Diabetic neuropathy management method based on nylon wire detection and related equipment

Abstract

The invention discloses a diabetic neuropathy management method based on nylon wire detection and related equipment. The method comprises the following steps: acquiring a feeling result input by a diabetic patient in response to 0.4 g nylon wire examination so as to obtain nylon wire examination data of the patient; obtaining a feeling result of the diabetic patient in response to temperature detection and vibration detection so as to obtain quantitative feeling examination data; inquiring the doctor-seeing historical information and the medicine purchasing historical information of the patient, and evaluating the diabetic neuropathy degree of the patient in combination with the doctor-seeing historical information, the medicine purchasing historical information, the nylon wire examination data and the quantitative sensory examination data. The problems that the sensitivity of detecting and screening DPN by nylon wires is low, and the nylon wires are not suitable for early diagnosis of DPN can be solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart medical care, and more specifically, the present invention relates to a diabetic neuropathy management method based on nylon filament detection and related equipment. Background Art

[0002] DPN (Diabetic Peripheral Neuropathy) is one of the common complications of diabetes. Studies have shown that the incidence of DPN in China is estimated to be 46.6%, but up to 50% of patients may not have any symptoms, and are a high-risk group for foot ulcers, trauma, etc. Therefore, early diagnosis and intervention are of great significance for slowing the progression of DPN and preventing complications. Among the many clinical examination methods, NCV (nerve conduction velocity) is currently considered to be the "gold standard" for diagnosing DPN due to its sensitivity, objectivity and reliability. However, because it requires specialists to operate, it is time-consuming, laborious and costly, which limits its popularity in primary medical units. Moreover, because it cannot examine small unmyelinated nerve fibers, it cannot be used as a routine examination method for early diagnosis of DPN. In recent years, Semmes-Weinstein (nylon thread) examination has gradually become an important tool for early clinical screening of DPN due to its simplicity, economy and non-invasiveness.

[0003] The nylon thread test evaluates nerve function by measuring the patient's sensory response to different pressures. It is easy to operate, does not require special equipment, and is suitable for use in various medical environments. Graded nylon threads are composed of nylon threads of different diameters, arranged from thin to thick. The doctor will touch the patient's foot skin with these nylon threads in a certain order. The patient needs to feedback whether he feels the contact of the nylon thread to assess the sensitivity of his sensory nerves. This examination method is simple, non-invasive and low-cost, and is very suitable for promotion and use in primary medical institutions. In China, nylon threads with a force level of 5.07 / 10g are mostly used for DPN screening. The results show that the sensitivity of 10g nylon thread examination for DPN screening is low and is not suitable for the early diagnosis of DPN. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In order to solve the problem that nylon thread examination has low sensitivity in screening DPN and is not suitable for early diagnosis of DPN, in a first aspect, the present invention proposes a diabetic neuropathy management method based on nylon thread detection, the method comprising:

[0006] Collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient;

[0007] Obtaining the feeling results of the diabetic patient in response to temperature detection and vibration detection to obtain quantitative sensory examination data;

[0008] The patient's medical history information and drug purchase history information are queried to evaluate the patient's diabetic neuropathy degree in combination with the medical history information, drug purchase history information, nylon thread examination data and the quantitative sensory examination data.

[0009] Optionally, also include:

[0010] Collecting foot image data of the diabetic patient, and analyzing the foot image data to extract skin risk feature data;

[0011] The degree of diabetic neuropathy of the patient is assessed by combining the medical history information, the drug purchase history information, the nylon thread inspection data, the quantitative sensory inspection data and the skin risk characteristic data.

[0012] Optionally, also include:

[0013] Collecting foot image data of the diabetic patient, analyzing the foot image data, and calculating ideal nylon thread inspection point positions;

[0014] The ideal nylon thread checkpoint position is displayed in the foot image of the diabetic patient.

[0015] Optionally, the foot image data is a video frame based on the collected foot video data of the diabetic patient, and further includes:

[0016] Real-time analysis of the contact point locations of nylon threads in diabetic patients’ foot video data;

[0017] When it is detected that the contact point reaches within the preset area of ​​the ideal nylon thread inspection point, a tactile confirmation question message is generated.

[0018] Optionally, also include:

[0019] The evaluation result is sent to a medical terminal associated with the diabetic patient.

[0020] Optionally, sending the evaluation result to a medical terminal associated with the diabetic patient includes:

[0021] When the evaluation result indicates that the diabetic patient has diabetic neuropathy or determines that the neuropathy is progressing based on the historical evaluation results of the diabetic patient, the evaluation result is sent to a medical care terminal associated with the diabetic patient.

