Diabetic foot management and control method for diabetic patient and related equipment

Abstract

The invention discloses a diabetic foot management and control method for a diabetic patient and related equipment. The method comprises the following steps: periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on historical diabetes data of the patient; generating a diabetic foot risk assessment problem table based on the peripheral neuropathy examination data and the diabetes historical data; and based on the peripheral neuropathy examination data and feedback data of the patient in response to the risk assessment problem table, predicting the diabetic foot disease risk level of the diabetic patient through a column diagram prediction model. The problem that a simple, feasible and easy-to-popularize diabetic foot prediction method is lacked at present 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 method for controlling diabetic foot in diabetic patients and related equipment. Background Art

[0002] Diabetic foot is a condition in which diabetic patients suffer from lower limb infection, ulcers, and / or deep tissue damage due to the combination of peripheral neuropathy and peripheral vascular disease. About 25% of diabetic patients will develop foot ulcers in their lifetime. Studies have found that even with medical treatment, the amputation rate of diabetic foot patients is still as high as 23%. Diabetic foot not only seriously endangers the physical and mental health of patients, but also increases the economic burden on patients' families. Through effective risk factor management, 75% of diabetic foot complications can be prevented. The International Diabetic Foot Working Group recommends that diabetic patients should be examined regularly to assess their risk of diabetic foot. Therefore, early identification of high-risk patients for diabetic foot and timely implementation of stratified intervention are of great significance and are an important research topic at present. At present, there is a lack of simple, feasible, and easy-to-promote diabetic foot prediction methods. Summary of the invention

[0003] 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.

[0004] In order to solve the problem of the lack of a simple, feasible and easy-to-promote diabetic foot prediction method, in the first aspect, the present invention proposes a diabetic foot control method for diabetic patients, the method comprising:

[0005] Periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data;

[0006] Generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data;

[0007] The risk level of diabetic foot disease in the diabetic patient is predicted by a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

[0008] Optionally, also include:

[0009] Based on the risk level, a diabetic foot prevention and treatment plan corresponding to the risk level is recommended to the diabetic patient, wherein the prevention and treatment plan includes a foot care plan and a self-monitoring plan.

[0010] Optionally, the self-monitoring program includes:

[0011] The image acquisition function, vibration function and battery thermal management function of the mobile phone of the diabetic patient are utilized to assist the diabetic patient in self-monitoring.

[0012] Optionally, the method of using the vibration function of the diabetic patient's mobile phone to assist the diabetic patient in self-monitoring includes:

[0013] Get the vibration motor parameters of different mobile phone models;

[0014] Establishing a mapping relationship model between vibration control parameters of different models of mobile phones and theoretical vibration sensations based on the vibration motor parameters and / or vibration experiments;

[0015] The mobile phone model of the diabetic patient is obtained, and based on a pre-established mapping relationship model between the mobile phone model and theoretical vibration sensation, the mobile phone is controlled to vibrate with different intensities, and the patient's perception result is obtained as vibration perception monitoring data.

[0016] Optionally, the method of using the battery thermal management function of the mobile phone of the diabetic patient to assist the diabetic patient in self-monitoring includes:

[0017] The temperature change of the mobile phone is adjusted by controlling the battery thermal management system of the mobile phone of the diabetic patient, and the patient's perception result is obtained as temperature perception monitoring data.

[0018] Optionally, the using of the image acquisition function of the mobile phone of the diabetic patient to assist the diabetic patient in self-monitoring includes:

[0019] Acquiring foot image information of the diabetic patient through the image acquisition function;

[0020] Performing image analysis on the foot image information to extract sub-images of abnormal skin points;

[0021] The skin abnormality point sub-image is matched with diabetic skin features to generate skin condition monitoring data.

[0022] Optionally, also include:

[0023] In case there is an abnormality in the monitoring data, generating an early warning message;

[0024] The warning message is sent to the client to which the diabetic patient belongs.

