor of the computing device 30 can implement the analysis method by executing the computer program instructions on the memory.

[0034] In some examples, computing device 30 may include, but is not limited to, laptops, tablets, mobile phones, desktop computers, or virtual computers (virtual computers can refer to virtual machines simulated by software with complete hardware system functions and running in a completely isolated environment). In some examples, the number of computing devices 30 may be multiple. In some examples, some of the multiple computing devices 30 may be used to acquire leakage images, while others may be used to perform steps in the analysis method other than acquiring leakage images. For example, a computing device 30, which is a mobile phone, may be used to acquire leakage images and transmit them to another computing device 30, which is a desktop computer or a virtual computer, for processing.

[0035] Continue to refer to Figure 1The analysis environment may include a continuous glucose monitoring system 40. The continuous glucose monitoring system 40 can collect the patient's blood glucose data and transmit it to a computing device 30. Specifically, the sensors in the continuous glucose monitoring system 40 may be partially or completely placed under the patient's skin to collect the patient's blood glucose data, which can be transmitted to the computing device 30 via a receiver located outside the patient's body.

[0036] This disclosure discloses a method and system for analyzing blood glucose fluctuations caused by insulin leakage through an NC membrane. After insulin injection, an adsorbent material 10 adsorbs leakage at a target location related to the leakage volume. After adsorption is complete, image analysis is performed on the image of the adsorbent material 10 to determine the leakage area. The leakage area is then determined, and the leakage volume corresponding to that area is determined based on the adsorption capacity of the adsorbent material 10 and the leakage area. Finally, the impact of the leakage volume on the patient's blood glucose fluctuations is determined. In this scenario, the adsorbent material 10 effectively collects the leakage, and image analysis intelligently and conveniently identifies the leakage area. The leakage situation is then quantitatively determined based on the leakage area and the adsorption capacity of the adsorbent material 10. Therefore, a quantitative analysis of the leakage situation (i.e., the leakage volume) can be obtained effectively, conveniently, and non-invasively to analyze the impact of the leakage volume on blood glucose fluctuations.

[0037] The adsorbent material 10 disclosed herein may be loaded with an adsorbent. The adsorbent can be any substance capable of adsorbing insulin. Preferably, the adsorbent can be nitrocellulose. In some examples, the nitrocellulose-loaded adsorbent material 10 can be a nitrocellulose membrane (also referred to as an NC membrane). A nitrocellulose membrane is a microporous filter membrane capable of adsorbing proteins, and insulin is a protein hormone. In this case, the nitrocellulose membrane has a strong adsorption capacity for insulin and can effectively adsorb it. In some examples, the nitrocellulose membrane may have a substantially uniform pore size. In this case, insulin can be adsorbed uniformly, thereby reducing the difficulty of subsequent calculation of leakage volume based on leakage area.

[0038] In some examples, a nitrocellulose membrane can be used as a carrier for the C / T line in colloidal gold test paper. In this case, the adsorbent 10 can be colloidal gold test paper, and the leakage image can be specific to the colloidal gold test paper.

[0039] Furthermore, the readily available nitrocellulose membrane further enhances the ease of obtaining leakage data. Through research and verification, the inventors have demonstrated that the application of nitrocellulose membranes to the adsorption of insulin on the human skin surface is safe and non-invasive, and effectively adsorbs leakage generated after insulin injection (i.e., leakage at the target location described later).

[0040] The analytical methods described in this disclosure can quantitatively assess leakage and analyze the impact of leakage volume on blood glucose fluctuations. The following description uses a nitrocellulose membrane as the adsorbent material 10 as an example and does not constitute a limitation of this disclosure. Figure 2 This is an exemplary flowchart illustrating an analytical method for blood glucose fluctuations as described in this disclosure.

[0041] like Figure 2 As shown, in some examples, the analysis method may include acquiring a leak image (step S102), performing image analysis on the leak image to determine the region in the leak image that corresponds to the leak and designating it as the leak area (step S104), determining the leak area based on the leak area (step S106), determining the leak volume based on the leak area (step S108), and determining the impact of the leak volume on the patient's blood glucose fluctuations (step S110).

[0042] refer to Figure 2 In this embodiment, in step S102, a leakage image can be acquired.

[0043] In some examples, the leak image can be a color image or a grayscale image. Preferably, the leak image can be a color image. A color leak image can capture more details of the leak.