[0022] Optionally, also include:

[0023] Based on the degree of diabetic neuropathy, matching diabetic neuropathy prevention and nursing education resources corresponding to the degree of lesions in a learning resource library;

[0024] The educational resource is sent to a patient terminal associated with the diabetic patient.

[0025] In a second aspect, the present invention further provides a diabetic neuropathy management device based on nylon thread detection, comprising:

[0026] A first acquisition unit is used to collect the feeling result of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient;

[0027] A second acquisition unit is used to acquire the feeling results of the diabetic patient in response to the temperature detection and the vibration detection to obtain quantitative sensory examination data;

[0028] The evaluation unit is used to query the patient's medical history information and drug purchase history information, so as to evaluate the degree of diabetic neuropathy of the patient by combining the medical history information, drug purchase history information, nylon yarn inspection data and the quantitative sensory inspection data.

[0029] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the diabetic neuropathy management method based on nylon filament detection as described in any one of the first aspects above when executing the computer program stored in the memory.

[0030] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for managing diabetic neuropathy based on nylon filament detection according to any one of the above items in the first aspect is implemented.

[0031] In summary, the diabetic neuropathy management method based on nylon thread detection proposed in this application, by collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input, to obtain the nylon thread inspection data of the patient; obtaining the feeling results of the diabetic patient in response to temperature detection and vibration detection, to obtain quantitative sensory inspection data; querying the patient's medical history information and drug purchase history information, so as to combine the medical history information, drug purchase history information, nylon thread inspection data and the quantitative sensory inspection data to evaluate the degree of diabetic neuropathy of the patient. Thus, by combining nylon thread inspection and quantitative sensory testing, the sensitivity and comprehensiveness of DPN screening are significantly improved. The method covers the functional evaluation of large and small nerve fibers, and is low-cost and easy to operate, and is suitable for use in primary medical units or patient families. By integrating the patient's historical information, the method can achieve individualized evaluation, which can not only help patients detect neuropathy early, but also delay the progression of the disease through early intervention. In addition, the flexibility of the method allows detection in a home environment, further expanding the coverage and convenience of screening, and providing an innovative solution for the management of chronic complications of diabetes. In the future, this method can be combined with intelligent equipment to realize real-time uploading and analysis of detection data, further improving diagnostic efficiency and accuracy.

[0032] The diabetic neuropathy management method based on nylon filament detection of the present invention, other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by technicians in this field through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0034] Figure 1 A schematic flow chart of a diabetic neuropathy management method based on nylon filament detection provided in an embodiment of the present application;

[0035] Figure 2 A schematic diagram of the structure of a diabetic neuropathy management device based on nylon thread detection provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of an electronic device for managing diabetic neuropathy based on nylon thread detection provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0038] To address the problem that the sensitivity of nylon thread examination for DPN screening is low and it is not suitable for early diagnosis of DPN, please refer to Figure 1 , is a flow chart of a diabetic neuropathy management method based on nylon filament detection provided in an embodiment of the present application, which may specifically include: steps S110 to S130.

[0039] S110, collecting the perception results of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient.

[0040] S120, obtaining the feeling results of the diabetic patient in response to the temperature detection and the vibration detection to obtain quantitative sensory examination data.

[0041] S130, querying the patient's medical history information and drug purchase history information, so as to evaluate the degree of diabetic neuropathy of the patient by combining the medical history information, drug purchase history information, nylon yarn inspection data and the quantitative sensory inspection data.

[0042] It is understandable that the nylon thread test is a simple, non-invasive tactile assessment method suitable for early screening of diabetic peripheral neuropathy (DPN). The use of 0.4g nylon thread can detect slight tactile damage, which is more sensitive than the traditional 5.07 / 10g nylon thread and is particularly suitable for evaluating early neuropathy. During the operation, medical staff need to ensure that the patient is in a comfortable state, clean the feet, and avoid sweat interference. The method of nylon thread inspection is to contact 0.4g nylon thread with specific test points on the patient's foot (such as toe tip, arch, heel, etc.) in turn, and avoid guessing by patients through randomized order. Each test point is tested three times to record whether the patient can feel the touch of the nylon thread. These data are recorded as "1" (perceived) and "0" (no perception), and finally form a map of the patient's foot tactile perception. By analyzing the number and distribution of abnormal points, the degree of tactile damage of the patient can be preliminarily judged. This method is simple to operate and can be completed by trained personnel in primary medical environments or patients' homes. It does not require professional equipment and is easy to promote.

