Diabetic foot treatment system and control method
By combining multi-point temperature measurement, laser positioning, and intelligent verification units with convolutional neural networks, the prediction bias of the diabetic foot grading model is corrected, achieving accurate judgment of diabetic foot grading and precise control of radiofrequency treatment parameters. This solves the prediction bias problem caused by labeling errors and insufficient sample size in existing technologies, and improves treatment efficacy and safety.
Patent Information
- Application Number
- CN202511476875.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-16
- Publication Date
- 2026-01-23
AI Technical Summary
Existing diabetic foot grading models suffer from prediction bias due to image data labeling errors or insufficient sample size, which affects treatment outcomes.
A multi-point temperature measurement unit, a laser positioning unit, a photo recognition unit, and an intelligent verification unit are used to construct a diabetic foot grading model in conjunction with a convolutional neural network. The model prediction bias is corrected by comparing the verification confidence index with the confidence index of adjacent grades, and the output power of the radiofrequency treatment device is dynamically adjusted in conjunction with real-time temperature data.
It improves the accuracy of diabetic foot grading, enables precise control of radiofrequency treatment parameters, avoids the risk of undertreatment or tissue damage, and enhances treatment efficacy and patient safety.
Smart Images
Figure CN121371499A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of diabetic foot treatment, and in particular to a diabetic foot treatment system and a control method. BACKGROUND
[0002] Diabetic foot is a general term for chronic ulcers, infections and gangrene of the feet caused by neuropathy and vasculopathy in diabetic patients. Its pathological characteristics include clinical manifestations such as reduced sensory nerve function, blood circulation disorder and decreased immune defense ability. At present, the medical community generally relies on a diabetic foot grade judgment model based on a deep learning algorithm to judge the grade of diabetic foot, which automatically analyzes the morphological characteristics of the patient's foot by constructing a convolutional neural network.
[0003] However, if the image data used for model training has annotation errors, such as mislabeling a third-level severe disease as a second-level severe disease, or a certain type of sample (such as a fifth-level severe diabetic foot) has insufficient data collection due to clinical rarity, it will significantly affect the learning process of the model. Specifically, the error labeling will cause the algorithm to deviate in the feature extraction stage, resulting in the model's inability to accurately capture typical disease characteristics of different grades, and insufficient sample size will limit the model's generalization ability for rare severe grades, resulting in class bias in the prediction stage, such as misjudging a fifth-level severe disease as a fourth-level severe disease or a lower level. Such recognition errors will directly mislead the selection of subsequent treatment plans, such as the adjustment standard of radiofrequency treatment power not matching the actual condition, ultimately reducing the treatment effect, and even possibly exacerbating the patient's condition due to insufficient intervention.
[0004] In view of the above technical defects, the present application provides a solution. SUMMARY
[0005] The purpose of the present application is to solve the problem of inaccurate diabetic foot disease grade recognition resulting from annotation errors in image data used for diabetic foot grade judgment model training, or insufficient data collection of a certain type of sample due to clinical rarity, causing prediction bias, and thus affecting the treatment effect of diabetic foot.
[0006] To achieve the above purpose, the present application adopts the following technical solution: a diabetic foot treatment system, comprising a multi-point temperature measurement unit, a laser positioning unit, a photographing and identifying unit, an intelligent verification unit and a power adjustment unit. The multi-point temperature measurement unit is used to monitor foot temperature data in real time by setting multiple wireless temperature measurement points at different positions of the foot, and to send the temperature data to the intelligent verification unit and the power adjustment unit. The laser positioning unit provides foot positioning guidance for the patient by projecting a visible light spot based on the straight-line propagation characteristics of the laser beam, assisting the patient to place the foot in the correct position. The photo recognition unit is used to take and store photos of the patient's feet, and then build a diabetic foot grading model to recognize the patient's diabetic feet from the photos and determine the preliminary grade of diabetic foot. The intelligent verification unit is used to verify the diabetic foot level determined by the photo recognition unit. If the verification accuracy is low, the patient's diabetic foot level will be re-determined, and the final level of diabetic foot will be determined. The power adjustment unit is used to acquire temperature data and perform calculations and analyses based on the patient's diabetic foot grade, thereby adjusting the output power of the radiofrequency treatment device for diabetic foot and performing radiofrequency treatment on the diabetic foot.
