A wound recognition and payment system based on precision medicine

Abstract

This invention belongs to the field of medical image processing technology. It discloses a wound recognition and billing system based on precision medicine, achieving intelligent management of wound treatment through multi-module collaborative operation. First, multi-angle digital images of the wound are acquired and standardized. After image enhancement and noise reduction, deep learning algorithms are applied for recognition and segmentation. Three-dimensional reconstruction is performed based on precise contour maps to form parametric feature vectors, enabling intelligent identification of wound type and assessment of severity. Based on the assessment results, the most suitable dressing type is matched, and the required consumables are predicted, generating a precise dressing plan. Personalized billing is then calculated and transparency is verified. Finally, the system automatically generates standardized medical service records through historical data comparison and rationality analysis. This system achieves precision, standardization, and transparency in wound treatment, improving the quality of medical services and optimizing resource allocation.

Description

Technical Field

[0001] This invention relates to the field of medical image processing technology, and more specifically, to a wound recognition and payment system based on precision medicine. Background Technology

[0002] Wound management, a crucial aspect of clinical medicine, involves multiple specialties including surgery, burn care, dermatology, and diabetic foot clinics. With advancements in medical technology, wound management has evolved from simple debridement and bandaging to a comprehensive treatment system. Currently, wound assessment primarily relies on visual observation and experience-based judgment by healthcare professionals, typically employing methods such as ruler measurement, transparent membrane tracing, and digital camera photography to obtain basic wound information. For wound area measurement, commonly used clinical methods include the length-width product method, transparent grid tracing, and digital image processing techniques. Internationally, the red, yellow, and black tricolor classification system and the NPUAP / EPUAP pressure injury grading system are widely adopted for wound classification.

[0003] Regarding wound treatment fees, most medical institutions adopt a pricing model based on wound level or a tiered pricing model based on wound area, such as for wounds smaller than 5cm. 2 5-10cm 2 , greater than 10cm 2 These correspond to different fee standards. With the advancement of medical informatization, some hospitals have begun to use digital imaging technology to assist in wound assessment. For example, two-dimensional image analysis software can achieve semi-automatic measurement of wound area, but the accuracy and consistency still need to be improved.

[0004] In clinical practice, healthcare professionals often rely on visual observation and subjective experience to assess wound conditions, leading to significant discrepancies in assessments of the same wound among different doctors and impacting treatment selection. Traditional manual measurement methods, such as translucent film tracing or ruler measurement, are not only time-consuming and labor-intensive but also suffer from low accuracy and poor repeatability, especially for irregularly shaped or unevenly deep wounds, where measurement errors are even greater. The lack of standardized dressing change criteria in medical institutions results in arbitrary selection of consumables, leading to waste of high-value supplies and potentially delaying wound healing due to inappropriate choices. In terms of billing, the lack of a scientific pricing mechanism based on objective parameters can result in vastly different charges for the same wound treatment. Healthcare professionals struggle to clearly explain the complexity of wound treatment and the rationality of charges to patients. Furthermore, wound treatment data is scattered across different systems, lacking a complete traceability mechanism. This not only increases the risk of medical errors but also hinders big data analysis and clinical research, ultimately limiting the overall improvement of wound treatment standards and the optimal allocation of medical resources.

[0005] In view of this, the present invention proposes a wound recognition and payment system based on precision medicine to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a wound recognition and payment system based on precision medicine, comprising:

[0007] The image acquisition and standardization module is used to acquire multi-angle digital images of the wound and perform standardization processing to obtain a standardized image dataset of the wound.

[0008] The image enhancement and noise reduction module is used to enhance and reduce noise in the standardized wound image dataset to obtain optimized image data.

[0009] The boundary recognition and segmentation module is used to apply deep learning algorithms to accurately recognize and segment the wound boundary based on the optimized image data, so as to obtain an accurate contour map of the wound.

[0010] The parameter measurement and reconstruction module is used to automatically measure the area, length and depth of the wound and perform three-dimensional reconstruction based on the precise contour map of the wound to obtain the wound parameter feature vector.

[0011] The wound type identification and assessment module is used to intelligently identify the wound type and assess its severity based on the wound parameter feature vector, and obtain a wound classification assessment matrix.

[0012] The matching and consumable prediction module is used to match dressing types and predict consumables based on the wound classification and evaluation matrix to obtain an accurate dressing plan.

[0013] The billing calculation and verification module is used to perform personalized billing calculation and transparency verification based on the precise dressing change plan, and obtain a dynamic billing model.

[0014] The analysis and record generation module is used to compare historical data and perform rationality analysis based on the dynamic charging model to obtain a charging rationality assessment report; based on the charging rationality assessment report and the precise dressing change plan, it automatically generates dressing change prescriptions and charging details to obtain standardized medical service records. The various modules are connected via wired and / or wireless means.

[0015] Preferably, the acquisition of multi-angle digital images of the wound and the standardization processing to obtain a standardized wound image dataset includes:

[0016] A medical-grade high-definition camera under standard light conditions is used to acquire multi-angle images of the wound to obtain a set of original wound images. The original wound image set is then subjected to illumination equalization processing to obtain an illumination-corrected image.

[0017] The illumination-corrected image is color-calibrated and a standard scale bar is embedded to obtain a scale-normalized image. Then, the viewing angle is corrected based on the scale-normalized image to obtain a viewing angle consistency image.

[0018] The image resolution of the viewpoint-consistent image is normalized to obtain a standardized image, and the metadata of the standardized image is annotated to obtain an annotated image data.

[0019] Spatial registration and multi-angle fusion are performed on the labeled image data to obtain a multi-dimensional view model, and the image format is standardized and converted on the multi-dimensional view model to obtain a standard format image;

[0020] The standardized image is associated with the patient's medical information and stored to obtain a standardized wound image dataset.

[0021] Preferably, the enhancement and noise reduction processing of the standardized wound image dataset to obtain optimized image data includes:

[0022] An adaptive histogram equalization algorithm is applied to the standardized wound image dataset to obtain a contrast-enhanced image, and nonlocal mean filtering is performed on the contrast-enhanced image to obtain a preliminary denoised image.

[0023] Wavelet transform is applied to the initial denoised image to remove high-frequency noise, resulting in a frequency-domain optimized image. Edge-preserving smoothing is then applied to the frequency-domain optimized image to obtain a detail-enhanced image.

[0024] The detailed enhancement image is subjected to image layer analysis to obtain a layered feature map, and targeted image enhancement is performed based on the layered feature map to obtain a feature-highlighted image;

[0025] Advanced noise removal is performed on the feature-highlighted image using a deep learning denoising network to obtain a high-quality denoised image. The high-quality denoised image is then evaluated to obtain a quality score table.