[0044] Furthermore, the acquisition device used to acquire leakage images can be any device with imaging capabilities. For example, the acquisition device can include, but is not limited to, a mobile phone, a computer, or a camera. In some examples, the acquisition device can be the aforementioned computing device 30. Preferably, the acquisition device can be a mobile phone. In this case, because mobile phones are easy to operate and readily accepted by patients or those performing injections, the convenience of obtaining leakage volume can be further improved. In some examples, the mobile phone can integrate an application for implementing the analytical methods involved in the examples of this disclosure to acquire leakage images and obtain the leakage volume based on the leakage images. In some examples, the application integrated on the mobile phone can also be used to determine the impact of leakage volume on the patient's blood glucose fluctuations.

[0045] In this embodiment, the leakage image can be an image taken after the nitrocellulose membrane has absorbed the leakage following insulin injection. Specifically, after insulin injection, a nitrocellulose membrane can be used to absorb leakage at a target location related to the leakage volume. After the leakage has been absorbed, an image of the nitrocellulose membrane after absorption can be acquired as the leakage image.

[0046] In some examples, after the nitrocellulose membrane has been continuously adsorbed for a first preset time to determine that the leakage has been completely adsorbed, a leakage image can be acquired within a second preset time. Furthermore, the first preset time can be determined by the adsorption capacity of the nitrocellulose membrane. The second preset time can be determined by the evaporation time of insulin relative to the nitrocellulose membrane. In this case, based on stricter time control, the leakage can be effectively adsorbed and the probability of leakage evaporation can be reduced, thereby obtaining a more accurate leakage image representing the leakage situation, which is beneficial to improving the accuracy of leakage area calculation.

[0047] In some examples, the first preset time can be a first fixed value (e.g., an empirical value) related to the adsorption capacity of the nitrocellulose membrane. Alternatively, the first fixed value can be a range. In some examples, the first preset time can be obtained through multiple tests of nitrocellulose membrane adsorption of leakage. In other examples, the first preset time can be estimated based on the adsorption capacity of the nitrocellulose membrane.

[0048] In some examples, the second preset time can be a second fixed value (e.g., an empirical value) related to the evaporation time of insulin adsorbed on the nitrocellulose membrane. Alternatively, the second fixed value can be a range. In some examples, the second preset time can be obtained through multiple trials validating the evaporation time of insulin adsorbed on the nitrocellulose membrane. In some examples, the second preset time can be less than 30 seconds. In some examples, the requirement for the second preset time can be reduced by improving the stability of insulin adsorption on the nitrocellulose membrane.

[0049] As described above, an image of the nitrocellulose membrane after the leakage has been adsorbed can be acquired as a leakage image. In some examples, an image of the nitrocellulose membrane after the leakage has been adsorbed and the image corresponding to the reference object 20 can be acquired as a leakage image. That is, the leakage image can simultaneously include the leakage pattern (i.e., the pattern for the leakage area) and the pattern of the reference object 20 (which can also be simply referred to as the reference pattern). In this case, the leakage area can be easily determined based on the reference object 20. In addition, since the reference object 20 is present, the consistency requirements for acquisition conditions (e.g., the resolution, focal length, and shooting distance of the acquisition device) are lower, which improves the convenience of acquiring leakage images.

[0050] In some examples, the size of the reference object 20 may be known. That is, the reference object 20 can be any object of known size. For example, the reference object 20 may include, but is not limited to, a coin and a ruler. Preferably, the reference object 20 can be a ruler. In this case, the area of ​​a pixel (i.e., the area of ​​a single pixel) can be conveniently determined by the scale on the ruler.

[0051] In some examples, during data acquisition, images of the nitrocellulose membrane after leakage can be taken against a reference object 20 of known size to obtain images of the leakage. In some examples, the nitrocellulose membrane and the reference object 20 can be located on the same plane during image acquisition. In this case, the distances of the nitrocellulose membrane and the reference object 20 from the data acquisition device are approximately the same, which reduces the difficulty of subsequent calculation of the leakage area.

[0052] Figure 3 This is an exemplary flowchart illustrating the acquisition of leakage images based on reference 20, as described in this disclosure example. Figure 4 This is a schematic diagram showing a leakage image using reference 20 as described in the example of this disclosure.

[0053] Specifically, Figure 3 The process of acquiring leakage images based on reference object 20 is shown, which may include the following steps:

[0054] Step S202: The patient injects insulin.

[0055] Step S204: After insulin injection, a nitrocellulose membrane can be used to collect any leakage at the target site. For example, a nitrocellulose membrane can be applied to the target site to collect the leakage.