[0007] Furthermore, the process of constructing a diabetic foot grading model is as follows: S11. Collect image data of multiple different grades of diabetic foot as a dataset, and randomly divide the dataset into training set and test set. Label the corresponding diabetic foot grade on each morphological image in the training set as a label. The diabetic foot grades are divided into grade 0, grade 1, grade 2, grade 3, grade 4 and grade 5. S12. Construct a diabetic foot grading model based on a convolutional neural network, train the diabetic foot grading model using a training set, and test the diabetic foot grading model using a test set to obtain a qualified diabetic foot grading model. S13. Input the patient's foot photograph into the qualified diabetic foot grading model to determine the preliminary grade of the patient's diabetic foot.
[0008] Furthermore, the process of verifying the diabetic foot severity level determined by the image recognition unit is as follows: S21. Based on the preliminary grade of the patient's diabetic foot determined by the diabetic foot grade judgment model, obtain the preset standard temperature of the diabetic foot and the preset standard conduction velocity of the foot motor nerve corresponding to the preliminary grade, and obtain the similarity data of the training set images in the diabetic foot grade judgment model corresponding to the preliminary grade of the patient's diabetic foot. At the same time, obtain the average temperature of the temperature measuring point on the diabetic foot and the conduction velocity data of the foot motor nerve. S22. Calculate the confidence index for the diabetic foot grade using the following formula. : ; in, The number of training images in the diabetic foot grading model corresponding to the initial grade of diabetic foot in patients. Let be the similarity between the training set images and the patient's diabetic foot images in the diabetic foot grading model corresponding to the i-th preliminary grade. For the preset image standard similarity, This represents the average temperature measured at various points on the diabetic foot ulcer. The preset standard temperature for diabetic foot corresponds to the initial grade of diabetic foot in patients. The velocity of the motor nerves in the foot of a patient with diabetic foot. The preset standard conduction velocity of foot motor nerves corresponds to the initial grade of diabetic foot in patients. The preset temperature weighting coefficient, The preset motor nerve conduction weighting coefficient; S23. Obtain the preset upper limit threshold for verification confidence. Validation confidence lower limit threshold , and the confidence index of the test Comparative analysis, when If the result is accurate, it indicates that the diabetic foot grade determined by the photo recognition unit is highly accurate, and the preliminary grade of diabetic foot is determined as the final grade of diabetic foot. S24, when If the result is negative, it indicates that the accuracy of the diabetic foot grade determined by the photo recognition unit is low, and the patient's diabetic foot grade will be reassessed.
[0009] Furthermore, the process of reassessing the diabetic foot grading of patients is as follows: S31. When the initial level of diabetic foot determined by the photo recognition unit is level zero and the level needs to be reassessed, the final level of diabetic foot is directly determined to be level one. S32. When the initial grade of diabetic foot determined by the photo recognition unit is grade 5 severe and a reassessment of the grade is required, the final grade of diabetic foot is directly determined to be grade 4 severe. S33. When the initial grade of diabetic foot determined by the image recognition unit is Grade 1, Grade 2, Grade 3, or Grade 4 severe, and a re-evaluation of the grade is required, the confidence index of the two adjacent grades of the initial grade is calculated using the confidence index formula for diabetic foot grade. and And comparative analysis, among which The next level below the preliminary level. The next level above the preliminary level, when When the initial grade is determined, the next level below it is defined as the final grade of diabetic foot. In such cases, the next higher level than the initial level is determined as the final level of diabetic foot. In such cases, medical experts are invited to determine the final grade of the patient's diabetic foot.