[0026] Based on the quality scoring table, the optimal processing parameters are selected to reprocess the image, resulting in optimized image data.

[0027] Preferably, the step of applying a deep learning algorithm based on the optimized image data to accurately identify and segment the wound boundary to obtain an accurate wound contour map includes:

[0028] A pre-trained convolutional neural network is applied to the optimized image data to perform preliminary localization of the wound region, resulting in a candidate region proposal map. A pixel-level semantic segmentation network is then applied to the candidate region proposal map to obtain a preliminary segmentation mask.

[0029] The initial segmentation mask is refined by applying an active contour model to obtain a fine boundary map. Then, a region growing algorithm is applied to the fine boundary map to process the fuzzy boundary regions, resulting in a complete contour map.

[0030] Conditional random fields are applied to the complete contour map to optimize spatial consistency, resulting in a spatially optimized contour. The spatially optimized contour is then verified by multi-scale feature fusion to obtain a verified contour map.

[0031] The verified contour map is constrained and corrected by medical expert knowledge rules to obtain the clinical verification contour. The boundary coordinates of the clinical verification contour are extracted and vectorized to obtain contour vector data.

[0032] The contour vector data is overlaid on the original image, and boundary markers and measurement reference points are added to obtain a precise contour map of the wound.

[0033] Preferably, the automatic measurement and three-dimensional reconstruction of area, length, and depth based on the precise contour map of the wound to obtain a wound parameter feature vector includes:

[0034] The pixel area of ​​the wound is calculated based on the precise contour map of the wound and converted to the actual area using a scale bar to obtain precise area data. The length of the wound is measured along the maximum extension direction of the wound to obtain the length parameter.

[0035] A wound depth map is constructed using multi-angle image information to obtain a depth distribution map. The average and maximum depths of the wound are then calculated based on the depth distribution map to obtain a set of depth parameters.

[0036] Based on the precise contour map of the wound and the depth parameter set, a three-dimensional mesh model of the wound is constructed to obtain a three-dimensional wound model. The volume and surface area of ​​the three-dimensional wound model are then calculated to obtain the three-dimensional parameter data.

[0037] Morphological analysis was performed on the precise contour map of the wound to extract morphological features such as perimeter, ellipticity, and complexity, resulting in a morphological feature set. Tissue type identification and area proportion analysis were performed on the wound interior to obtain tissue feature data.

[0038] By integrating the precise area data, the length parameter, the depth parameter set, the three-dimensional parameter data, the morphological feature set, and the tissue feature data, a wound parameter feature vector is obtained.

[0039] Preferably, the step of intelligently identifying wound type and assessing severity based on the wound parameter feature vector to obtain a wound classification assessment matrix includes:

[0040] A multi-class machine learning algorithm is applied to the feature vector of the wound parameters to obtain a preliminary judgment result of the wound type. Based on the preliminary judgment result of the wound type, the wound type knowledge base is queried to obtain the standard wound type definition.

[0041] Based on the standard wound type definition and the wound parameter feature vector, the wound severity score is calculated to obtain a severity assessment index. The healing difficulty is then predicted based on the severity assessment index to obtain a healing assessment report.

[0042] Anomaly detection is performed on the feature vector of the wound parameters to identify special wound types, special type labels are obtained, and processing priority weights are set according to the special type labels to obtain a priority coefficient table;

[0043] The severity assessment indicators, the healing assessment report, and the priority coefficient table are mapped to the clinical treatment guideline rule set to obtain a list of clinical intervention recommendations. Based on the list of clinical intervention recommendations, the required dressing change resource level is determined to obtain a resource requirement table.

[0044] The standard wound type definition, the severity assessment index, the healing assessment report, the priority coefficient table, and the resource requirement table are integrated to obtain a wound classification assessment matrix.

[0045] Preferably, the step of matching dressing types and predicting consumables based on the wound classification assessment matrix to obtain a precise dressing change plan includes:

[0046] The dressing change type is automatically determined based on the area, length, and depth data of the wound classification and evaluation matrix to obtain a preliminary dressing change type result. The standard dressing change type definition library is then queried based on the preliminary dressing change type result to obtain the standard dressing change specification.

[0047] The tissue characteristics and severity assessment indicators in the wound classification assessment matrix are judged a second time to obtain auxiliary judgment correction results. The auxiliary judgment correction results are then merged with the initial judgment results of the dressing change type to obtain the final dressing change type confirmation.

[0048] Based on the final dressing change type confirmation and the wound classification assessment matrix, the required types and quantities of medical consumables are predicted, resulting in a consumables list estimation table. The consumables cost is then calculated based on the consumables list estimation table to obtain material cost accounting data.

[0049] Based on the final medication change type, the required medical staff hours and professional levels are determined, and a human resource demand forecast is obtained. Based on the human resource demand forecast, the labor cost is calculated to obtain labor cost accounting data.

[0050] By integrating the material cost accounting data, the labor cost accounting data, and the final dressing change type confirmation, a precise dressing change plan is obtained.

[0051] Preferably, the step of performing personalized billing calculations and transparency verification based on the precise medication change plan to obtain a dynamic billing model includes:

[0052] The basic fee is calculated based on the dressing type and consumable cost in the precise dressing change plan to obtain the basic fee standard. The complexity adjustment coefficient is then calculated based on the complexity index in the wound classification assessment matrix to obtain the adjusted fee.

[0053] The adjusted charges are adjusted according to medical insurance policies and rules and regional differences to obtain regional charging standards. Based on the regional charging standards and the patient's special circumstances, personalized adjustments are made to obtain personalized charging schemes.

[0054] A cost-benefit analysis is performed on the personalized pricing scheme to obtain a cost-benefit assessment report. Based on the cost-benefit assessment report, a pricing fairness verification mechanism is constructed to obtain the fairness verification results.

[0055] Based on the fairness verification results, a detailed fee explanation document is generated to obtain a fee transparency document. A patient-friendly fee explanation interface is designed based on the fee transparency document to obtain a visual fee explanation.

[0056] The personalized pricing scheme, the cost-benefit assessment report, and the pricing transparency document are integrated to construct a dynamic pricing model.

[0057] Preferably, the process involves comparing historical data and performing a reasonableness analysis based on the dynamic charging model to obtain a charging reasonableness assessment report; based on the charging reasonableness assessment report and the precise medication change plan, a medication change prescription and charging details are automatically generated to obtain a standardized medical service file, including:

[0058] The dynamic charging model is compared and analyzed with historical charging data for similar wounds to obtain longitudinal comparison results, and is also compared with the charging data for similar services in medical institutions in the same region to obtain horizontal comparison results.