[0056] Step S206: The nitrocellulose membrane with collected leakage can be placed near a reference object 20 of known size, located on the same plane as the nitrocellulose membrane. The vicinity of the reference object 20 can be, for example, above, below, to the left, or to the right of the reference object 20.

[0057] Step S208: The nitrocellulose membrane after leakage can be photographed along with the reference object 20 to obtain images of the leakage. As an example, Figure 4 This is a schematic diagram showing a leakage image with reference object 20 as a scale, adsorbent material 10 as a nitrocellulose membrane, and the nitrocellulose membrane placed above the scale. Figure 4 The diagram shows a nitrocellulose membrane pattern 11 representing a nitrocellulose membrane, a leakage pattern 111 within the nitrocellulose membrane pattern 11, a scale pattern 21 representing a scale, and a background cloth pattern 50 representing a background cloth. The background cloth can be used to suppress noise from the shooting environment, which is beneficial for distinguishing the areas corresponding to the adsorbent material 10 and the reference object 20 from the leakage image.

[0058] In some examples, a gap may exist between the leakage pattern and the reference pattern in the leakage image. In this case, the mutual influence between the leakage pattern and the reference pattern can be reduced, and it is beneficial to obtain images of the corresponding areas separately for image analysis.

[0059] However, the examples disclosed herein are not limited to this. In other examples, reference object 20 may not be used (that is, the reference object pattern may not be included in the leakage image), which will be described in detail later in the section on determining the leakage area.

[0060] As mentioned above, nitrocellulose membranes can be used to absorb leakage at the target site. In some examples, the target site can be any one or more locations associated with the leakage. In some examples, the target site can include the insulin outlet of an insulin injection device and the skin surface around the patient's injection site. In this case, the leakage at the target site can substantially encompass the medication that was not injected into the patient, thus enabling subsequent effective assessment of the impact of the leakage volume on the patient's blood glucose fluctuations and / or the effectiveness of the injection procedure.

[0061] In some examples, the leakage at different locations within the target area can be distributed across different parts of the nitrocellulose membrane. In such cases, leakage at different locations can be distinguished within a single leak image acquisition, allowing for the simultaneous acquisition of leakage amounts at each location and the total leakage amount across multiple locations.

[0062] In other examples, the target location can be either the insulin outlet of the injection device or a location on the skin surface surrounding the patient's injection site. This allows for the acquisition of leakage volume at each location. The specific target location can be selected based on the intended use of the leakage measurement.

[0063] In some examples, the injection device may include at least one of a needle-free injection device (also known as a jet injection device) and a needle-based injection device. In this case, the leakage rate corresponding to different injection devices can be obtained, thus helping the patient choose the appropriate device. A needle-free injection device can utilize a specific device to generate instantaneous high pressure, which propels the medication through a very fine nozzle onto the skin surface, forming micropores and allowing the medication to diffuse directly into the subcutaneous tissue. Furthermore, for needle-free injection devices, the insulin outlet can be a nozzle. For needle-based injection devices, the insulin outlet can be a needle tip.

[0064] Furthermore, the nitrocellulose membrane can be any color that creates a color difference between the areas where insulin leakage occurs and the areas where leakage does not occur after insulin adsorption. Preferably, the nitrocellulose membrane can be white. In this case, the white nitrocellulose membrane will appear darker after insulin adsorption, which is beneficial for distinguishing leakage areas in subsequent image analysis.

[0065] Furthermore, for nitrocellulose membranes that form color differences after adsorbing insulin, there can be a color difference between the non-leaking areas (which can be referred to as the background area) and the leaking areas (i.e., the leaking areas) in the leakage image. That is, the grayscale values ​​of pixels in the background area and the leaking area in the leakage image can belong to different grayscale ranges. In this case, distinguishing the leaking areas by utilizing the color change after the nitrocellulose membrane adsorbs insulin is more convenient than methods that require reagent reactions to generate color intensity.

[0066] Return to reference Figure 2 In this embodiment, in step S104, image analysis can be performed on the leakage image to determine the area in the leakage image that corresponds to the leakage and to identify the leakage area.

[0067] In some examples, during image analysis, leak images can be segmented to identify the leaking region. For instance, image segmentation algorithms can be used to segment leak images to extract the leaking region. Image segmentation algorithms can include, but are not limited to, active contouring, Grabcut, or thresholding methods.