[0010] Furthermore, the process of adjusting the output power of the radiofrequency ablation device for diabetic foot is as follows: S41. Obtain the preset base power of the radiofrequency treatment device for diabetic foot and the average temperature data of the temperature measurement points on the diabetic foot, and perform analysis and calculation. S42. Calculate the adjusted output power of the radiofrequency ablation device for diabetic foot using the following formula. : ; in, The preset base power of the radiofrequency ablation device for diabetic foot is determined based on the final stage of diabetic foot. The target temperature for treating diabetic foot is preset based on the final stage of diabetic foot. This represents the average temperature measured at various points on the diabetic foot ulcer. When taking a temperature measurement at a temperature measurement point on a diabetic foot ulcer, the difference in average temperature between adjacent temperature measurement samples. When taking temperature measurements at the temperature measurement point on a diabetic foot ulcer, the interval between adjacent temperature measurement samples is as follows: The rate of change of average temperature The preset temperature deviation conversion factor is used to determine the final grade of diabetic foot. The preset temperature change conversion coefficient is used based on the final grade of diabetic foot. S43, The power adjustment unit adjusts the output power of the diabetic foot radiofrequency treatment device based on calculations and analysis. The output power of the radiofrequency ablation device for diabetic foot is adjusted to treat the patient's diabetic foot.
[0011] The present invention also provides a method for treating and controlling diabetic foot, comprising the following steps: Step 1: By setting multiple wireless temperature measurement points at different locations on the foot, the foot temperature data is monitored in real time and sent to the intelligent verification unit and power adjustment unit; Step 2: Based on the linear propagation characteristics of laser beams, the laser positioning unit projects a visible light spot to provide foot positioning guidance for the patient, helping the patient to place their feet in the correct position. Then, the photo recognition unit takes and stores photos of the patient's feet to build a diabetic foot grading model. The model is used to recognize the patient's diabetic feet from the photos to determine the preliminary grade of diabetic foot. Step 3: The intelligent verification unit verifies the diabetic foot level determined by the photo recognition unit. If the verification accuracy is low, the patient's diabetic foot level is re-determined, and the final level of diabetic foot is determined. Step 4: Obtain temperature data through the power adjustment unit and perform calculation and analysis in conjunction with the patient's diabetic foot grade to obtain the output power of the adjusted radiofrequency treatment device for diabetic foot. Adjust the output power of the radiofrequency treatment device for diabetic foot and treat the patient's diabetic foot.
[0012] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This diabetic foot treatment system and control method utilizes an intelligent verification unit to comprehensively analyze multi-source data, including training set image similarity, standard foot temperature, and motor nerve conduction velocity. Combined with a preset confidence index formula, it verifies the initial grade, effectively identifying and correcting model prediction biases caused by training data labeling errors or insufficient sample size. Through preset direct adjustment rules, comparison of confidence indices between adjacent grades, and the introduction of a medical expert confirmation mechanism, the accuracy of diabetic foot grade assessment is effectively improved. Secondly, based on the final determined diabetic foot grade and real-time temperature data, the power adjustment unit dynamically calculates the output power of the radiofrequency ablation device, achieving precise control of treatment parameters. This effectively avoids the risk of insufficient treatment or tissue damage caused by a fixed power scheme in radiofrequency ablation devices, improving the treatment effect on diabetic foot patients while ensuring patient safety. Attached Figure Description Figure 1 A schematic diagram of the system flow of the present invention is shown; Figure 2 A schematic diagram of the method flow of the present invention is shown. Detailed Implementation
[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1
[0014] like Figure 1 As shown, a diabetic foot treatment system first utilizes a multi-point temperature measurement unit to monitor foot temperature data in real time by setting multiple wireless temperature measurement points at different locations on the foot, and sends the temperature data to an intelligent verification unit and a power adjustment unit. Then, a laser positioning unit projects a visible light spot based on the linear propagation characteristics of a laser beam to provide foot positioning guidance for the patient, assisting the patient in placing their foot in the correct position for photographing and treatment. Next, a photographic recognition unit captures and stores images of the patient's foot, and then constructs a diabetic foot grading model to perform photographic recognition of the patient's diabetic foot and determine the preliminary grade of diabetic foot. The process of constructing a diabetic foot grading model is as follows: S11. Collect image data of multiple different grades of diabetic foot as a dataset, and randomly divide the dataset into training set and test set. Label the corresponding diabetic foot grade on each morphological image in the training set as a label. The diabetic foot grades are divided into grade 0, grade 1, grade 2, grade 3, grade 4 and grade 5. S12. Construct a diabetic foot grading model based on a convolutional neural network, train the diabetic foot grading model using a training set, and test the diabetic foot grading model using a test set to obtain a qualified diabetic foot grading model. S13. Input the patient's foot photograph into the qualified diabetic foot grading model to determine the preliminary grade of the patient's diabetic foot.