[0059] Based on the longitudinal comparison results and the horizontal comparison results, a charging deviation index is calculated to obtain a charging deviation assessment table. A reasonableness judgment standard is established based on the charging deviation assessment table to obtain a reasonableness judgment framework.

[0060] The dynamic charging model is evaluated using the aforementioned reasonableness assessment framework to obtain the charging reasonableness analysis results. Based on the charging reasonableness analysis results, charging recommendations are formed to obtain a charging reasonableness assessment report.

[0061] Based on the fee reasonableness assessment report, the optimal fee scheme is selected to obtain the final fee decision, and a standardized electronic prescription is automatically generated based on the final fee decision and the precise medication change plan to obtain electronic prescription data;

[0062] The electronic prescription data, the wound parameter feature vector, the wound classification assessment matrix, the precise dressing change plan, and the charge reasonableness assessment report are integrated and archived to obtain a standardized medical service file, which includes wound assessment records, dressing change plans, charge basis, and treatment recommendations.

[0063] Preferably, the standardized wound image dataset, the optimized image data, the precise wound contour map, the wound parameter feature vector, the wound classification and evaluation matrix, the precise dressing change plan, the dynamic charging model, the charging rationality assessment report, and the standardized medical service file are all stored in a secure medical data platform, forming a complete wound treatment information chain. The wound treatment information chain uses blockchain technology to ensure data integrity and immutability, and the secure medical data platform has a hierarchical access control mechanism.

[0064] The technical effects and advantages of the wound recognition and payment system based on precision medicine of the present invention are as follows:

[0065] This invention significantly improves the objectivity and standardization of wound management by establishing a precision medicine wound identification and billing system, solving the problem of significant subjective differences in traditional assessments. The system achieves full transparency from assessment to billing, enhancing doctor-patient trust and reducing medical disputes. Through scientifically sound pricing standards and detailed explanation mechanisms, patients can clearly understand the composition of medical costs, improving satisfaction with medical services. Simultaneously, accurate resource allocation prediction reduces waste of medical resources, lowers medical costs, and improves the operational efficiency of medical institutions. Standardized treatment procedures and data management not only facilitate medical quality control but also provide reliable evidence for clinical research and medical insurance policy formulation. Personalized treatment plans significantly improve wound healing outcomes, shorten patient recovery periods, and alleviate patient suffering and financial burden. Furthermore, the system's data security mechanisms protect patient privacy and comply with modern medical ethics requirements. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of a wound recognition and payment system based on precision medicine according to the present invention;

[0067] Figure 2 This is a detailed flowchart illustrating the steps involved in obtaining a precise contour map of the wound according to the present invention. Detailed Implementation

[0068] 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.

[0069] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0070] This application provides a wound recognition and billing system based on precision medicine. The execution entities of the precision medicine-based wound recognition and billing system include, but are not limited to, medical devices, data processing terminals, hospital information systems, cloud servers, etc., which can be considered general computing nodes of this application. The data processing terminal includes, but is not limited to, at least one of a medical image processing system, a wound analysis system, and a medical billing management system.

[0071] Please see Figure 1 This invention provides a wound recognition and payment system based on precision medicine, comprising:

[0072] The image acquisition and standardization module is used to acquire multi-angle digital images of the wound and perform standardization processing to obtain a standardized image dataset of the wound.

[0073] The image enhancement and noise reduction module is used to enhance and reduce noise in a standardized wound image dataset to obtain optimized image data.

[0074] The boundary recognition and segmentation module is used to accurately identify and segment wound boundaries based on optimized image data using deep learning algorithms, thereby obtaining an accurate wound contour map.

[0075] The parameter measurement and reconstruction module is used to automatically measure the area, length and depth of the wound and perform three-dimensional reconstruction based on the accurate contour map of the wound, so as to obtain the wound parameter feature vector.

[0076] The wound type identification and assessment module is used to intelligently identify wound types and assess severity based on wound parameter feature vectors, and obtain a wound classification assessment matrix.

[0077] The matching and consumable prediction module is used to match dressing types and predict consumables based on the wound classification assessment matrix to obtain an accurate dressing plan.

[0078] The billing calculation and verification module is used to perform personalized billing calculation and transparency verification based on the precise medication change plan, resulting in a dynamic billing model.

[0079] The analysis and record generation module is used to compare and analyze historical data based on the dynamic charging model to obtain a charging rationality assessment report; based on the charging rationality assessment report and the precise medication change plan, it automatically generates medication change prescriptions and charging details to obtain standardized medical service records; the various modules are connected by wired and / or wireless means to realize data transmission between modules.

[0080] This invention establishes a foundation of wound data through high-definition image acquisition and standardized processing, ensuring data quality and consistency. Advanced image enhancement and noise reduction technologies improve image quality, creating conditions for accurate identification. Deep learning algorithms are applied to achieve precise identification and segmentation of wound boundaries, overcoming the limitations of traditional manual measurement. Key parameter features of the wound are obtained through precise measurement and 3D reconstruction, providing comprehensive quantitative assessment data. Machine learning is combined to achieve intelligent wound type identification and severity assessment, forming a scientific classification and assessment matrix. Based on the assessment results, precise dressing change type matching and consumable prediction are performed, enabling precise allocation of medical resources. A personalized billing calculation and transparency verification mechanism is constructed to ensure the rationality and explainability of charges. Standardized medical service records are generated through historical data comparison and rationality analysis, providing support for medical decision-making and quality management.

[0081] In this embodiment of the invention, the image acquisition and standardization module is used to acquire multi-angle digital images of the wound and perform standardization processing to obtain a standardized wound image dataset, specifically for:

[0082] A medical-grade high-definition camera under standard light conditions was used to acquire multi-angle images of the wound to obtain a set of original wound images. The original wound image set was then subjected to illumination equalization processing to obtain an illumination-corrected image.

[0083] Color calibration and standard scale embedding are performed on the illumination-corrected image to obtain a scale-normalized image. Viewpoint correction is then performed based on the scale-normalized image to obtain a viewpoint-consistent image.

[0084] The image resolution of the viewpoint-consistent image is normalized to obtain a standardized image, and the metadata of the standardized image is annotated to obtain an annotated image data.

[0085] Spatial registration and multi-angle fusion are performed on the labeled image data to obtain a multi-dimensional view model. The multi-dimensional view model is then converted to a standard format image.

[0086] Standardized wound image datasets are obtained by associating and storing standard format images with patient medical information.