[0025] In a second aspect, the present invention further provides a diabetic foot control device for diabetic patients, comprising:

[0026] A detection unit, used for periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data;

[0027] A generating unit, configured to generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data;

[0028] The analysis unit is used to predict the diabetic foot disease risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

[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 method for managing diabetic foot in diabetic patients 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 foot in diabetic patients according to any one of the above items in the first aspect is implemented.

[0031] In summary, the diabetic foot control method for diabetic patients proposed in this application obtains peripheral neuropathy examination data of diabetic patients periodically, and the period is dynamically determined based on the patient's diabetes history data; generates a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data; and predicts the diabetic foot risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question table. Therefore, the combination of dynamic monitoring and quantitative evaluation can significantly improve the early identification rate of high-risk patients with diabetic foot and reduce the risk of disease progression. Through stratified risk assessment, it helps medical staff to develop personalized intervention plans, such as providing special foot care education or increasing the frequency of follow-up for high-risk patients. This method combines dynamic examinations and simplified risk questionnaires, is suitable for primary medical institutions and patient self-management, and has a strong promotion value.

[0032] The diabetic foot management method for diabetic patients 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 also 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 diagram of a flow chart of a diabetic foot control method for diabetic patients provided in an embodiment of the present application;

[0035] Figure 2 A schematic diagram of the structure of a diabetic foot control device for diabetic patients provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of an electronic device for controlling diabetic foot in diabetic patients 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] In order to solve the problem of the lack of simple, feasible and easy-to-promote diabetic foot prediction methods, please refer to Figure 1 , is a flow chart of a method for managing diabetic foot in diabetic patients provided in an embodiment of the present application, which may specifically include: steps S110 to S130.

[0039] S110, periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data.

[0040] S120, generating a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data.

[0041] S130, predicting the diabetic foot risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

[0042] It should be noted that diabetic foot is a serious complication caused by peripheral neuropathy and vascular disease. Early identification and stratified intervention of diabetic patients can effectively reduce its incidence and amputation rate. This program proposes a simple and easy method for predicting diabetic foot through periodic data acquisition, personalized risk assessment, and nomogram model prediction of disease risk level.

[0043] It is understandable that there are individual differences in the course and progression of diabetes. By collecting the patient's diabetes history data (such as duration of illness, blood sugar control, previous history of ulcers, etc.), the cycle of peripheral neuropathy examination is dynamically determined to ensure that the examination frequency matches the patient's actual risk level. The peripheral neuropathy examination data is combined with the diabetes history data, and a risk assessment questionnaire is generated through systematic analysis, focusing on the patient's degree of nerve function damage, foot symptoms (such as numbness, pain), and related lifestyle habits (such as shoe and sock selection, foot care). A nomogram model is used to conduct a multivariate comprehensive analysis of the patient's peripheral neuropathy data and the evaluation questionnaire feedback data to generate an intuitive risk score. The model visualizes the weights of multiple variables to help doctors predict the risk level of diabetic foot in patients and formulate intervention measures in a stratified manner.

[0044] Exemplarily, peripheral neuropathy examination data is obtained periodically, wherein the examination method may include a vibration sense test, using a 128Hz tuning fork to detect the vibration perception ability of the foot. A pinprick test, assessing the sensitivity of the patient's foot to pinprick pain. And nylon thread (Semmes-Weinstein test): detect the loss of tactile sensation in multiple areas of the sole of the foot. For high-risk patients (such as those with a history of foot ulcers), an examination can be performed every 3 months. For low- and medium-risk patients, an examination can be performed every 6 to 12 months. Diabetes history data may include the patient's blood sugar control (HbA1c level), length of disease course and other influencing factors. Use electronic health records (EHR) to record the examination results and integrate them with the patient's historical data. For example, patient A: 10 years of diabetes, large fluctuations in HbA1c levels, and no history of ulcers. The dynamic calculation examination cycle is 6 months. Patient B: 15 years of disease, with a history of ulcers and amputations. It is recommended to check every 3 months.