[0043] It is understandable that quantitative sensory testing is an important supplement to nylon thread examination. Through temperature and vibration detection, the functional status of small nerve fibers and large nerve fibers is evaluated. Temperature detection uses hot and cold probes. The probe surface is kept at the specified high and low temperatures (such as 40°C and 20°C), and contacts multiple parts of the patient's foot in turn. The patient is required to identify the type and location of hot and cold stimuli to determine the sensory function of small nerve fibers. Vibration detection is completed using a 128Hz tuning fork or a vibration sensor. The duration of the patient's perception of vibration is recorded by vibrating the tuning fork and contacting the patient's plantar bone protrusion (such as the base of the big toe). A reduction in vibration perception time usually indicates dysfunction of large nerve fibers. These test data are quantified through parameters such as temperature correct recognition rate and vibration continuous perception time to form a quantitative sensory impairment score for the patient. Compared with nylon thread examination, quantitative sensory testing can more comprehensively reflect the patient's neuropathy condition and provide more basis for early diagnosis.

[0044] It should be noted that the patient's historical information is crucial for a comprehensive assessment of the severity and potential influencing factors of diabetic neuropathy (DPN). By querying the patient's medical records and medication records, you can gain an in-depth understanding of the course of the disease and intervention. Medical records include the patient's blood sugar control level, previous complications (such as diabetic foot ulcers or retinopathy), and related treatment information to help analyze the possible progression of neuropathy. Medication records focus on whether the patient has long-term use of drugs that affect nerve function (such as chemotherapy drugs, certain antibiotics), and whether neuroprotectants (such as methylcobalamin or lipoic acid) are taken in a standardized manner. In addition, the patient's lifestyle information (such as smoking, drinking, and exercise) may also be an important factor affecting nerve health. This information can be obtained through an electronic medical record system or a patient questionnaire, and combined with test data to provide a background basis for subsequent comprehensive analysis.

[0045] Exemplarily, the comprehensive assessment is based on the collected nylon inspection data, quantitative sensory test data and patient history information, and the severity of diabetic neuropathy is systematically evaluated through data integration and weighted scoring. In the nylon inspection, if the number of abnormal points is greater than 3, it indicates early tactile disorder; in the quantitative sensory test, if the temperature correct recognition rate is less than 60% or the vibration perception time is less than 10 seconds, it indicates that the function of small nerve fibers or large nerve fibers is impaired. If these test abnormalities are combined with factors such as long-term poor blood sugar control and irregular use of neuroprotective drugs in the medical history, the patient's neuropathy risk score will be further improved. Ultimately, the evaluation model can divide the patient's neuropathy into three levels: mild, moderate and severe. Mild patients usually only show tactile disorders; moderate patients have abnormal touch, temperature and vibration tests, and are accompanied by a certain high-risk medical history; severe patients have obvious test abnormalities and may have complications such as diabetic foot ulcers. This comprehensive evaluation method can provide clinicians with accurate patient status information, so as to formulate individualized treatment plans.

[0046] In summary, the diabetic neuropathy management method based on nylon thread detection provided in the embodiment of the present application, by collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input, to obtain the nylon thread inspection data of the patient; obtaining the feeling results of the diabetic patient in response to temperature detection and vibration detection, to obtain quantitative sensory inspection data; querying the patient's medical history information and drug purchase history information, so as to combine the medical history information, drug purchase history information, nylon thread inspection data and the quantitative sensory inspection data to evaluate the degree of diabetic neuropathy of the patient. Thus, by combining nylon thread inspection and quantitative sensory testing, the sensitivity and comprehensiveness of DPN screening are significantly improved. The method covers the functional evaluation of large and small nerve fibers, and is low-cost, easy to operate, and suitable for use in primary medical units or patient families. By integrating the patient's historical information, the method can achieve individualized evaluation, which can not only help patients detect neuropathy early, but also delay the progression of the disease through early intervention. In addition, the flexibility of the method allows testing to be carried out in a home environment, further expanding the coverage and convenience of screening, and providing an innovative solution for the management of chronic complications of diabetes. In the future, this method can be combined with intelligent equipment to realize real-time uploading and analysis of detection data, further improving diagnostic efficiency and accuracy.

[0047] According to some embodiments, further comprising:

[0048] Collecting foot image data of the diabetic patient, and analyzing the foot image data to extract skin risk feature data;

[0049] The degree of diabetic neuropathy of the patient is assessed by combining the medical history information, the drug purchase history information, the nylon thread inspection data, the quantitative sensory inspection data and the skin risk characteristic data.

[0050] It is understandable that to further improve the accuracy of diabetic peripheral neuropathy (DPN) screening, combining foot image data analysis to extract skin risk features can provide additional information on neuropathy and skin ulcer risk.