[0087] In this embodiment, a medical-grade high-definition camera (resolution no less than 4000×3000 pixels) equipped with a standard color temperature (5500K) LED ring light source is used to acquire wound images at fixed distances and multiple angles (0°, 45°, 90°, etc.) to ensure coverage of the entire wound and its detailed areas. A standard color chart and length reference scale are placed in the images to form a set of original wound images. An adaptive illumination equalization algorithm (such as the Retinex algorithm or multi-scale adaptive histogram equalization) is applied to the acquired original wound image set to eliminate shadows and highlights, balance the overall image brightness, improve the visibility of details in dark areas, and generate an illumination-corrected image. Color calibration is performed using the standard color chart in the images, and a color management system (CMS) is applied to map the image colors to the standard sRGB or Adobe RGB color space to ensure color consistency and accuracy under different devices and lighting conditions. Simultaneously, the conversion ratio between pixels and actual lengths is calculated based on the reference scale in the images, and the standard scale information is embedded into the image metadata to generate a scale-normalized image. Based on feature points and reference markers in the scaled images, a perspective transformation algorithm is applied to correct the viewing angle, converting wound images taken from tilted angles into standard top-down views. This ensures that images taken from different angles have a consistent viewing angle, generating perspective-consistent images. All perspective-consistent images are then uniformly adjusted to a predetermined standard resolution (e.g., 4096×3072 pixels). Bicubic interpolation or super-resolution algorithms are applied to ensure image quality, while image cropping and boundary padding are performed to ensure the wound's position and proportion within the image are consistent, generating standardized images. Comprehensive metadata annotations are added to the standardized images, including basic patient information (anonymized to protect privacy), shooting time, equipment parameters, lighting conditions, viewing angle information, scale data, and a preliminary description of the wound. This metadata is stored in DICOM or a custom XML format, generating annotated image data. Feature point matching and image registration techniques (such as SIFT, SURF, or ORB algorithms) are used to spatially align labeled image data taken from different angles, establishing pixel-level correspondences. Then, multi-view geometry and image fusion techniques (such as weighted averaging, gradient domain fusion, or wavelet fusion) are employed to integrate multi-angle image information, generating a multi-dimensional view model with enhanced details and stereoscopic information. This multi-dimensional view model is converted to a standard medical imaging format (such as DICOM) or a common image format (such as lossless compressed TIFF or PNG) to ensure data compatibility and long-term preservation value, while retaining all metadata and scale information, generating standard format images. These standard format images are then linked to medical information in the patient's electronic medical record system (such as medical history, previous wound treatment records, and relevant test results). Secure database technology is used to store this linked information, ensuring data integrity and access security, ultimately forming a standardized wound image dataset, providing a high-quality data foundation for subsequent analysis.

[0088] In this embodiment of the invention, the image enhancement and noise reduction module is used to enhance and reduce the noise of a standardized wound image dataset to obtain optimized image data, specifically for:

[0089] An adaptive histogram equalization algorithm was applied to a standardized wound image dataset to obtain a contrast-enhanced image. Then, non-local mean filtering was applied to the contrast-enhanced image to obtain a preliminary denoised image.

[0090] Wavelet transform is applied to the initially denoised image to remove high-frequency noise, resulting in a frequency-domain optimized image. Edge-preserving smoothing is then applied to the frequency-domain optimized image to obtain a detail-enhanced image.

[0091] Image layer analysis is performed on the detail-enhanced image to obtain layered feature maps, and targeted image enhancement is performed based on the layered feature maps to obtain a feature-highlighted image;

[0092] Advanced noise removal is performed on feature-highlighting images using a deep learning denoising network to obtain high-quality denoised images. The high-quality denoised images are then evaluated for image quality to obtain a quality score table.

[0093] The image is reprocessed based on the optimal processing parameters selected using a quality scoring table to obtain optimized image data.

[0094] In this embodiment, the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm is applied to the standardized wound image dataset. This algorithm enhances image contrast through local region histogram equalization while limiting the contrast enhancement to prevent noise amplification. Different contrast parameters are used for different regions of the wound (such as granulation tissue, necrotic tissue, and epithelial tissue) to enhance the distinguishability of various tissues, generating a contrast-enhanced image. The contrast-enhanced image is then processed using a Non-Local Mean (NLM) filtering algorithm. This algorithm effectively removes noise while preserving image structure and texture details by searching for similar regions in the image and performing a weighted average. It is particularly suitable for processing subtle textures and tissue boundaries in wound images. Algorithm parameters (such as search window size and similarity threshold) are automatically adjusted according to the wound type to generate a preliminary denoised image. Finally, a multi-scale wavelet transform (such as bioorthogonal wavelet or curvelet transform) is applied to the preliminary denoised image to decompose the image into different frequency sub-bands. Soft or hard thresholding is used to selectively remove noise from the high-frequency sub-bands while preserving important edge and texture information. Coefficients in the low-frequency sub-bands are adjusted to enhance the basic structure. The image is then reconstructed to generate a frequency-optimized image. Edge-preserving smoothing algorithms (such as bilateral filtering, guided filtering, or anisotropic diffusion filtering) are applied to frequency-domain optimized images. These algorithms can smooth homogeneous regions while preserving and enhancing edge information, making them particularly suitable for processing wound boundaries and transition regions between different tissue types. Algorithm parameters (such as spatial domain and range parameters, and the number of iterations) are automatically adjusted according to wound characteristics to generate detailed enhanced images. Image layering analysis techniques are then applied to the detailed enhanced images. Through color space conversion (such as RGB to HSV or Lab) and clustering algorithms (such as K-means or mean shift), the wound region is divided into different tissue layers (such as granulation layer, fibrin layer, necrosis layer, etc.). Feature descriptors are extracted for each layer to form a layered feature map, which visually displays the tissue composition and distribution of the wound. Based on the layered feature map, targeted enhancement processing is applied to different tissue layers. For example, the red channel contrast is enhanced for granulation tissue, texture details are enhanced for the fibrin layer, and boundary clarity is enhanced for necrosis tissue. At the same time, color saturation and brightness are adjusted to highlight tissue differences, generating feature-highlighted images that make different types of tissue easier to identify and distinguish. Advanced noise removal of feature-highlighted images can be achieved by applying pre-trained deep learning denoising neural networks (such as DnCNN, FFDNet, or Noise2Noise architecture). These networks are trained on a large number of medical images and can identify and remove complex noise patterns while preserving medically important details. The network parameters are automatically adjusted according to the wound type and image quality to generate high-quality denoised images.A comprehensive image quality assessment is performed on high-quality denoised images, including objective metrics (such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM)) and specialized evaluation metrics for medical images (such as diagnostic relevance index, detail preservation, and edge integrity). The overall score reflects the image's practical value in clinical applications, forming a quality score table. Based on the evaluation results in the quality score table, intelligent parameter optimization algorithms (such as Bayesian optimization or genetic algorithms) are used to automatically select the optimal combination of processing parameters, including contrast enhancement parameters, filter intensity, wavelet threshold, and edge preservation parameters. The original image is then reprocessed to ensure optimal image quality and clinical usability, ultimately generating optimized image data that provides high-quality input data for subsequent wound boundary recognition and segmentation.