[0045] Exemplarily, a question sheet is automatically generated based on the results of peripheral neuropathy examinations and the patient's historical data (such as foot deformities, skin lesions). For example, questions may include: Is there numbness or tingling in the feet? Do you check your feet regularly? Do you wear appropriate shoes and socks? Etc. The questionnaire is filled out through a mobile phone or a tablet device in the clinic, and the patient's answer data is uploaded in real time. The system classifies and analyzes the questionnaire feedback data to identify potential high-risk signals. For example, patient A reported in the questionnaire that "the feet often tingle and no daily foot examinations are performed." The system marks it as a moderate risk to remind medical staff to pay special attention.

[0046] Exemplarily, multiple variables (such as vibration sense loss, previous ulcer history, footwear selection, blood sugar level) are converted into quantitative risk scores through a nomogram model. Based on the weight of each variable, a cumulative score is generated and the patient's risk level (low, medium, and high risk) is predicted. The model input can be peripheral neuropathy examination data and questionnaire feedback data. The output can be the risk level of diabetic foot disease, as well as a visualized personalized risk score chart. For example, the model input: patient A's peripheral neuropathy data (loss of vibration sense, partial abnormality of pinprick sensation), questionnaire feedback data (improper footwear selection). Output: The model predicts that the probability of diabetic foot disease in the next 6 months is 25%, marked as moderate risk.

[0047] In summary, the diabetic foot control method for diabetic patients provided in the embodiment of the present application is to periodically obtain the peripheral neuropathy examination data of the diabetic patient, and the period is dynamically determined based on the patient's diabetes history data; generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data; and predict the diabetic foot risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question table. Therefore, the combination of dynamic monitoring and quantitative evaluation can significantly improve the early identification rate of high-risk patients with diabetic foot and reduce the risk of disease progression. Through stratified risk assessment, it helps medical staff to develop personalized intervention plans, such as providing special foot care education or increasing the frequency of follow-up for high-risk patients. This method combines dynamic examinations and simplified risk questionnaires, is suitable for primary medical institutions and patient self-management, and has a strong promotion value.

[0048] According to some embodiments, further comprising:

[0049] Based on the risk level, a diabetic foot prevention and treatment plan corresponding to the risk level is recommended to the diabetic patient, wherein the prevention and treatment plan includes a foot care plan and a self-monitoring plan.

[0050] Understandably, foot care programs provide step-by-step interventions from basic care to specialized care for patients at different risk levels. For low-risk patients (early prevention and control), the goal of care is to maintain foot health and prevent early damage. Recommended measures may be daily foot inspections: instruct patients to visually and touch the skin of the feet (such as whether there are cracks, redness, swelling, blisters). Loose and comfortable shoes that do not compress the feet are recommended. Choose socks made of seamless design and sweat-absorbing materials. Use non-irritating moisturizers to avoid excessive moisture between the toes. Trim toenails regularly (avoid too short to avoid injuring soft tissue). Wash the feet with warm water (avoid hot water) and dry thoroughly. For example, patient A is a low-risk patient, and daily monitoring shows that the nerve function of the feet is basically normal and blood sugar control is good. It is recommended that he check his feet and use moisturizer every day, and change sweat-absorbing and breathable socks. For moderate-risk patients (enhanced monitoring), the goal of care may be to delay the progression of lesions and avoid the appearance of new lesions. Recommended measures may include strengthening monitoring and avoiding potential injuries in addition to low-risk measures. It is recommended to visit a professional medical institution regularly for foot examinations (such as toenail trimming and callus removal) by a podiatrist. Protective insoles or arch support devices are provided to reduce pressure concentration. If small cracks appear on the feet, use sterile gauze and antibacterial ointment to treat them. Avoid long walks or wearing high heels. For example, patient B has mild vibration loss, and the questionnaire shows that he has worn inappropriate shoes that caused toe tenderness. The plan recommends that he wear corrective insoles, go to the hospital for monthly examinations, and strengthen self-examination of the feet. For high-risk patients (active intervention), the goal of care can be to prevent ulcers and infections and protect foot function. Recommended measures may include implementing a higher level of protection based on moderate-risk measures. Provide customized shoes based on abnormal foot structure (such as hallux valgus, hammertoe). If redness, swelling, and ulcers are found on the feet, see a doctor immediately and be treated by a specialist. After cleaning the feet, apply disinfectants (such as chlorhexidine solution) to susceptible areas. Avoid wet environments, especially in the rainy season. For example, patient C has severe vibration loss and a history of foot ulcers. The protocol recommends wearing customized foot protection shoes, regular follow-up, and establishing a dedicated podiatry care file.