[0068] In addition to the image segmentation algorithms mentioned above, this disclosure also provides an image analysis method. This image analysis method can perform contrast enhancement on a leak image to obtain an enhanced image, and classify the pixels in the enhanced image to determine the leak region. Figure 5 This is an exemplary flowchart illustrating the image analysis method involved in the examples of this disclosure. Figure 6A This shows a histogram of the gray values ​​of the lightness component involved in the example of this disclosure before histogram equalization. Figure 6B This is a histogram showing the gray values ​​of the lightness component involved in the example of this disclosure after histogram equalization.

[0069] refer to Figure 5 The image analysis method may include contrast enhancement of the leakage image to obtain an enhanced image (step S302). This improves the contrast between the leakage area and other areas in the enhanced image.

[0070] In some examples, in contrast enhancement, the color space of the leaked image can be converted to a target color space with a lightness component (also known as a brightness component), the gray values ​​of the lightness component in the target color space can be histogram equalized, and the gray values ​​of the lightness component can be converted back to the RGB (Red, Green, Blue) space via the histogram equalized target color space to obtain the enhanced image.

[0071] As an example, Figure 6A and Figure 6BThe histograms of the lightness component's grayscale values ​​before and after histogram equalization are shown. It can be seen that before histogram equalization, Figure 6A Two peaks are clearly visible (corresponding to the background area and the leaking area), and the grayscale distribution is within a narrow range (meaning the contrast of the leaking image is not high). After histogram equalization, Figure 6B The more uniform grayscale distribution increases the range of grayscale value differences between pixels, thereby enhancing the contrast in the image.

[0072] In some examples, for the gray values ​​of the brightness component, the cumulative distribution function corresponding to histogram equalization can satisfy the formula:

[0073]

[0074] Where k can represent the gray value of the lightness component, and n k S can represent the number of pixels with a gray value of k in the brightness component of the leakage image, N can represent the total number of pixels in the leakage image, and S can represent the number of pixels with a gray value of k in the brightness component of the leakage image. k It can be represented as the gray value of the lightness component of k after histogram equalization. In some examples, for an 8-bit grayscale image, k∈[0,255].

[0075] In some examples, the target color space may also include hue and saturation components. For instance, the target color space can be an HSV (Hue, Saturation, Value) space or an HSI (Hue, Saturation, Intensity) space. Thus, enhanced images can be generated using either the HSV or HSI color space.

[0076] In some examples, for leak images that contain other patterns (e.g., reference patterns or background fabric patterns), an image of the leaking area within the leak image can be contrast-enhanced to obtain an enhanced image. For example, an image of the leaking area can be cropped from the leak image and its contrast enhanced to obtain an enhanced image.

[0077] Return to reference Figure 5 The image analysis method may also include classifying pixels in the enhanced image to determine the leakage area (step S304).

[0078] In some examples, pixels in the enhanced image can be classified to obtain two categories of pixels, and pixels of the target category can be selected from the two categories of pixels. The leakage area can be determined based on the region corresponding to the pixels of the target category.

[0079] As mentioned above, after the white nitrocellulose membrane absorbs insulin, its color darkens, and it appears as a lower grayscale value in the enhanced image. Therefore, areas with lower grayscale values ​​in the enhanced image can be considered leakage areas. Specifically, the pixels in the enhanced image can be classified into a first category and a second category. The area corresponding to the pixel with the higher grayscale mean of the first and second categories is designated as the background area, and the area corresponding to the pixel with the higher grayscale mean is designated as the leakage area. In this way, the leakage area can be determined based on the color change after the nitrocellulose membrane absorbs insulin, thus obtaining a more accurate leakage area.

[0080] In some examples, the classification of obtaining the first and second class of pixels described above can be achieved using K-means clustering. Specifically, in K-means clustering, K can be set to 2, the pixels in the enhanced image can be considered as samples, and the grayscale values ​​of the three channels (or three spaces) in the RGB space of each sample can be used as features. Unsupervised clustering is then performed on the samples to obtain the first and second class of pixels. Thus, it is possible to obtain two categories of pixels based on K-means clustering.

[0081] In some examples, image analysis methods can further adjust the leakage region using convex hull fitting. This reduces the influence of isolated pixels, improving the accuracy of calculating the leakage region. Additionally, it makes the leakage region as regular as possible, facilitating observation of the leakage. Specifically, connectivity analysis can be performed on the leakage region to determine at least one connected component, the convex hull of each connected component can be obtained, and the leakage region can be adjusted to fit the convex hull corresponding to at least one connected component. In some examples, the convex hull can be the smallest convex polygon of each connected component.