[0095] In the embodiments of the present invention, see Figure 2 This is a detailed flowchart illustrating the steps involved in obtaining a precise wound contour map. The boundary recognition and segmentation module is used to accurately identify and segment wound boundaries based on optimized image data using deep learning algorithms, resulting in a precise wound contour map. Specifically, it is used for:

[0096] A pre-trained convolutional neural network is applied to the optimized image data to perform preliminary localization of the wound region, resulting in a candidate region proposal map. A pixel-level semantic segmentation network is then applied to the candidate region proposal map to obtain a preliminary segmentation mask.

[0097] An active contour model is applied to the initial segmentation mask to refine the boundaries, resulting in a fine boundary map. A region growing algorithm is then applied to the fine boundary map to process the fuzzy boundary regions, resulting in a complete contour map.

[0098] Conditional random fields are applied to optimize spatial consistency of the complete contour map to obtain a spatially optimized contour. Multi-scale feature fusion is then performed on the spatially optimized contour to obtain a validated contour map.

[0099] The validated contour map is constrained and corrected by medical expert knowledge rules to obtain the clinical validation contour. The boundary coordinates of the clinical validation contour are extracted and vectorized to obtain contour vector data.

[0100] The contour vector data is overlaid on the original image, and boundary markers and measurement reference points are added to obtain a precise contour map of the wound.

[0101] In this embodiment, a pre-trained deep convolutional neural network (such as Faster R-CNN, YOLO v4, or RetinaNet) is used to quickly scan optimized image data, identify and locate regions that may contain wounds. The network is trained on a large number of labeled wound images, enabling it to adapt to wounds of different shapes, sizes, and locations. It outputs rectangular bounding boxes containing the wounds and confidence scores, forming a candidate region proposal map. A high-precision pixel-level semantic segmentation network (such as U-Net, DeepLab v3+, or Mask R-CNN) is then applied to the candidate region proposal map. These networks are specifically designed for medical image segmentation tasks, capable of classifying each pixel and accurately distinguishing wound regions from normal skin. The network employs an encoder-decoder structure and skip connections to preserve spatial details, outputting a binary mask of the wound region, forming a preliminary segmentation mask. Using the initial segmentation mask as initialization conditions, active contour models (such as the level set method or the Snake algorithm) are applied for boundary refinement. These models, through the principle of energy minimization, allow the contour to gradually evolve towards the true boundary of the wound, while considering image gradients, curvature constraints, and regional statistical characteristics. This allows them to adapt to complex wound boundary shapes and generate a refined boundary map. For any remaining blurred or uncertain boundary regions in the refined boundary map, an adaptive region growing algorithm is applied. Using definite boundary points as seeds, the region is gradually expanded based on pixel similarity (such as color, texture, and gradient). Algorithm parameters (such as growth threshold and similarity measure) are automatically adjusted based on local image features to fill boundary gaps and uncertain regions, generating a complete contour map. Conditional Random Field (CRF) post-processing is applied to the complete contour map to optimize the spatial consistency of the segmentation results. CRF considers the spatial relationships and appearance similarities between pixels, refining boundary details, eliminating isolated regions and small holes, and is particularly suitable for processing subtle transition regions at the wound edge, generating a spatially optimized contour. Multi-scale feature fusion verification is performed on the spatially optimized contour. Edge detection results at different resolutions (such as Canny, Sobel, or LoG operators) and texture analysis results are combined to ensure the consistency and accuracy of the boundary at different scales. Scale conflicts are resolved through a voting mechanism or weighted fusion to improve the reliability of boundary localization, generating a verified contour map. The verified contour map is compared with a medical expert knowledge rule base, and a rule-based constraint correction system is applied. These rules include wound morphological features (such as boundary smoothness, concavity / convexity, and symmetry), histological features (such as typical distribution of different tissue types), and clinical experience (such as common morphologies of wounds in specific locations). Boundary areas that do not conform to medical knowledge are adjusted according to these rules to generate a clinically verified contour.For clinical validation of the wound contour, boundary tracking algorithms (such as contour tracing or boundary chain code extraction) are applied to extract the precise coordinate sequence of the boundary. Then, the Douglas-Peucker algorithm or B-spline curve fitting is used for vectorization, converting the pixel-based boundary into a mathematically described smooth curve, reducing data redundancy while preserving key boundary features, generating contour vector data. The contour vector data is overlaid on the original optimized image, and the wound boundary is marked with high-contrast colored lines (such as red or green). Key measurement reference points (such as the two endpoints of the maximum length, feature points, and boundary inflection points) are added to the boundary, and size scales and direction indicators are embedded in the image to form a precise wound contour map, visually displaying the precise range and morphological characteristics of the wound, providing a foundation for subsequent parameter measurement and 3D reconstruction.

[0102] In this embodiment of the invention, the parameter measurement and reconstruction module is used to automatically measure the area, length, and depth of the wound based on a precise contour map and to perform three-dimensional reconstruction to obtain a wound parameter feature vector. Specifically, it is used for:

[0103] The pixel area of ​​the wound is calculated based on the precise contour map of the wound and converted to the actual area using a scale bar to obtain precise area data. The length of the wound is measured along the maximum extension direction of the wound to obtain the length parameter.

[0104] A wound depth map is constructed using multi-angle image information to obtain a depth distribution map. The average and maximum depths of the wound are then calculated based on the depth distribution map to obtain a set of depth parameters.

[0105] A three-dimensional mesh model of the wound is constructed based on the accurate contour map and depth parameter set of the wound to obtain a three-dimensional wound model. The volume and surface area of ​​the three-dimensional wound model are then calculated to obtain the three-dimensional parameter data.

[0106] Morphological analysis was performed on the precise contour map of the wound to extract morphological features such as perimeter, ellipticity, and complexity, resulting in a morphological feature set. Tissue type identification and area proportion analysis were performed on the wound interior to obtain tissue feature data.