[0051] Exemplarily, patient self-monitoring is an important part of diabetic foot prevention and treatment, with the goal of helping patients detect problems in a timely manner and take early action. For low-risk patients, the monitoring frequency may be once a day. Monitoring content may include foot skin examination (color, temperature, integrity). Pay attention to whether there are foreign objects or tightness in shoes and socks. Educational content may include training patients to master simple foot detection techniques. Provide foot health education manuals. For moderate-risk patients, the monitoring frequency may be twice a day (once in the morning and once in the evening). Monitoring content may include detailed records of changes in foot sensation (such as numbness, tingling) and local pressure points. Use mobile phone applications to record changes in sole pressure distribution (such as equipped with smart insoles). Educational content may be to guide patients to use portable vibration detection tools. Educate patients to contact a doctor immediately once a problem is found. For example, patient B reported that he often felt tingling in his feet at night. It is recommended that he be monitored through a vibration detection tool, and if the abnormality persists, he should seek medical attention in time. For high-risk patients, the monitoring frequency may be 3 times a day. Monitoring content may include emphasizing pre-ulcer signal monitoring (such as redness, swelling, damage, and exudation). Use an infrared thermometer to monitor changes in sole temperature (temperature difference >2°C may indicate inflammation). Transmit data to medical institutions in real time through remote monitoring equipment. For example, patient C has experienced abnormal temperature in the area where sole pressure is concentrated. It is recommended to install wireless monitoring equipment and connect to the doctor team for real-time tracking. Therefore, according to the patient's risk level, targeted care plans and monitoring measures are provided in layers to significantly reduce the incidence of diabetic foot ulcers. Through intuitive risk assessment and simple and easy-to-implement prevention and treatment plans, help patients take the initiative and form good daily habits. Focus on high-risk patients for intervention, while reducing over-medicalization of low-risk patients and reducing the overall medical burden.

[0052] In some examples, the self-monitoring regimen includes:

[0053] The image acquisition function, vibration function and battery thermal management function of the mobile phone of the diabetic patient are utilized to assist the diabetic patient in self-monitoring.

[0054] In some examples, using the vibration function of the diabetic patient's mobile phone to assist the diabetic patient in self-monitoring includes:

[0055] Get the vibration motor parameters of different mobile phone models;

[0056] Establishing a mapping relationship model between vibration control parameters of different models of mobile phones and theoretical vibration sensations based on the vibration motor parameters and / or vibration experiments;

[0057] The mobile phone model of the diabetic patient is obtained, and based on a pre-established mapping relationship model between the mobile phone model and theoretical vibration sensation, the mobile phone is controlled to vibrate with different intensities, and the patient's perception result is obtained as vibration perception monitoring data.

[0058] It should be noted that by using the vibration function of the diabetic patient's mobile phone and the pre-established mapping relationship between the mobile phone model and vibration perception, the patient is guided to self-monitor vibration perception. This method is not only low-cost and easy to promote, but also can help patients detect neuropathy problems early. The following details its implementation principle, key steps and technical effects.