[0051] Exemplarily, a high-definition camera or mobile device is used to capture multi-angle images of the patient's feet to ensure complete coverage of the sole, between the toes and other key areas (such as the heel). To improve the accuracy of the analysis, a uniform light source should be used during the acquisition process, and the feet should be kept clean and dry to avoid interference from light and shadow. The image data is analyzed by an algorithm to extract skin risk features, including but not limited to the following: Skin color changes: such as local redness, pallor or purpura, may indicate inflammation, ischemia or ulcer risk; Skin texture changes: Detect whether there are cracks, scales or dryness, which are usually related to neuropathy; Ulcers or wounds: Assess the risk of infection by identifying skin damage or ulcer sites; Swelling and deformation: Analyze whether the foot morphology is abnormal, such as toe joint deformation or foot edema, indicating structural problems caused by neuropathy; Vascular abnormalities: Extract superficial vascular status information to determine the circulation status of the foot.

[0052] Exemplarily, the skin risk feature data is combined with other key data (nylon inspection data, quantitative sensory test data, medical history information and drug purchase history information) to conduct a multi-dimensional comprehensive assessment: If ulcers, cracks or color changes are found in the foot skin image analysis, it indicates that the patient may be at risk of skin problems caused by neuropathy. If the nylon inspection and quantitative sensory test data are abnormal at the same time, the patient's likelihood of foot ulcers and infections is significantly increased. If there is a record of long-term poor blood sugar control in the medical history, the importance of the skin risk feature data is further weighted. The multi-parameter scoring model is weighted to comprehensively assess the risk of DPN and its related complications. The model is weighted according to the importance and abnormality of the data source. For example, the weight of skin ulcers and cracks is higher in the lesion assessment. Therefore, combined with the extended method of foot image data analysis, the quantitative analysis of skin risk features is added on the basis of the original tactile and sensory assessment, which improves the comprehensiveness and accuracy of diabetic neuropathy screening. Through non-invasive image acquisition methods, it is not only possible to assess the degree of neuropathy, but also to predict the risk of complications such as foot ulcers, providing early warning for patients. In addition, this method is highly flexible and can be combined with mobile terminal devices or intelligent algorithms to complete foot image acquisition and preliminary analysis in a home environment, which will help further promotion and application.

[0053] In some examples, this also includes:

[0054] Collecting foot image data of the diabetic patient, analyzing the foot image data, and calculating ideal nylon thread inspection point positions;

[0055] The ideal nylon thread checkpoint position is displayed in the foot image of the diabetic patient.

[0056] It is understandable that in order to further optimize the efficiency and accuracy of nylon thread inspection, the ideal nylon thread inspection point is calculated and marked based on the patient's foot image data. This method intelligently analyzes the foot image, combines the nerve distribution and risk area characteristics, determines the best inspection position, and visualizes it in the image, providing clear operation guidance for medical staff or patients.

[0057] Exemplarily, high-definition camera equipment is used to collect high-resolution images of the feet of diabetic patients to ensure coverage of the entire sole, including the toes, arches, heels and other areas. To ensure the quality of the image, it should be collected under a uniform light source, and the patient's feet should be clean and dry to avoid light and shadow interference. Through intelligent algorithm analysis of foot images, combined with anatomical and nerve distribution models, the most suitable points for nylon tactile examination are calculated: anatomical references can be included, and the possible inspection areas are preliminarily delineated according to the direction and branches of the main nerve distribution of the foot (such as tibial nerves and peroneal nerves); skin feature recognition, using image processing algorithms to extract the characteristics of the foot skin, including color changes, texture features, swelling, cracking, etc. These features can reflect potential neuropathy or pressure concentration areas; historical data integration, combined with the patient's previous examination results (such as nylon abnormal point distribution) and medical history information, optimize the point distribution strategy, and give priority to covering high-risk areas; point optimization algorithm, based on the uniformity, representativeness and clinical risk feature distribution of specific areas of nerve distribution, use algorithms to select several ideal inspection points (such as 10 points) to ensure the accuracy of the results. After the analysis is completed, the calculated ideal inspection points are directly marked on the patient's foot image for intuitive guidance of operation. The display form can be to mark the ideal points with dots on the image. Different points can be distinguished by color according to the risk level, such as high-risk points are represented by red and low-risk points are represented by green. The operation prompt can be that each point can be accompanied by a number to guide the inspection order. Distance and location instructions (such as "inside of the left foot arch") are provided for easy operation. The generated point-marked image can be presented in the following ways: real-time display, directly displayed on the mobile device screen or inspection instrument; printout, generate paper inspection guidance map for traditional manual operation; electronic archiving, archive the marked image with other patient examination results for subsequent comparison. Based on intelligent algorithms and image analysis, combined with nerve distribution and individual characteristics, the selection of inspection points is optimized to avoid missed inspections or result deviations caused by random distribution of points in traditional inspections. Through image visualization annotation, intuitive point distribution guidance is provided for inspection, reducing operation complexity and subjectivity. Through point optimization, invalid inspections are reduced, operation time is shortened, and screening efficiency is improved.