[0015] Then, the diabetic foot level determined by the photo recognition unit is verified by the intelligent verification unit. If the verification accuracy is low, the patient's diabetic foot level is re-determined, and the final level of diabetic foot is determined. The process of verifying the diabetic foot severity determined by the image recognition unit is as follows: S21. Based on the preliminary grade of the patient's diabetic foot determined by the diabetic foot grade judgment model, obtain the preset standard temperature of the diabetic foot and the preset standard conduction velocity of the foot motor nerve corresponding to the preliminary grade, and obtain the similarity data of the training set images in the diabetic foot grade judgment model corresponding to the preliminary grade of the patient's diabetic foot. At the same time, obtain the average temperature of the temperature measurement point on the diabetic foot and the conduction velocity data of the foot motor nerve. The conduction velocity of the foot motor nerve is calculated by stimulating the nerve trunk and recording the muscle action potential, combined with the distance between stimulation points and the latency difference. S22. Calculate the confidence index for the diabetic foot grade using the following formula. : ; in, The number of training images in the diabetic foot grading model corresponding to the initial grade of diabetic foot in patients. Let be the similarity between the training set images and the patient's diabetic foot images in the diabetic foot grading model corresponding to the i-th preliminary grade. For the preset image standard similarity, This represents the average temperature measured at various points on the diabetic foot ulcer. The preset standard temperature for diabetic foot corresponds to the initial grade of diabetic foot in patients. The velocity of the motor nerves in the foot of a patient with diabetic foot. The preset standard conduction velocity of foot motor nerves corresponds to the initial grade of diabetic foot in patients. The preset temperature weighting coefficient, The preset motor nerve conduction weighting coefficients, and ; S23. Obtain the preset upper limit threshold for verification confidence. Validation confidence lower limit threshold , and the confidence index of the test Comparative analysis, when If the result is accurate, it indicates that the diabetic foot grade determined by the photo recognition unit is highly accurate, and the preliminary grade of diabetic foot is determined as the final grade of diabetic foot. S24, when If the result is negative, it indicates that the accuracy of the diabetic foot grade determined by the photo recognition unit is low, and the patient's diabetic foot grade will be reassessed.
[0016] The process for reassessing the diabetic foot severity of patients is as follows: S31. When the initial level of diabetic foot determined by the photo recognition unit is level zero and the level needs to be reassessed, the final level of diabetic foot is directly determined to be level one. S32. When the initial grade of diabetic foot determined by the photo recognition unit is grade 5 severe and a reassessment of the grade is required, the final grade of diabetic foot is directly determined to be grade 4 severe. S33. When the initial grade of diabetic foot determined by the image recognition unit is Grade 1, Grade 2, Grade 3, or Grade 4 severe, and a re-evaluation of the grade is required, the confidence index of the two adjacent grades of the initial grade is calculated using the confidence index formula for diabetic foot grade. and And comparative analysis, among which It is the next level below the initial level (e.g., if the initial level is Level 1 severe illness, the next level below it is Level 2 severe illness). The next level above the initial level (e.g., if the initial level is level four severe illness, the next level above it is level three severe illness), when When the initial grade is determined, the next level below it is defined as the final grade of diabetic foot. In such cases, the next higher level than the initial level is determined as the final level of diabetic foot. In such cases, medical experts are invited to determine the final grade of the patient's diabetic foot.