[0059] It is understandable that peripheral neuropathy in diabetic patients usually manifests as a decrease in sensory function, especially a decrease in sensitivity to vibration. Through the vibration motor (Haptic Motor) of the mobile phone, the patient's feet are stimulated according to vibration signals of different intensities, and their vibration perception feedback is recorded to evaluate the integrity of the vibration sense. The key points include: vibration motor parameter modeling, that is, the performance of vibration motors of different mobile phone models is different, and it is necessary to establish a mapping relationship between vibration intensity and actual feeling. Personalized monitoring, that is, adjusting the vibration intensity according to the mobile phone model and patient feedback to accurately evaluate the patient's vibration sense function.

[0060] Exemplarily, obtain the technical specifications of vibration motors of different models of mobile phones (such as vibration frequency range, amplitude). Use external test equipment (such as accelerometer) to verify the vibration intensity and frequency. Through a standardized test process, place the mobile phone on a surface that simulates human skin (such as silicone material). Record the theoretical vibration perception threshold of a normal human body for vibration signals of different intensities. For example, test mobile phone model A: the vibration frequency range is 100-200Hz, and the peak intensity is 5m / s 2 Tested mobile phone model B: vibration frequency range 120-250Hz, peak intensity 6.5m / s 2 . The test results show that the 200Hz vibration of model A is weaker than the vibration of the same frequency of model B, and needs to be corrected during modeling. Collect a large amount of user data and analyze the perceptual feedback corresponding to vibration signals of different intensities (such as "perceivable" and "imperceptible"). Establish a "vibration intensity-vibration perception" mapping model, normalize the intensity range, and make the vibration effects of different mobile phone models consistent. Assume that the vibration perception threshold conforms to the Sigmoid curve distribution, and adjust the parameters for different mobile phone vibration signals. Curve example: vibration intensity <1m / s 2 90% of users cannot feel it; intensity>4m / s 2 95% of users can clearly perceive it. For example, the mapping relationship model (mobile phone model A): 100Hz: the perception threshold is 1.5m / s 2 200Hz: Perception threshold is 3.0m / s 2 Mapping relationship model (mobile phone model B): 120Hz: perception threshold is 1.2m / s 2 250Hz: Perception threshold is 2.8m / s2 . During the patient's self-monitoring process, the device model and vibration motor parameters are automatically obtained through the mobile phone's operating system interface (such as Android or iOS API). Based on the mobile phone model and the pre-established mapping model, vibration signals of different intensities and frequencies are generated. The patient places the mobile phone on a specific part of the sole of the foot (such as the toe, heel). The system triggers vibration signals of different intensities in turn, and the patient records the perception through the App (such as "perceived" or "not perceived"). Repeat the test multiple times and take the average value to reduce the error. For example, patient A uses model B mobile phone: the vibration frequency is 120Hz, and the patient's perception intensity threshold is 2.0m / s 2 . The vibration frequency is 250Hz, and the patient has no perception, so the system marks it as "vibration sense loss". The system compares the patient's feedback data with the theoretical model to determine the status of his vibration sense function. If the perception threshold is higher than the normal range, the patient may have peripheral nerve damage. The system generates a detailed report, including: perception threshold curve. Risk warning (such as "mild injury" or "high risk"). As a result, the patient's existing mobile phone devices can be used without additional hardware investment, greatly reducing monitoring costs. Patients can complete self-tests at home without frequent visits to the hospital, thereby enhancing follow-up compliance. The model corrects the error between vibration intensity and perception threshold to ensure the scientific nature of the monitoring results. Early detection of neuropathy problems provides data support for doctors to facilitate the formulation of further intervention measures.

[0061] In some examples, utilizing the battery thermal management function of the diabetic patient's mobile phone to assist the diabetic patient in self-monitoring includes:

[0062] The temperature change of the mobile phone is adjusted by controlling the battery thermal management system of the mobile phone of the diabetic patient, and the patient's perception result is obtained as temperature perception monitoring data.