[0107] By integrating precise area data, length parameters, depth parameter sets, three-dimensional parameter data, morphological feature sets, and tissue feature data, a wound parameter feature vector is obtained.

[0108] In this embodiment, a pixel counting method is used to calculate the total number of pixels contained within the wound contour. Then, combined with a standard scale bar in the image (embedded through pre-standardization processing), the number of pixels is converted into actual area (square centimeters or square millimeters). The conversion process considers the angle correction between the image plane and the wound surface to ensure measurement accuracy and generate precise area data. The two farthest points on the wound contour are identified as the endpoints of the major axis, and the Euclidean distance between these two points is calculated and converted into actual length (centimeter or millimeter) using the scale bar. Simultaneously, the maximum width perpendicular to the major axis is identified, and the aspect ratio is calculated. These measurement results constitute the length parameter. Using wound images taken from different angles, stereo vision techniques (such as structured light, binocular stereo matching, or multi-view stereo reconstruction) are applied to construct the depth information of the wound. The algorithm calculates the relative depth of each pixel by identifying the disparity of corresponding points, generating a depth distribution map representing the three-dimensional morphology of the wound. Based on the depth distribution map, the average depth, maximum depth, depth standard deviation, and depth distribution histogram of the wound are calculated. These indicators comprehensively describe the depth characteristics of the wound and are of great significance for assessing the severity of the wound and the difficulty of treatment, forming a depth parameter set. By combining a precise wound contour map and a depth parameter set, a 3D mesh model of the wound is constructed using 3D surface reconstruction algorithms (such as Poisson surface reconstruction, Marcin cube, or level set methods). The model uses triangular or quadrilateral meshes to represent the geometry of the wound surface, including precise depth variations and surface undulations, generating a 3D wound model. Computational geometry algorithms are then applied to the 3D wound model to calculate the wound volume (cubic centimeters or cubic millimeters) and actual surface area (square centimeters or square millimeters considering surface curvature and undulations). These parameters more accurately reflect the true size of the wound than the planar projected area, especially for wounds with uneven depths, forming three-dimensional parametric data. Digital image processing techniques are used to perform morphological analysis on the precise wound contour map, calculating the perimeter (actual length of the contour), roundness (similarity to a circle of equal area), ellipticity (similarity to the best-fit ellipse), convexity (ratio to its convex hull), irregularity (fractal dimension of the boundary), and complexity (standard deviation of boundary curvature variation). These parameters quantify the shape characteristics of the wound, forming a morphological feature set. Deep learning image segmentation networks (such as FCN, SegNet, or DeepLab) are used to identify tissue types within the wound, dividing the wound area into different tissue types (such as granulation tissue, fibrin tissue, necrotic tissue, epithelial tissue, etc.), calculating the area of ​​each type of tissue and its percentage of the total area, and analyzing tissue distribution patterns and boundary features. This information is crucial for assessing the wound healing stage and developing treatment plans, forming tissue feature data.Precise area data, length parameters, depth parameter sets, three-dimensional parameter data, morphological feature sets, and tissue feature data are integrated into a unified data structure to form a wound parameter feature vector that comprehensively describes the geometry and tissue characteristics of the wound. This vector contains quantitative measurement results and qualitative feature descriptions, providing a scientific basis for subsequent wound type identification and severity assessment.

[0109] In this embodiment of the invention, the wound type discrimination and assessment module is used to intelligently discriminate wound types and assess severity based on wound parameter feature vectors, obtaining a wound classification assessment matrix, specifically used for:

[0110] A multi-class machine learning algorithm is applied to the feature vector of wound parameters to obtain a preliminary judgment result of wound type. Based on the preliminary judgment result of wound type, the wound type knowledge base is queried to obtain the standard wound type definition.

[0111] Wound severity scores are calculated based on standard wound type definitions and wound parameter feature vectors to obtain severity assessment indicators. Healing difficulty is then predicted based on these indicators to generate a healing assessment report.

[0112] Anomaly detection is performed on the feature vector of wound parameters to identify special wound types, special type labels are obtained, and processing priority weights are set according to the special type labels to obtain a priority coefficient table;

[0113] The severity assessment indicators, healing assessment reports, and priority coefficient tables are mapped to the clinical treatment guideline rule set to obtain a list of clinical intervention recommendations. Based on the list of clinical intervention recommendations, the required dressing change resource level is determined to obtain a resource demand table.

[0114] By integrating the standard wound type definition, severity assessment indicators, healing assessment report, priority coefficient table, and resource requirement table, a wound classification assessment matrix is ​​obtained.

[0115] In this embodiment, the wound parameter feature vector is input into a multi-classification machine learning model (such as random forest, support vector machine, gradient boosting decision tree, or deep neural network) trained on a large number of clinical cases. The model identifies the type of wound based on the pattern of the feature vector, such as pressure injury, diabetic foot ulcer, venous ulcer, arterial ulcer, mixed ulcer, burn wound, etc., and outputs the probability distribution and confidence score of each type to form a preliminary wound type identification result. The preliminary wound type identification result is matched with the system's built-in wound type knowledge base. The knowledge base contains structured information such as standard definitions, typical features, pathogenesis, evolution patterns, and common complications of various wounds, and is maintained and updated by medical experts. The query result outputs the standard wound type definition that best matches the current wound, including precise medical terminology description and ICD code. Based on standard wound type definitions and wound parameter feature vectors, a professional scoring system (such as Wagner classification, NPUAP classification, PUSH score, or DESIGN-R score) is applied to calculate the wound severity score. The scoring system selects appropriate assessment criteria according to the wound type, considering factors such as area, depth, tissue type, infection status, and exudate volume, generating standardized severity assessment indicators, including numerical scores and grading results. Based on the severity assessment indicators and wound parameter feature vectors, a machine learning prediction model (such as a survival analysis model, Cox proportional hazards model, or deep learning time series prediction model) is applied to predict the wound healing difficulty and healing time. The model is trained based on historical wound healing data, considering wound characteristics, patient conditions, and treatment plans, outputting a healing difficulty rating, estimated healing time, and healing risk factor analysis, forming a healing assessment report. Anomaly detection algorithms (such as isolated forest, single-class SVM, or autoencoder reconstruction error detection) are applied to the feature vector of wound parameters to identify abnormal features that do not conform to common wound patterns, such as signs of malignant transformation, rare lesion characteristics, and special infection manifestations. These features may indicate that the wound requires special treatment or specialist consultation. The detection results output abnormal feature descriptions and abnormality severity scores, forming special type labels. Based on the special type labels and severity assessment indicators, priority weights for wound treatment are set. The weights consider factors such as the urgency of the wound (e.g., infection risk, rate of deterioration), treatment sensitivity, and resource requirements. A multi-criteria decision-making method is used to calculate a comprehensive priority score, forming a priority coefficient table to guide the rational allocation of medical resources. The severity assessment indicators, healing assessment reports, and priority coefficient tables are mapped and matched with a set of clinical treatment guidelines. The rule set contains evidence-based wound treatment guidelines and best practice recommendations. Based on the characteristics of the current wound and the assessment results, the system automatically matches applicable clinical intervention measures, including debridement methods, dressing material selection, antibiotic use recommendations, and follow-up frequency, forming a list of clinical intervention recommendations.The system analyzes the required medical resource levels based on the clinical intervention recommendation list, including the professional level requirements of medical staff, special equipment needs, types and quantities of consumables, and estimated treatment time. According to the complexity and specialization of resource requirements, the system categorizes and quantifies these requirements, forming a resource requirement table. This table provides a basis for subsequent dressing change plan design and cost estimation. The system integrates standard wound type definitions, severity assessment indicators, healing assessment reports, priority coefficient tables, and resource requirement tables into a multi-dimensional data structure, constructing a wound classification assessment matrix. This matrix comprehensively describes the wound type, severity, healing expectation, treatment priority, and resource requirements, providing a scientific basis for precision medicine decision-making.