[0017] Finally, temperature data is obtained through the power adjustment unit and calculated and analyzed in conjunction with the patient's diabetic foot grade. The output power of the radiofrequency treatment device for diabetic foot is then adjusted and radiofrequency treatment is performed on the diabetic foot. The process of adjusting the output power of the radiofrequency ablation device for diabetic foot is as follows: S41. Obtain the preset base power of the radiofrequency treatment device for diabetic foot and the average temperature data of the temperature measurement points on the diabetic foot, and perform analysis and calculation. S42. Calculate the adjusted output power of the radiofrequency ablation device for diabetic foot using the following formula. : ; in, The preset base power of the radiofrequency ablation device for diabetic foot is determined based on the final stage of diabetic foot. The target temperature for treating diabetic foot is preset based on the final stage of diabetic foot. This represents the average temperature measured at various points on the diabetic foot ulcer. When taking a temperature measurement at a temperature measurement point on a diabetic foot ulcer, the difference in average temperature between adjacent temperature measurement samples. When taking temperature measurements at the temperature measurement point on a diabetic foot ulcer, the interval between adjacent temperature measurement samples is as follows: The rate of change of average temperature The preset temperature deviation conversion factor is used to determine the final grade of diabetic foot. The preset temperature change conversion coefficient is used to determine the final grade of diabetic foot. and All were determined by analyzing a large amount of historical operating data from radiofrequency ablation devices, combined with the severity of diabetic foot disease and the actual impact of temperature; S43, The power adjustment unit adjusts the output power of the diabetic foot radiofrequency treatment device based on calculations and analysis. The output power of the radiofrequency ablation device for diabetic foot is adjusted to treat the patient's diabetic foot. Example 2
[0018] like Figure 2 As shown, a method for treating and controlling diabetic foot includes the following steps: Step 1: By setting multiple wireless temperature measurement points at different locations on the foot, the foot temperature data is monitored in real time and sent to the intelligent verification unit and power adjustment unit; Step 2: Based on the linear propagation characteristics of laser beams, the laser positioning unit projects a visible light spot to provide foot positioning guidance for the patient, helping the patient to place their feet in the correct position. Then, the photo recognition unit takes and stores photos of the patient's feet to build a diabetic foot grading model. The model is used to recognize the patient's diabetic feet from the photos to determine the preliminary grade of diabetic foot. Step 3: The intelligent verification unit verifies the diabetic foot level determined by the photo recognition unit. If the verification accuracy is low, the patient's diabetic foot level is re-determined, and the final level of diabetic foot is determined. Step 4: Obtain temperature data through the power adjustment unit and perform calculation and analysis in conjunction with the patient's diabetic foot grade to obtain the output power of the adjusted radiofrequency treatment device for diabetic foot. Adjust the output power of the radiofrequency treatment device for diabetic foot and treat the patient's diabetic foot.
[0019] This invention utilizes an intelligent verification unit to comprehensively analyze multi-source data, including training set image similarity, standard foot temperature, and motor nerve conduction velocity, and combines this with a preset confidence index formula to verify the initial grade. This effectively identifies and corrects model prediction biases caused by incorrect training data labeling or insufficient sample size. Through preset direct adjustment rules, comparison of confidence indices between adjacent grades, and the introduction of a medical expert confirmation mechanism, the accuracy of diabetic foot grade assessment is effectively improved. Secondly, based on the final determined diabetic foot grade and real-time temperature data, the power adjustment unit dynamically calculates the output power of the radiofrequency ablation device, achieving precise control of treatment parameters. This effectively avoids the risk of insufficient treatment or tissue damage caused by a fixed power scheme for radiofrequency ablation devices, improving the treatment effect for diabetic foot patients while ensuring patient safety.