[0058] For example, for a patient who is undergoing DPN screening for the first time, 10 ideal examination points are calculated through foot image acquisition and analysis, distributed on the toes (4 points), arches (3 points), heels (2 points) and foot edges (1 point). The system marks these points in the image and provides suggestions for the order of examination. Medical staff can perform standardized examinations based on the guidance and quickly obtain highly accurate results.

[0059] For example, for patients whose medical history suggests long-term poor blood sugar control, image analysis shows high-risk features such as pigmentation and cracking in the arch area. Combined with the nerve distribution model, the system prioritizes marking checkpoints in the arch and toes, and highlights these areas in red to remind medical staff to pay special attention.

[0060] In some examples, the foot image data is a video frame based on the collected foot video data of the diabetic patient, and further includes:

[0061] Real-time analysis of the contact point locations of nylon threads in diabetic patients’ foot video data;

[0062] When it is detected that the contact point reaches within the preset area of ​​the ideal nylon thread inspection point, a tactile confirmation question message is generated.

[0063] It is understandable that in order to further improve the accuracy and interactive efficiency of nylon wire inspection, by collecting video data of the patient's foot, the nylon wire contact point position is analyzed in real time to determine whether it has reached the preset area range of the ideal inspection point. When the contact enters the specified range, the system automatically generates a tactile confirmation question message to help the operator obtain instant feedback from the patient and ensure the accuracy of data collection.

[0064] Exemplarily, a high-definition camera is used to record video data of the foot inspection process of a diabetic patient in real time, covering the entire operation area. Video acquisition should ensure that the resolution is high enough so that the system can accurately identify the details of the nylon wire contact and the patient's foot. Video acquisition requires a stable light source and guides the operator to keep the equipment and inspection tools relatively still. By processing video frames in real time, the system analyzes the interactive position of the nylon wire contact and the foot, and can use image recognition technology to locate the foot area and mark the position of the nylon wire. A dynamic tracking algorithm (such as an optical flow method or a deep learning model) can be used to track the contact point between the nylon wire and the foot in real time to determine its movement trajectory in space. The coordinates of the nylon wire contact can be matched with the pre-calculated ideal inspection point to determine whether the contact enters the preset area range. When the system detects that the nylon wire contact enters the preset area range of the ideal inspection point (such as an error of ±2mm), further operation prompts are triggered, wherein the preset area range is defined according to the ideal coordinates when the point is calculated, and adjusted in combination with possible deviations in actual operation. The contact point position is analyzed in real time. If the contact point stays in the area for more than a certain time (such as 1 second), the system determines it as effective contact. In the effective contact area, the system automatically generates a tactile confirmation question message to prompt the operator to obtain patient feedback. The question message includes: visual prompts, such as popping up real-time text information on the video screen, such as "This point is tactilely confirmed, please ask the patient if he feels it." Voice prompts, such as reminding the operator to collect patient feedback through speakers or headphones. At the same time, a record button is provided for the operator to enter the patient's answer (such as "felt" or "not felt"), and the system automatically records the results and updates the analysis report. Therefore, through real-time analysis of video frames, dynamic monitoring of the nylon wire contact position is achieved to ensure accurate operation. The system generates a tactile confirmation question message to remind the operator to obtain patient feedback in a timely manner to reduce missed measurements or misjudgments. Through automatic identification and matching of the contact range, the operator's workload is reduced and the inspection efficiency is improved.

[0065] For example, a diabetic patient is undergoing a tactile examination of the foot with nylon thread. The system analyzes the trajectory of the nylon thread contact point in real time through video acquisition and finds that it enters the preset range of the first ideal point. The system pops up a prompt on the screen: "The contact point reaches the first point, please ask the patient if he feels the touch." The medical staff asks the patient according to the prompt and records his answer "I feel it". The system automatically associates the result with the point information to generate a complete examination record.

[0066] For example, during the operation, the nylon wire contact did not accurately touch the ideal point, but deviated from the preset range. After the system detected the deviation, it prompted on the screen "Please adjust the contact position to get closer to the ideal range of the third point". The operator repositioned according to the prompt until the contact entered the area and completed the inspection, avoiding the result error caused by the operation deviation.