[0082] Furthermore, each connectivity component can include multiple interconnected pixels. In some examples, connectivity analysis can be eight-neighbor connectivity analysis. Specifically, within the region corresponding to a pixel of the target category, the connectivity between that pixel and its eight neighboring pixels can be analyzed to identify multiple interconnected pixels. In other examples, connectivity analysis can also be four-neighbor connectivity analysis.

[0083] In some examples, the leakage region can be filtered using the Laplacian operator before convex hull fitting to adjust the edges of the leakage region. This reduces the computational cost of subsequent convex hull fitting.

[0084] Return to reference Figure 2 In this embodiment, in step S106, the leakage area (that is, the actual area of ​​insulin adsorbed by the nitrocellulose membrane) can be determined based on the leakage area.

[0085] In some examples, the leakage area can be determined based on the resolution of the leakage image and the leakage region. The resolution of the leakage image can be used to determine the number of pixels in the corresponding region of the leakage image.

[0086] As described above, an image of the nitrocellulose membrane after the leakage has been adsorbed and the corresponding image of the reference object 20 can be acquired as a leakage image. In some examples, the leakage area can be determined based on the resolution of the leakage image, the leakage region, and the reference object pattern in the leakage image.

[0087] Specifically, for a reference object 20 with known dimensions, the number of pixels in the leakage region can be determined based on the resolution of the leakage image, the area of ​​each pixel (i.e., the area of ​​a single pixel) can be determined based on the pattern of the reference object in the leakage image, and the leakage area can be determined based on the number and area of ​​each pixel in the leakage region. Thus, the leakage area can be determined based on the reference object 20.

[0088] Taking reference object 20 as a ruler as an example, since the ruler has scales, edge detection can be performed on the portion of the leakage image including the ruler pattern using image processing algorithms to obtain areas with clear scales on the ruler pattern. This allows us to obtain the scale range corresponding to that area. Based on the number of pixels within and between this scale range, we can obtain the side length of each pixel. Based on the side length of each pixel, we can obtain its area. Therefore, based on the number of pixels and the area of ​​each pixel in the leakage area, we can determine the leakage area. In some examples, the side length of each pixel can be obtained by dividing the scale range by the number of pixels within that scale range.

[0089] As mentioned above, in other examples, reference 20 may not be used. For example, acquisition conditions can be fixed to ensure that the pixel area of ​​each pixel in all leakage images is roughly fixed, thus enabling the leakage region to be converted into a leakage area based on the known pixel area and number of pixels. As another example, the pixel interval (the actual size represented by the distance between two pixels in the leakage image) can be obtained from a DICOM (Digital Imaging and Communications in Medicine) format leakage image. Using the pixel interval as the side length of a pixel, the pixel area can be obtained based on the pixel's side length, and then the leakage area can be determined based on the number and area of ​​pixels in the leakage region.

[0090] Continue to refer to Figure 2 In this embodiment, in step S108, the leakage amount can be determined based on the leakage area.

[0091] In some examples, the leakage rate corresponding to the leakage area can be determined based on the adsorption capacity of the nitrocellulose membrane for insulin (hereinafter referred to as adsorption capacity) and the leakage area. Generally, the particle size, pore size, and porosity of the nitrocellulose membrane can affect the adsorption capacity. In some examples, the adsorption capacity can be a fixed value. In some examples, the adsorption capacity can be obtained experimentally. In some examples, the adsorption capacity can be a known parameter of the nitrocellulose membrane. In some examples, the adsorption capacity can be expressed using the dose of insulin adsorbed per unit area of ​​the nitrocellulose membrane.

[0092] In some examples, the adsorption capacity can be adjusted based on the pixel values ​​of the pixels in the leakage area. For instance, a weight can be determined based on the grayscale value of the saturation component, which can then be used to adjust the adsorption capacity. In this case, adjusting the adsorption capacity by incorporating the pixel values ​​of the pixels takes into account subtle differences in insulin dosage that may exist in the leakage area, thus enabling a more accurate determination of the leakage volume.

[0093] In some examples, a formula can be established based on adsorption capacity to convert leakage area to leakage volume, thus automatically converting the leakage area to leakage volume. This allows for convenient conversion of leakage area to leakage volume. In other examples, a formula can be established based on adsorption capacity and the pixel values ​​of pixels within the leakage area to automatically convert the leakage area to leakage volume.

[0094] Continue to refer to Figure 2 In this embodiment, in step S110, the impact of the leakage volume on the patient's blood glucose fluctuations can be determined. In other examples, the impact of the leakage on the patient's blood glucose fluctuations can also be determined based on the leakage area or leakage area.