[0020] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.
[0021] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A diabetic foot treatment system, characterized in that, It includes a multi-point temperature measurement unit, a laser positioning unit, a photo recognition unit, an intelligent verification unit, and a power adjustment unit; The multi-point temperature measurement unit is used to monitor foot temperature data in real time by setting multiple wireless temperature measurement points at different locations on the foot, and send the temperature data to the intelligent verification unit and the power adjustment unit. The laser positioning unit is based on the linear propagation characteristics of laser beams. It provides foot positioning guidance to patients by projecting visible light spots, helping patients to place their feet in the correct position. The photo recognition unit is used to take and store photos of the patient's feet, and then build a diabetic foot grading model to recognize the patient's diabetic feet from the photos and determine the preliminary grade of diabetic foot. The intelligent verification unit is used to verify the diabetic foot level determined by the photo recognition unit. If the verification accuracy is low, the patient's diabetic foot level will be re-determined, and the final level of diabetic foot will be determined. The power adjustment unit is used to acquire temperature data and perform calculations and analyses based on the patient's diabetic foot grade, thereby adjusting the output power of the radiofrequency treatment device for diabetic foot and performing radiofrequency treatment on the diabetic foot.
2. The diabetic foot treatment system according to claim 1, characterized in that, The process of constructing a diabetic foot grading model is as follows: S11. Collect image data of multiple different grades of diabetic foot as a dataset, and randomly divide the dataset into training set and test set. Label the corresponding diabetic foot grade on each morphological image in the training set as a label. The diabetic foot grades are divided into grade 0, grade 1, grade 2, grade 3, grade 4 and grade 5. S12. Construct a diabetic foot grading model based on a convolutional neural network, train the diabetic foot grading model using a training set, and test the diabetic foot grading model using a test set to obtain a qualified diabetic foot grading model. S13. Input the patient's foot photograph into the qualified diabetic foot grading model to determine the preliminary grade of the patient's diabetic foot.
3. The diabetic foot treatment system according to claim 1, characterized in that, The process of verifying the diabetic foot severity determined by the image recognition unit is as follows: S21. Based on the preliminary grade of the patient's diabetic foot determined by the diabetic foot grade judgment model, obtain the preset standard temperature of the diabetic foot and the preset standard conduction velocity of the foot motor nerve corresponding to the preliminary grade, and obtain the similarity data of the training set images in the diabetic foot grade judgment model corresponding to the preliminary grade of the patient's diabetic foot. At the same time, obtain the average temperature of the temperature measuring point on the diabetic foot and the conduction velocity data of the foot motor nerve. S22. Calculate the verification confidence index for the diabetic foot grade using the following formula. : ； in, The number of training images in the diabetic foot grading model corresponding to the initial grade of diabetic foot in patients. Let be the similarity between the training set images and the patient's diabetic foot images in the diabetic foot grading model corresponding to the i-th preliminary grade. For the preset image standard similarity, This represents the average temperature measured at various points on the diabetic foot ulcer. The preset standard temperature for diabetic foot corresponds to the initial grade of diabetic foot in patients. The velocity of the motor nerves in the foot of a patient with diabetic foot. To correspond to the preset standard conduction velocity of foot motor nerves for the initial grade of diabetic foot in patients, The preset temperature weighting coefficient, The preset motor nerve conduction weighting coefficient; S23. Obtain the preset upper limit threshold for verification confidence. And the lower confidence threshold of the test , and the confidence index of the test Comparative analysis, when If the result is accurate, it indicates that the diabetic foot grade determined by the photo recognition unit is highly accurate, and the preliminary grade of diabetic foot is determined as the final grade of diabetic foot. S24, when If the result is negative, it indicates that the accuracy of the diabetic foot grade determined by the photo recognition unit is low, and the patient's diabetic foot grade will be reassessed.