[0067] In some examples, this also includes:

[0068] The evaluation result is sent to a medical terminal associated with the diabetic patient.

[0069] In some examples, sending the evaluation result to a medical terminal associated with the diabetic patient includes:

[0070] When the evaluation result indicates that the diabetic patient has diabetic neuropathy or determines that the neuropathy is progressing based on the historical evaluation results of the diabetic patient, the evaluation result is sent to a medical care terminal associated with the diabetic patient.

[0071] It is understandable that in order to further improve the efficiency and response speed of diabetic neuropathy (DPN) management, when the evaluation results indicate that diabetic patients have neuropathy or determine the progression of the lesion, the system can send the evaluation results in real time to the medical terminal associated with the patient. This function is designed to quickly deliver diagnostic information to the medical team or caregivers to facilitate timely intervention and optimization of treatment plans. Thus, through the automated sending of evaluation results, the patient's neuropathy status and progression information are delivered to the medical terminal in a timely manner, shortening the information delivery cycle. Structured result data is sent together with auxiliary images and analysis reports to provide comprehensive decision support for doctors and caregivers. The evaluation results are stored in the associated electronic medical record system to facilitate subsequent tracking of changes in the patient's condition.

[0072] For example, the evaluation results of a diabetic patient showed that the number of abnormal tactile points on the sole of the foot was 5 / 10, the vibration perception time was significantly shortened, and the foot image analysis showed that there was a risk of early cracks in the arch area. The system sends the evaluation results and diagnosis conclusions to the endocrinologist terminal associated with the patient. After receiving the notification, the doctor can further arrange special examinations for neuropathy and develop a personalized intervention plan.

[0073] For example, in this follow-up visit, the evaluation results of a diabetic patient under long-term management showed that the number of tactile abnormalities increased from 3 / 10 to 7 / 10, and the vibration perception time decreased from 8 seconds to 5 seconds. At the same time, video analysis found that the skin color of the foot darkened, indicating an increased possibility of local ischemia. The system automatically sends data packets to the nursing team terminal, reminding them to adjust the patient's care plan in time, and recommends further diagnosis through the doctor's terminal.

[0074] In some examples, this also includes:

[0075] Based on the degree of diabetic neuropathy, matching diabetic neuropathy prevention and nursing education resources corresponding to the degree of lesions in a learning resource library;

[0076] The educational resource is sent to a patient terminal associated with the diabetic patient.

[0077] Understandably, in order to help diabetic patients better understand and manage diabetic neuropathy (DPN), after assessing the patient's degree of neuropathy, the system matches the prevention and care education resources that match the patient's condition from the learning resource library and sends these resources to the patient's associated terminal device. This function aims to improve the patient's self-management ability through personalized health education, thereby delaying the progression of the disease and reducing the occurrence of complications.

[0078] Exemplarily, the system comprehensively evaluates the degree of diabetic neuropathy based on nylon filament inspection, quantitative sensory testing, foot image or video analysis, and patient history information. The degree of lesions is usually divided into the following levels: no lesions: no obvious signs of nerve damage; mild lesions: mild abnormalities in touch and vibration perception, no skin damage or other high-risk features; moderate lesions: obvious abnormalities in touch, vibration and temperature perception, and the skin may be at risk of early damage (such as cracking, mild ulcers); severe lesions: severe damage to nerve function, accompanied by skin ulcers, infections or other complications. The system has a built-in learning resource library covering a variety of diabetic neuropathy prevention and care content, including: video tutorials (such as foot care instructions, neuropathy prevention); graphic guides (such as diet, exercise and daily care recommendations); interactive tools (such as daily health reminders and symptom record templates). According to the patient's degree of lesions, the system matches the corresponding content from the resource library. For example: no lesions or mild lesions: provide prevention-oriented educational resources, such as foot cleaning and moisturizing techniques, exercise precautions, etc. Moderate lesions: Focus on providing nursing guidance and early intervention advice, including regular checkups, footwear selection, and blood sugar management. Severe lesions: Provide nursing resources for complications, such as wound management methods, when to seek medical help, and diet and medication management strategies. Push messages can be sent through the patient's health management app, and resources can be accessed directly after clicking.

[0079] It is understandable that by obtaining the vibration motor parameters of different models of mobile phones, establishing a mapping model between vibration control parameters and theoretical vibration perception, and applying this model to conduct personalized vibration perception tests based on the patient's mobile phone model, a high-precision quantitative sensory examination can be achieved. This method is suitable for both home testing and large-scale screening, providing an economical and efficient solution for the management of diabetic neuropathy (DPN).