[0116] In this embodiment of the invention, the matching and consumable prediction module is used to perform dressing change type matching and consumable prediction based on the wound classification assessment matrix to obtain an accurate dressing change plan, specifically for:

[0117] The dressing change type is automatically determined based on the area, length and depth data of the wound classification assessment matrix, and a preliminary dressing change type judgment result is obtained. Based on the preliminary dressing change type judgment result, the standard dressing change type definition library is queried to obtain the standard dressing change specification.

[0118] A secondary judgment is made on the tissue characteristics and severity assessment indicators in the wound classification assessment matrix to obtain auxiliary judgment correction results. The auxiliary judgment correction results are then integrated with the initial judgment results of dressing type to obtain the final dressing type confirmation.

[0119] Based on the final confirmation of dressing type and the wound classification assessment matrix, the required types and quantities of medical consumables are predicted, resulting in a consumables list estimate table. The consumables cost is then calculated based on the consumables list estimate table, yielding material cost accounting data.

[0120] Based on the final medication change type, the required medical staff hours and professional levels are determined, and the human resource demand forecast is obtained. Based on the human resource demand forecast, the labor cost is calculated, and the labor cost accounting data is obtained.

[0121] By integrating material cost accounting data, labor cost accounting data, and the final dressing change type confirmation, a precise dressing change plan can be obtained.

[0122] In this embodiment, the area, length, and depth data in the wound classification assessment matrix are analyzed. A decision tree algorithm is applied to automatically determine the applicable dressing change type level, such as simple dressing change, complex dressing change, and special dressing change. The judgment criteria are based on the standardized dressing change classification system of medical institutions, considering factors such as wound size, depth, and complexity. The initial judgment result of the dressing change type is output, including the dressing change type code and preliminary description. The initial judgment result of the dressing change type is matched with the system's built-in standard dressing change type definition library. The definition library contains structured information such as standard specifications, technical requirements, applicable conditions, and operation procedures for various dressing change operations. It is regularly updated and maintained by medical professionals. The query result outputs the standard dressing change specification that best matches the current wound, providing standardized guidance for subsequent treatment. Further analysis of the tissue characteristics (such as the proportion of granulation tissue, presence of necrotic tissue, and signs of infection) and severity assessment indicators in the wound classification assessment matrix is ​​performed. A rule-based expert system is applied for secondary judgment. The expert system contains a large number of judgment rules summarized from clinical experience, which can identify wound conditions that require special treatment (such as the need for debridement, antibiotic treatment, or special dressings), and output suggestions for correcting the initial judgment result, forming an auxiliary judgment correction result. The auxiliary judgment correction results and the initial judgment results of the dressing change type are integrated using a weighted fusion method. The weight allocation considers the clinical importance and predictive reliability of each indicator, resolves potential conflicts, and forms a consistent judgment. Finally, the most suitable dressing change type for the current wound condition is determined, including detailed operation type, required technical level, and special precautions, forming the final dressing change type confirmation. Based on the final dressing change type confirmation and the wound classification assessment matrix (especially area, depth, and tissue characteristic data), a predictive algorithm is applied to estimate the required types and quantities of medical consumables, including cleaning solutions, disinfectants, dressings (such as alginate dressings, hydrocolloid dressings, foam dressings, silver ion dressings, etc.), fixation materials (such as tape, bandages), and auxiliary supplies (such as gloves, instruments, containers, etc.). The predictive algorithm is trained based on historical dressing change records, considers the correlation pattern between wound characteristics and dressing change type, and generates a consumables list estimation table, detailing the name, specifications, and quantity of each type of consumable. Based on the consumables list and the medical institution's consumables price database, the total cost of all consumables required during dressing changes is calculated, including basic consumables costs, special dressing costs, and auxiliary material costs. The impact of bulk discounts and packaging specifications is also considered, resulting in detailed material cost accounting data to provide a basis for billing calculations. Based on the final dressing change type confirmation and wound complexity, the required medical staff hours and professional level requirements for completing the dressing change are predicted. The hourly work prediction considers the complexity of wound management, the number of operational steps, and special treatment needs. The professional level requirements consider technical difficulty and risk level. The prediction results output the required nurse / doctor level (e.g., junior nurse, senior nurse, specialist), number of nurses, and estimated working hours, forming a human resource demand forecast.Based on human resource demand forecasts and medical institutions' labor cost standards, the labor costs during dressing changes are calculated, including basic operating fees, technical difficulty surcharges, and special time period fees (such as nighttime and holiday surcharges). The differentiated fee standards for different levels of medical staff are also considered, forming labor cost accounting data to provide a complete basis for total cost calculation. Material cost accounting data and labor cost accounting data are integrated with the final dressing change type confirmation into a unified data structure to construct a precise dressing change plan. This plan includes detailed descriptions of dressing change types, operational procedure guidance, a list of required consumables, human resource allocation suggestions, and cost composition analysis, providing comprehensive dressing change guidance for medical staff and a scientific basis for fee calculation.

[0123] In this embodiment of the invention, the charge calculation and verification module is used to perform personalized charge calculation and transparency verification based on the precise medication change plan, thereby obtaining a dynamic charge model, specifically used for:

[0124] The basic fee is calculated based on the dressing type and consumable cost in the precision dressing change plan. The basic fee standard is obtained, and the complexity adjustment coefficient is calculated by combining the complexity index in the wound classification assessment matrix to obtain the adjusted fee.