[0063] It should be noted that diabetic patients often show reduced temperature perception due to peripheral neuropathy, which may make it difficult for patients to detect skin damage caused by high or low temperatures. Through the thermal management function of the mobile phone battery, the dynamic changes in the surface temperature of the device are controlled, and the integrity of the patient's temperature perception is evaluated based on the patient's perceived feedback data of temperature changes. This method makes full use of the characteristics of existing devices and provides a low-cost and convenient means of self-monitoring.

[0064] It is understandable that the thermal management system of mobile phone batteries controls the overall temperature change of the device by adjusting the operating frequency of the CPU or GPU, limiting the battery charge and discharge rate, adjusting the power consumption mode, etc. The thermal management function can increase or decrease the surface temperature of the device within a certain range. Peripheral nerve damage in diabetic patients may lead to reduced temperature sensitivity. By simulating the gradual increase or decrease of the ambient temperature, the patient's temperature perception ability is recorded and the state of their neurological function is evaluated.

[0065] Exemplarily, the thermal management capabilities of the device (such as maximum temperature, temperature control rate) are obtained through the open interface or diagnostic tool of the mobile phone operating system. Different mobile phone models have different thermal management limits and need to be calibrated separately. Increase the CPU / GPU computing load to trigger the battery thermal management system. By adjusting the power consumption mode or adjusting the heat dissipation function, gradually increase the surface temperature of the device (such as from 35°C to 45°C). Reduce the CPU / GPU load, or enable the fast heat dissipation mechanism. Gradually reduce the surface temperature of the device (such as from 30°C to 20°C). Use the built-in temperature sensor to monitor the surface temperature of the device in real time. Limit the temperature change range to a safe range (such as not exceeding 45°C to avoid burns). For example, mobile phone model A: the controllable surface temperature range is 20°C to 40°C. By adjusting the load, the device temperature changes every 30 seconds in steps of 1°C. Instruct the patient to stick the phone to a specific area of ​​the foot (such as the sole of the foot, the back of the toes). Ensure that the contact area is sufficient and there is no obstruction (such as thick socks). The mobile phone executes the heating and cooling modes in sequence, and prompts the patient to record the sensory feedback after each temperature change. The patient selects "perceived temperature change" or "not perceived" through the App interface. Repeat the test on multiple areas of the foot (such as toes, arches, and heels) to ensure data accuracy. If the patient fails to perceive a certain temperature change, the system automatically increases the temperature difference to retest. For example, the initial temperature is set to 30°C and gradually increases to 40°C. The patient reported that he first perceived the change when the temperature reached 38°C, and the system recorded his temperature perception threshold. According to the patient's temperature perception threshold (such as the lowest or highest temperature perceived), it is compared with the data of normal people. Determine whether the temperature perception function is abnormal (such as slow perception or complete loss). The system generates a temperature perception impairment assessment report for the patient based on the monitoring results. In this way, the functions of existing equipment (mobile phones) are fully utilized, and no additional hardware investment is required. Patients can complete monitoring at home at any time. Accurate control of temperature changes is achieved through precise matching of mobile phone models and thermal management parameters. The digitized temperature perception threshold provides a basis for quantitative evaluation. Timely detection of abnormal temperature perception helps prevent secondary injuries such as burns and frostbite. Provide graded intervention measures to support the diagnosis and treatment of medical institutions.

[0066] In some examples, using the image acquisition function of the diabetic patient's mobile phone to assist the diabetic patient in self-monitoring includes:

[0067] Acquiring foot image information of the diabetic patient through the image acquisition function;

[0068] Performing image analysis on the foot image information to extract sub-images of abnormal skin points;

[0069] The skin abnormality point sub-image is matched with diabetic skin features to generate skin condition monitoring data.

[0070] It should be noted that by using the camera image acquisition function of the diabetic patient's mobile phone to obtain the patient's foot image information, combined with image analysis technology to extract the skin abnormal area and match it with the diabetic skin feature database to generate monitoring data. This method is simple and easy to use, and can help patients detect early skin abnormalities and prevent the occurrence and deterioration of complications such as diabetic foot.