[0080] Exemplarily, vibration perception is handled by Aβ fibers, which are most sensitive to vibrations in a specific frequency range (such as 128Hz). Neuropathy can lead to an increase in the perception threshold or a shortened duration. By controlling the vibration frequency and intensity to gradually test the patient's perceptual feedback, the functional state of their nerve fibers can be quantified. Different models of smartphones use different types of vibration motors (such as linear motors and rotor motors), which differ in vibration frequency, amplitude, and intensity. By experimentally testing the vibration characteristics of different mobile phones, a mapping relationship between vibration parameters and human perception can be established to ensure the standardization of the test. Based on the patient's mobile phone model and its corresponding vibration characteristics, the vibration parameters are adjusted in real time to match the theoretical model, and the patient's perceptual feedback is obtained to form accurate quantitative sensory examination data.

[0081] Exemplarily, a high-precision vibration analyzer is used to test the performance of vibration motors of different models of mobile phones, including frequency range, amplitude and intensity. Record parameters such as peak frequency, amplitude and output power. Use a mobile phone vibration experiment to simulate human perception conditions. Fix the mobile phone on a vibration response platform and test the vibration effects under different frequency and intensity combinations. Test the human perception threshold and duration, record the patient's subjective feedback on vibrations of different intensities, and form a preliminary mapping of mobile phone vibration parameters and perception thresholds. Establish vibration frequency (Hz), intensity (m / s 2 ) and the theoretical relationship between the human perception threshold (perception time, perception intensity). For example: vibration frequency 128Hz, intensity 5m / s 2Corresponding to the perception threshold range of normal people. Based on the vibration experiment, the mapping between the vibration parameters of the mobile phone model and the theoretical perception model is constructed. The model is optimized by regression analysis or machine learning methods to ensure the standardized correction of the vibration characteristics of different mobile phone models. The final control parameter library is generated, such as: mobile phone model A: frequency deviation +2Hz, intensity coefficient 0.8. Mobile phone model B: frequency deviation -3Hz, intensity coefficient 1.2. The patient's mobile phone model can be obtained by the application automatically identifying the mobile phone model or manually input. The vibration control parameters of the corresponding mobile phone model are extracted from the parameter library. The program controls the mobile phone to perform tasks with the vibration intensity and frequency suitable for the patient's test. The vibration perception test is carried out in the following steps: The mobile phone is placed on the test site (such as the base of the toe of the plantar toe). The system gradually increases the vibration intensity, and the patient gives real-time feedback on whether it is perceived. The system records the perception threshold (minimum intensity), duration and feedback accuracy. The results form quantitative sensory examination data, which is compared and analyzed with the health standard. Therefore, through the vibration parameter calibration and mapping model, the difference in vibration characteristics of different mobile phones is solved to ensure that the test results meet the theoretical perception standards. It is applicable to a variety of mobile phone models, expanding the coverage of detection tools and meeting the personalized needs of different patients. Relying on existing smartphone hardware, without the need for additional equipment, patients can complete the test independently in medical institutions or at home. Significantly reducing the cost of testing, it is particularly suitable for primary medical institutions and large-scale screening scenarios. The test data can be automatically analyzed and uploaded to the cloud, and combined with the health management system to achieve dynamic monitoring and management of the patient's condition.

[0082] See also Figure 2 , an embodiment of a diabetic neuropathy management device based on nylon thread detection in the embodiment of the present application may include:

[0083] A first acquisition unit 21 is used to collect the feeling result of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient;

[0084] A second acquisition unit 22 is used to acquire the feeling results of the diabetic patient in response to the temperature detection and the vibration detection to obtain quantitative sensory examination data;

[0085] The evaluation unit 23 is used to query the patient's medical history information and drug purchase history information, so as to evaluate the degree of diabetic neuropathy of the patient by combining the medical history information, drug purchase history information, nylon yarn inspection data and the quantitative sensory inspection data.

[0086] In summary, the diabetic neuropathy management device based on nylon thread detection provided in the embodiment of the present application obtains the nylon thread inspection data of the patient by collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input; obtains the feeling results of the diabetic patient in response to temperature detection and vibration detection to obtain quantitative sensory inspection data; queries the patient's medical history information and drug purchase history information, and combines the medical history information, drug purchase history information, nylon thread inspection data and the quantitative sensory inspection data to evaluate the degree of diabetic neuropathy of the patient. Thus, by combining nylon thread inspection and quantitative sensory testing, the sensitivity and comprehensiveness of DPN screening are significantly improved. The method covers the functional evaluation of large and small nerve fibers, and is low-cost, easy to operate, and suitable for use in primary medical units or patient families. By integrating the patient's historical information, the method can achieve individualized evaluation, which can not only help patients detect neuropathy early, but also delay the progression of the disease through early intervention. In addition, the flexibility of the method allows testing to be carried out in a home environment, further expanding the coverage and convenience of screening, and providing an innovative solution for the management of chronic complications of diabetes. In the future, this method can be combined with intelligent equipment to realize real-time uploading and analysis of detection data, further improving diagnostic efficiency and accuracy.