[0095] In this embodiment, blood glucose fluctuations can also be referred to as blood glucose variability. Because diabetic patients have a lower ability to control their blood glucose, blood glucose fluctuations often exceed the correct range. During insulin injection, if the dose of insulin injected into the patient's body is insufficient, it can also easily cause unstable blood glucose control and result in blood glucose fluctuations.

[0096] In some examples, step S110 can analyze changes in blood glucose levels before and after insulin injection to determine the impact of leakage on blood glucose fluctuations. Furthermore, the inventors considered that as the insulin dosage increases, the absorption rate of the medication at the injection site gradually slows down, thus affecting leakage. In other words, leakage can be related to the dosage of insulin injected each time. In some examples, the impact of the injection dosage on blood glucose fluctuations can be analyzed comprehensively. This allows for a more comprehensive analysis of the impact on blood glucose fluctuations.

[0097] Specifically, the impact of leakage on a patient's blood glucose fluctuations can be determined based on the injection dose, leakage volume, and the patient's blood glucose levels before and after insulin injection. In this case, it is possible to obtain the effect of leakage volume on blood glucose fluctuations at the corresponding injection dose (i.e., insulin dosage), thereby enabling timely intervention (e.g., adjusting the dose or guiding standardized injection behavior).

[0098] In addition, a patient's blood glucose levels before and after insulin injection can be divided into a first blood glucose level and a second blood glucose level. The first blood glucose level is the blood glucose level before insulin injection, and the second blood glucose level is the blood glucose level after insulin injection. Blood glucose level can also be referred to as blood glucose concentration.

[0099] In some examples, a patient's blood glucose levels (i.e., the first blood glucose level and / or the second blood glucose level) can be determined by a finger-prick blood collection device and / or a continuous glucose monitoring system 40. For example, a patient can obtain their blood glucose levels over time by wearing the continuous glucose monitoring system 40. In this case, the patient's blood glucose data can be obtained conveniently and promptly, allowing for better analysis of the impact of leakage on blood glucose fluctuations.

[0100] In some examples, in determining the impact of leakage volume on a patient's blood glucose fluctuations, multiple target data points (i.e., multiple target data points corresponding to multiple insulin injections) can be obtained for each insulin injection. The correlation between changes in leakage volume and the patient's blood glucose fluctuations can then be determined based on these multiple target data points. The target data points for each insulin injection (i.e., each target data point) can include the injection dose, leakage volume, first blood glucose level, and second blood glucose level. In this case, the impact of leakage volume on blood glucose fluctuations can be determined by comparing the leakage volume corresponding to multiple injections with the corresponding blood glucose indicators.

[0101] In some examples, determining the impact of leakage on a patient's blood glucose fluctuations can involve acquiring multiple target data points corresponding to several days of insulin injections. Based on these target data points, multi-day blood glucose levels are obtained, and the impact of leakage on blood glucose fluctuations is analyzed based on daily injection doses, leakage volume, and blood glucose levels. In this case, the impact of leakage volume on multi-day blood glucose fluctuations at a given injection dose can be obtained. That is, the sustained impact of leakage volume on blood glucose fluctuations can be determined. In some examples, continuous monitoring for a preset number of days (e.g., 7 days) can be used to obtain multi-day blood glucose levels.

[0102] In some examples, multi-day blood glucose indicators may include, but are not limited to, standard deviation (SD), mean of daily differences (MODD), mean amplitude of glycemic excursion (MAGE), and time within the target range for blood glucose (TIR).

[0103] In some examples, adverse reactions experienced by the patient can also be recorded. This allows for a comprehensive analysis of the impact of leakage on blood glucose fluctuations based on the severity of adverse reactions. In some examples, adverse reactions may include, but are not limited to, injection site pain, injection site allergies, or hypoglycemia.

[0104] As described above, the target site can include the insulin outlet of the injection device used to inject insulin and the skin surface around the patient's injection site. In this case, leakage at the target site can substantially encompass the medication that was not injected into the patient's body, thereby enabling effective subsequent assessment of the effectiveness of the injection.

[0105] In some examples, the analysis method may also include standardizing injection procedures based on leakage volume. In some examples, the effectiveness of the injection procedure can be determined based on leakage volume, and a corresponding guidance message can be created to standardize the injection procedure. Specifically, the effectiveness of the injection procedure can be determined based on leakage volume, and a first guidance message can be created and output to the subject performing the injection to standardize the injection procedure. In this case, the risk of patients not injecting sufficient insulin can be reduced, the occurrence of adverse reactions can be reduced, and by timely standardizing injection procedures, the effectiveness of subsequent injections can be improved, reducing the economic problems caused by insufficient insulin injection, which has significant clinical value.