[0071] For example, the patient is instructed to use a mobile phone camera to take multi-angle high-definition images of the front, side, and sole of the foot under good lighting conditions (natural light or uniform white light). The shooting distance is recommended to be set to 20-30 cm to avoid blurring the image due to being too close. Light correction can be performed to correct the difference in brightness and darkness caused by uneven lighting. Image resolution can be optimized to ensure clear image details for subsequent analysis (such as at least 1080p resolution). Region segmentation can be performed, that is, the contour of the foot is identified by an algorithm and non-relevant background (such as floor, shoes) is filtered out. For example, the patient uses a mobile phone to take an image of the foot, and the preprocessing is completed through real-time guidance of the App to ensure that the image meets the analysis requirements. The foot image is analyzed using a convolutional neural network algorithm to extract relevant features. Color abnormalities can be included, for example, identifying areas such as erythema, pallor, and blackening. Identify morphological abnormalities, such as detecting structural features such as blisters, ulcers, and fissures. Identify texture abnormalities, for example, analyze skin texture changes such as roughness and scaling. Mark the potentially abnormal areas as sub-images, perform local amplification and enhancement processing, and facilitate refined analysis. Among them, accurate segmentation of foot skin can be achieved based on models such as U-Net or Mask R-CNN. Feature classifiers (such as SVM, deep learning models) are used to determine whether it is an abnormal point. For example, the image analysis results show that a 3 mm diameter erythema is found on the sole of the patient's left foot, and the system extracts this area as an abnormal point sub-image. The extracted abnormal point sub-image is matched with the diabetic skin feature database. For example, the erythema feature may indicate a local pressure point or early infection. The black spot feature may be related to tissue necrosis or blood circulation disorders. Or it can be compared with known diabetic foot ulcer features to determine the severity of the ulcer. A report is generated based on the matching results, which may include: the type, size, and location of the abnormal area. It can also include a similarity score with typical diabetic foot lesions. It can also include a risk level prompt (low risk, medium risk, high risk). For example, the system analyzes the erythema feature, and the matching results show that the diameter of the erythema gradually increases and the surrounding area is slightly swollen, which is determined to be an early infection risk, and the patient is advised to seek medical treatment as soon as possible. Therefore, through regular self-monitoring, patients can be helped to detect skin lesions such as erythema, blisters or ulcers as early as possible, reducing the incidence of serious complications. Patients can complete monitoring at home, which can reduce unnecessary hospital follow-up times, save time and costs. Intuitive image reports and systematic feedback enhance patients' awareness of diabetic foot self-management and promote long-term monitoring.

[0072] In some examples, this also includes:

[0073] In case there is an abnormality in the monitoring data, generating an early warning message;

[0074] The warning message is sent to the client to which the diabetic patient belongs.

[0075] It is understandable that during the monitoring of the foot skin condition of diabetic patients, when the detection data is abnormal, the system will automatically generate an early warning message and send the message to the patient's client (such as a mobile phone app or a doctor's system). This process realizes instant feedback and graded early warning of abnormal data, helping patients take early intervention measures to prevent serious complications.

[0076] See also Figure 2 , an embodiment of the diabetic foot control device for diabetic patients in the embodiment of the present application may include:

[0077] A detection unit 21, for periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data;

[0078] A generating unit 22, configured to generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data;

[0079] The analyzing unit 23 is used to predict the diabetic foot risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the feedback data of the patient in response to the risk assessment question form.