[0087] like Figure 3 As shown, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any one of the above-mentioned methods for managing diabetic neuropathy based on nylon filament detection are implemented:

[0088] Collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient;

[0089] Obtaining the feeling results of the diabetic patient in response to temperature detection and vibration detection to obtain quantitative sensory examination data;

[0090] The patient's medical history information and drug purchase history information are queried to evaluate the patient's diabetic neuropathy degree in combination with the medical history information, drug purchase history information, nylon thread examination data and the quantitative sensory examination data.

[0091] Since the electronic device introduced in this embodiment is a device used to implement a diabetic neuropathy management device based on nylon thread detection in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by the technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0092] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation method in the corresponding embodiment:

[0093] Collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient;

[0094] Obtaining the feeling results of the diabetic patient in response to temperature detection and vibration detection to obtain quantitative sensory examination data;

[0095] The patient's medical history information and drug purchase history information are queried to evaluate the patient's diabetic neuropathy degree in combination with the medical history information, drug purchase history information, nylon thread examination data and the quantitative sensory examination data.

[0096] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of managing diabetic neuropathy based on nylon filament detection in the corresponding embodiment.

[0102] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0108] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for managing diabetic neuropathy based on nylon filament detection, characterized in that: include: Collecting the feeling results of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient; Obtaining the feeling results of the diabetic patient in response to temperature detection and vibration detection to obtain quantitative sensory examination data; The patient's medical history information and drug purchase history information are queried to evaluate the patient's diabetic neuropathy degree in combination with the medical history information, drug purchase history information, nylon thread examination data and the quantitative sensory examination data.

2. The method according to claim 1, characterized in that Also includes: Collecting foot image data of the diabetic patient, and analyzing the foot image data to extract skin risk feature data; The degree of diabetic neuropathy of the patient is assessed by combining the medical history information, the drug purchase history information, the nylon thread inspection data, the quantitative sensory inspection data and the skin risk characteristic data.

3. The method according to claim 1, characterized in that Also includes: Collecting foot image data of the diabetic patient, analyzing the foot image data, and calculating ideal nylon thread inspection point positions; The ideal nylon thread checkpoint position is displayed in the foot image of the diabetic patient.

4. The method according to claim 3, characterized in that The foot image data is a video frame based on the collected foot video data of the diabetic patient, and further includes: Real-time analysis of the contact point locations of nylon threads in diabetic patients’ foot video data; When it is detected that the contact point reaches within the preset area of ​​the ideal nylon thread inspection point, a tactile confirmation question message is generated.

5. The method according to claim 1, characterized in that Also includes: The evaluation result is sent to a medical terminal associated with the diabetic patient.

6. The method according to claim 5, characterized in that The step of sending the evaluation result to a medical terminal associated with the diabetic patient comprises: When the evaluation result indicates that the diabetic patient has diabetic neuropathy or determines that the neuropathy is progressing based on the historical evaluation results of the diabetic patient, the evaluation result is sent to a medical care terminal associated with the diabetic patient.

7. The method according to any one of claims 1 to 6, characterized in that Also includes: Based on the degree of diabetic neuropathy, matching diabetic neuropathy prevention and nursing education resources corresponding to the degree of lesions in a learning resource library; The educational resource is sent to a patient terminal associated with the diabetic patient.

8. A diabetic neuropathy management device based on nylon thread detection, characterized in that: include: A first acquisition unit is used to collect the feeling result of the diabetic patient in response to the 0.4g nylon thread inspection input to obtain the nylon thread inspection data of the patient; A second acquisition unit is used to acquire the feeling results of the diabetic patient in response to the temperature detection and the vibration detection to obtain quantitative sensory examination data; The evaluation unit is used to query the patient's medical history information and drug purchase history information, so as to evaluate the degree of diabetic neuropathy of the patient by combining the medical history information, drug purchase history information, nylon yarn inspection data and the quantitative sensory inspection data.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the diabetic neuropathy management method based on nylon filament detection as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the diabetic neuropathy management method based on nylon filament detection as described in any one of claims 1 to 7 is implemented.

Images