[0106] For example, after obtaining the amount of leakage at the insulin outlet of the injection device and the skin surface around the injection site, it is possible to determine whether the patient's injection was effective based on the amount of leakage. If it is determined to be an ineffective injection, a first guidance message is created and the person who performed the injection is notified. The person can receive the first guidance message and regulate the injection operation behavior based on the first guidance message.

[0107] In some examples, the correlation between leakage volume and a patient's blood glucose fluctuations can be determined based on the leakage volume, and corresponding guidance messages can be created to regulate injection behavior. Specifically, the correlation between leakage volume and a patient's blood glucose fluctuations can be determined based on the leakage volume, and a second guidance message can be created and output to the person performing the injection behavior (i.e., injecting insulin) to regulate the injection behavior. In this case, it can have practical guidance significance for blood glucose fluctuations in patients with poor pancreatic function (e.g., patients with type 1 diabetes).

[0108] For example, it can be determined whether the leakage exceeds a threshold that affects the patient's blood glucose fluctuations. If it does, a second guidance message can be created and the person administering the injection can be notified. The person can receive the second guidance message and regulate the injection behavior based on it (e.g., the person can determine whether to administer additional insulin to stabilize the patient's blood glucose based on the second guidance message).

[0109] Figure 7A This is a comparison diagram showing the leakage area with and without needles as described in the examples of this disclosure. Figure 7B This is a comparison graph showing the leakage area with and without needles at different injection doses involved in the examples of this disclosure.

[0110] In some examples, the analysis method may also include analyzing the leakage volume of different injection devices. In this case, the analysis can identify injection devices with relatively low leakage, thereby guiding the patient's injection behavior. Specifically, the analysis can obtain the leakage volume corresponding to different injection devices and compare the leakage volumes of different injection devices. In some examples, the leakage volume of different injection devices can be analyzed based on the same injection dose. As an example, the leakage volume is represented by the leakage area. Figure 7A The diagram shows a comparison of leakage areas between needle-based (i.e., needle-based injection devices) and needle-free (i.e., needle-free injection devices) devices at the same injection dose. A value less than 0.05 indicates a difference between needle-based and needle-free devices.

[0111] In some examples, the analysis method may also include analyzing leakage volume for different injection doses. In this case, the relationship between injection dose and leakage volume can be obtained, thereby guiding the patient's injection dosage. This can improve the efficacy of insulin injections. Specifically, the analysis can obtain the leakage volume corresponding to different injection doses of insulin and compare the leakage volumes corresponding to different injection doses. In some examples, the leakage volume for different injection doses can be analyzed under the condition of the same injection device. As an example, the leakage volume is represented by the leakage area. Figure 7B It shows different injection doses (e.g.) Figure 7B The comparison of leakage area with and without needles at 10 units, 20 units and 30 units is shown in the figure. If P is less than 0.05, it can be said that there is a difference between with and without needles. u represents the unit of insulin dose.

[0112] This disclosure also relates to a blood glucose fluctuation analysis system, comprising: a processor and a memory. The memory is used to store instructions, which the processor executes to perform one or more steps of the analysis method described above.

[0113] This disclosure also relates to an electronic device that may include at least one processing circuit. The at least one processing circuit is configured to perform one or more steps of the analysis method or image analysis method described above.

[0114] This disclosure also relates to a computer-readable storage medium that may store at least one instruction, which, when executed by a processor, implements one or more steps of the analysis method or image analysis method described above.

[0115] The present disclosure discloses an analysis method, system, device, and medium for analyzing blood glucose fluctuations caused by insulin leakage adsorbed by a nitrocellulose membrane. After insulin injection, the nitrocellulose membrane adsorbs leakage at a target location related to the leakage volume. After adsorption is complete, image analysis is performed on the image of the nitrocellulose membrane after adsorption and a reference object 20 of known size to determine the leakage area. The leakage area is determined based on the leakage area and the reference object pattern. Furthermore, the leakage volume corresponding to the leakage area is determined based on the insulin adsorption capacity of the nitrocellulose membrane and the leakage area. Finally, the impact of the leakage volume on the patient's blood glucose fluctuations is determined based on the injection dose, leakage volume, and changes in blood glucose levels before and after insulin injection. In this scenario, the nitrocellulose membrane effectively collects the leakage volume, and the leakage area is intelligently and conveniently determined through image analysis. The leakage area corresponding to the leakage region is conveniently determined based on the reference object 20, and the leakage situation is quantitatively determined based on the leakage area and the adsorption capacity of the nitrocellulose membrane. Therefore, quantitative leakage information can be obtained effectively, conveniently, and non-invasively to analyze the impact of leakage volume on blood glucose fluctuations.