[0080] In summary, the diabetic foot control device for diabetic patients provided in the embodiment of the present application periodically obtains the peripheral neuropathy examination data of the diabetic patient, and the period is dynamically determined based on the patient's diabetes history data; generates a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data; and predicts the diabetic foot risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question table. Therefore, the combination of dynamic monitoring and quantitative evaluation can significantly improve the early identification rate of high-risk patients with diabetic foot and reduce the risk of disease progression. Through stratified risk assessment, it helps medical staff to develop personalized intervention plans, such as providing special foot care education or increasing the frequency of follow-up for high-risk patients. This method combines dynamic examinations with simplified risk questionnaires, is suitable for primary medical institutions and patient self-management, and has a strong promotion value.

[0081] like Figure 3As 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 of the above-mentioned methods for controlling diabetic foot in diabetic patients are implemented:

[0082] Periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data;

[0083] Generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data;

[0084] The risk level of diabetic foot disease in the diabetic patient is predicted by a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

[0085] Since the electronic device introduced in this embodiment is a device used to implement a diabetic foot control device for diabetic patients in the embodiment of the present application, based on the method introduced in the embodiment of the present application, 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 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.

[0086] 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:

[0087] Periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data;

[0088] Generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data;

[0089] The risk level of diabetic foot disease in the diabetic patient is predicted by a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0095] 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 diabetic foot management for diabetic patients in the corresponding embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 controlling diabetic foot in diabetic patients, characterized in that: include: Periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data; Generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data; The risk level of diabetic foot disease in the diabetic patient is predicted by a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

2. The method according to claim 1, characterized in that Also includes: Based on the risk level, a diabetic foot prevention and treatment plan corresponding to the risk level is recommended to the diabetic patient, wherein the prevention and treatment plan includes a foot care plan and a self-monitoring plan.

3. The method according to claim 1, characterized in that The self-monitoring program includes: The image acquisition function, vibration function and battery thermal management function of the mobile phone of the diabetic patient are utilized to assist the diabetic patient in self-monitoring.

4. The method according to claim 3, characterized in that The method of using the vibration function of the diabetic patient's mobile phone to assist the diabetic patient in self-monitoring includes: Get the vibration motor parameters of different mobile phone models; Establishing a mapping relationship model between vibration control parameters of different models of mobile phones and theoretical vibration sensations based on the vibration motor parameters and / or vibration experiments; The mobile phone model of the diabetic patient is obtained, and based on a pre-established mapping relationship model between the mobile phone model and theoretical vibration sensation, the mobile phone is controlled to vibrate with different intensities, and the patient's perception result is obtained as vibration perception monitoring data.

5. The method according to claim 3, characterized in that The method of using the battery thermal management function of the mobile phone of the diabetic patient to assist the diabetic patient in self-monitoring includes: The temperature change of the mobile phone is adjusted by controlling the battery thermal management system of the mobile phone of the diabetic patient, and the patient's perception result is obtained as temperature perception monitoring data.

6. The method according to claim 3, characterized in that The method of using the image acquisition function of the mobile phone of the diabetic patient to assist the diabetic patient in self-monitoring includes: Acquiring foot image information of the diabetic patient through the image acquisition function; Performing image analysis on the foot image information to extract sub-images of abnormal skin points; The skin abnormality point sub-image is matched with diabetic skin features to generate skin condition monitoring data.

7. The method according to any one of claims 4 to 6, characterized in that Also includes: In case there is an abnormality in the monitoring data, generating an early warning message; The warning message is sent to the client to which the diabetic patient belongs.

8. A diabetic foot control device for diabetic patients, characterized in that: include: A detection unit, used for periodically acquiring peripheral neuropathy examination data of a diabetic patient, wherein the period is dynamically determined based on the patient's diabetes history data; A generating unit, configured to generate a diabetic foot risk assessment question table based on the peripheral neuropathy examination data and the diabetes history data; The analysis unit is used to predict the diabetic foot disease risk level of the diabetic patient through a nomogram prediction model based on the peripheral neuropathy examination data and the patient's feedback data in response to the risk assessment question form.

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 foot management method for diabetic patients as described in any one of claims 1-7 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 foot management method for diabetic patients according to any one of claims 1 to 7 is implemented.

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