4. The diabetic foot treatment system according to claim 1, characterized in that, The process for reassessing the diabetic foot severity of patients is as follows: S31. When the initial level of diabetic foot determined by the photo recognition unit is level zero and the level needs to be reassessed, the final level of diabetic foot is directly determined to be level one. S32. When the initial grade of diabetic foot determined by the photo recognition unit is grade 5 severe and a reassessment of the grade is required, the final grade of diabetic foot is directly determined to be grade 4 severe. S33. When the initial grade of diabetic foot determined by the image recognition unit is Grade 1, Grade 2, Grade 3, or Grade 4 severe, and a re-evaluation of the grade is required, the confidence index of the two adjacent grades of the initial grade is calculated using the confidence index formula for diabetic foot grade. and And comparative analysis, among which The next level below the preliminary level. The next level above the preliminary level, when When the initial grade is determined, the next level below it is defined as the final grade of diabetic foot. In such cases, the next higher level than the initial level is determined as the final level of diabetic foot. In such cases, medical experts are invited to determine the final grade of the patient's diabetic foot.
5. The diabetic foot treatment system according to claim 1, characterized in that, The process of adjusting the output power of the radiofrequency ablation device for diabetic foot is as follows: S41. Obtain the preset base power of the radiofrequency treatment device for diabetic foot and the average temperature data of the temperature measurement points on the diabetic foot, and perform analysis and calculation. S42. Calculate the adjusted output power of the radiofrequency ablation device for diabetic foot using the following formula. : ； in, The preset base power of the radiofrequency ablation device for diabetic foot is determined based on the final stage of diabetic foot. The target temperature for treating diabetic foot is preset based on the final stage of diabetic foot. This represents the average temperature measured at various points on the diabetic foot ulcer. When taking a temperature measurement at a temperature measurement point on a diabetic foot ulcer, the difference in average temperature between adjacent temperature measurement samples. When taking temperature measurements at the temperature measurement point on a diabetic foot ulcer, the interval between adjacent temperature measurement samples is as follows: The rate of change of average temperature The preset temperature deviation conversion factor is used to determine the final grade of diabetic foot. The preset temperature change conversion coefficient is used based on the final grade of diabetic foot. S43, The power adjustment unit adjusts the output power of the diabetic foot radiofrequency treatment device based on calculations and analysis. The output power of the radiofrequency ablation device for diabetic foot is adjusted to treat the patient's diabetic foot.
6. A method for treating and controlling diabetic foot, characterized in that, Includes the following steps: Step 1: By setting multiple wireless temperature measurement points at different locations on the foot, the foot temperature data is monitored in real time and sent to the intelligent verification unit and power adjustment unit; Step 2: Based on the linear propagation characteristics of laser beams, the laser positioning unit projects a visible light spot to provide foot positioning guidance for the patient, helping the patient to place their feet in the correct position. Then, the photo recognition unit takes and stores photos of the patient's feet to build a diabetic foot grading model. The model is used to recognize the patient's diabetic feet from the photos to determine the preliminary grade of diabetic foot. Step 3: The intelligent verification unit verifies the diabetic foot level determined by the photo recognition unit. If the verification accuracy is low, the patient's diabetic foot level is re-determined, and the final level of diabetic foot is determined. Step 4: Obtain temperature data through the power adjustment unit and perform calculation and analysis in conjunction with the patient's diabetic foot grade to obtain the output power of the adjusted radiofrequency treatment device for diabetic foot. Adjust the output power of the radiofrequency treatment device for diabetic foot and treat the patient's diabetic foot.