[0116] Furthermore, the scheme disclosed herein, which uses nitrocellulose membrane as a tool to measure the leakage of insulin injected by the injection device, is conducive to the formation of a standardized insulin leakage assessment process, laying a solid foundation for clinical application and bringing benefits to patients with good blood glucose management.

[0117] While the present invention has been specifically described above in conjunction with the accompanying drawings and embodiments, it is to be understood that the above description does not limit the present invention in any way. Those skilled in the art can make modifications and variations to the present invention as needed without departing from the essential spirit and scope of the invention, and all such modifications and variations fall within the scope of the present invention.

Claims

1. A method for analyzing blood glucose fluctuations caused by insulin leakage adsorbed by a nitrocellulose membrane, characterized in that, include: Leakage images are acquired, wherein after insulin injection, a nitrocellulose membrane is used to adsorb the leakage at a target location. After the leakage is adsorbed, images of the nitrocellulose membrane and a reference object of known size are captured to obtain the leakage image. Image analysis is performed on the leakage image to determine the region corresponding to the leakage in the image, which is then designated as the leakage area. The leakage area is determined based on the resolution of the leakage image, the leakage area, and the reference object pattern in the leakage image. The leakage volume corresponding to the leakage area is determined based on the insulin adsorption capacity of the nitrocellulose membrane and the leakage area. Furthermore, the impact of the leakage volume on the patient's blood glucose fluctuations is determined based on the injection dose, the leakage volume, the patient's first blood glucose level, and the patient's second blood glucose level, wherein the first blood glucose level is the patient's blood glucose level before insulin injection, and the second blood glucose level is the patient's blood glucose level after insulin injection. The target locations include the insulin outlet of the injection device used to inject insulin and the skin surface surrounding the patient's injection site. The nitrocellulose membrane is white. In the image analysis, the leakage image is contrast-enhanced to obtain an enhanced image. The pixels in the enhanced image are classified to obtain a first type of pixel and a second type of pixel. The area corresponding to the pixel with the larger grayscale mean of the first type of pixel and the second type of pixel is taken as the background area, and the area corresponding to the other type of pixel is taken as the leakage area.

2. The analytical method according to claim 1, characterized in that: The injection device includes at least one of a needle-free injection device and a needle-based injection device.

3. The analytical method according to claim 1, characterized in that: After the nitrocellulose membrane is used to continuously adsorb for a first preset time to determine that the adsorption of the leakage is complete, the leakage image is acquired within a second preset time. The first preset time is determined by the adsorption capacity, and the second preset time is determined by the evaporation time of insulin on the nitrocellulose membrane.

4. The analytical method according to claim 2, characterized in that: In determining the impact of the leakage volume on the patient's blood glucose fluctuations, multiple target data points are acquired for multiple insulin injections by the patient. Each of the multiple target data points includes the injection dose, the leakage volume, the first blood glucose level, and the second blood glucose level. Based on the multiple target data points, the correlation between the leakage volume change and the patient's blood glucose fluctuations is determined.

5. The analytical method according to claim 1, characterized in that: The number of pixels in the leakage region is determined based on the resolution of the leakage image, the area of ​​each pixel is determined based on the reference pattern in the leakage image, and the leakage area is determined based on the number of pixels and the area of ​​each pixel.

6. The analytical method according to claim 2, characterized in that: The analysis method further includes determining the effectiveness of the injection behavior based on the leakage volume, thereby creating a first guidance message and outputting the first guidance message to the object performing the injection behavior to regulate the injection behavior; and / or The analysis method further includes determining the correlation between the leakage volume and the patient's blood glucose fluctuations based on the leakage volume, thereby creating a second guidance message and outputting the second guidance message to the subject performing the injection to regulate the injection behavior.

7. The analytical method according to claim 2, characterized in that: It also includes analyzing the leakage volume of different injection devices or different injection doses.

8. The analytical method according to claim 1, characterized in that: The reference point is a ruler.

9. An analytical system for the detection of blood glucose excursions due to insulin leaks adsorbed to a nitrocellulose membrane, comprising: processor; And a memory for storing instructions, which the processor executes to perform the analysis method according to any one of claims 1 to 8.

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