[0125] The adjusted charges are adjusted according to medical insurance policies and rules and regional differences to obtain regional charging standards. Then, personalized adjustments are made based on the regional charging standards and the patient's specific circumstances to obtain personalized charging plans.

[0126] A cost-benefit analysis was conducted on the personalized pricing scheme to obtain a cost-benefit assessment report. Based on the cost-benefit assessment report, a pricing fairness verification mechanism was constructed to obtain the fairness verification results.

[0127] Based on the fairness verification results, a detailed fee explanation document is generated, resulting in a fee transparency document. A patient-friendly fee explanation interface is then designed based on the fee transparency document, resulting in a visual fee explanation.

[0128] A dynamic pricing model is constructed by integrating personalized pricing plans, cost-benefit assessment reports, and pricing transparency documents.

[0129] In this embodiment, based on the dressing change type (e.g., simple dressing change, complex dressing change, special dressing change) and detailed consumable cost data in the precision dressing change plan, the basic fee amount is calculated using the standard charging rules of medical institutions. The basic fee includes basic operation fees, consumable fees, and basic technical fees. The calculation process follows the regulations for medical service price management to ensure the compliance and completeness of the charging items, forming a basic fee standard. The complexity indicators in the wound classification assessment matrix, such as wound depth, tissue complexity, infection status, and special site coefficients, are analyzed. A weighted calculation method is applied to determine the complexity adjustment coefficient. This coefficient reflects the impact of wound treatment difficulty on the fee. The adjustment coefficient is applied to the technical fee portion of the basic fee to generate an adjusted fee that reflects the technical value of the medical service. The adjusted fee is matched with the local medical insurance policy rule database. Medical insurance reimbursement rules (e.g., reimbursement ratio, maximum limit, special material policies) and regional difference adjustment parameters (e.g., regional medical service price index) are applied for calculation to ensure that the fee complies with medical insurance policy requirements and regional medical price levels, outputting a regionalized fee standard that conforms to local standards. Based on the specific circumstances of patients (such as those with chronic diseases, elderly patients, low-income groups, and special populations) and the special policies of medical institutions (such as medical assistance, preferential policies, and membership systems), personalized adjustment rules are applied to adjust regional pricing standards in a targeted manner. This ensures that charges comply with regulations while taking into account the actual situation of patients, thus forming a personalized pricing plan. A cost-benefit analysis is conducted on the personalized pricing plan, calculating the cost recovery rate, profit margin, and sustainability indicators of medical services. Simultaneously, the cost-effectiveness of patient affordability and expected treatment outcomes is assessed. The analysis results include cost composition analysis, benefit prediction, and long-term economic impact assessment, forming a cost-benefit evaluation report to provide an economic basis for the rationality of charges. Based on the cost-benefit evaluation report, a multi-dimensional pricing fairness verification mechanism is constructed. Through horizontal comparison (comparing with similar services at similar medical institutions), vertical comparison (comparing with historical trends in pricing standards), and internal consistency testing (analyzing differences in charges for similar services among different patients), the fairness and rationality of charges are comprehensively evaluated. The verification results output a fairness score and potential problem analysis, forming a fairness verification result. Based on the fairness verification results, a detailed billing breakdown document is generated. This document includes a detailed list of billing items, calculation basis, explanations of adjustment factors, and policy references. It employs a clear structure and professional yet easy-to-understand language to ensure the transparency and explainability of the billing process, forming a billing transparency document that provides patients with detailed information for understanding charges. Based on this document, an intuitive and user-friendly billing explanation interface is designed. The interface uses graphical displays (such as pie charts and bar charts), comparative analysis, and concise explanations to translate professional medical terminology into easily understandable expressions, highlighting key billing items and adjustment factors, creating a visual explanation of charges to help patients clearly understand the billing structure.By integrating personalized pricing plans, cost-benefit assessment reports, and pricing transparency documents into a unified data model, a dynamic pricing model is constructed. This model can update pricing estimates in real time based on factors such as changes in wound condition, treatment progress, and policy adjustments, providing accurate, transparent, and reasonable pricing references for medical institutions and patients, while also providing data support for medical management decisions.

[0130] In this embodiment of the invention, the analysis and record generation module is used to perform historical data comparison and rationality analysis based on the dynamic charging model to obtain a charging rationality assessment report; based on the charging rationality assessment report and the precise medication change plan, it automatically generates a medication change prescription and charging details to obtain a standardized medical service record, specifically used for:

[0131] The dynamic charging model was compared and analyzed with historical charging data for similar wounds to obtain longitudinal comparison results, and it was also compared with the charging data for similar services in medical institutions in the same region to obtain horizontal comparison results.

[0132] Based on the results of longitudinal and horizontal comparisons, a toll deviation index is calculated to obtain a toll deviation assessment table. Then, a reasonableness judgment standard is established based on the toll deviation assessment table to obtain a reasonableness judgment framework.

[0133] The dynamic charging model is evaluated using a reasonableness assessment framework to obtain the results of the charging reasonableness analysis. Based on the results of the charging reasonableness analysis, charging recommendations are formed, resulting in a charging reasonableness assessment report.

[0134] Based on the fee reasonableness assessment report, the optimal fee plan is selected to obtain the final fee decision. Based on the final fee decision and the precise medication change plan, a standardized electronic prescription is automatically generated to obtain electronic prescription data.

[0135] By integrating and archiving electronic prescription data, wound parameter feature vectors, wound classification assessment matrices, precise dressing change plans, and fee reasonableness assessment reports, standardized medical service files are obtained, which include wound assessment records, dressing change plans, fee basis, and treatment recommendations.

[0136] In this embodiment, historical charging records for similar wounds (similar in type, severity, and treatment) are extracted from a medical database. Time series analysis is applied to compare the consistency between the current dynamic charging model and historical charging trends, analyze the rationality and influencing factors of charging changes, and output charging change rate, deviation, and analysis of the causes of change, forming a longitudinal comparison result. Charging data for similar wound treatment services provided by other medical institutions in the same region (such as the same city or province) are collected. Statistical analysis methods are applied to compare the differences in charging levels among institutions, considering the influence of factors such as medical institution level, service quality, and geographical location, and outputting relative charging level, market positioning, and competitiveness analysis, forming a horizontal comparison result. Based on the longitudinal and horizontal comparison results, a charging deviation index is calculated. This index quantifies the degree of deviation between the current charging scheme and historical trends and market levels. The index calculation considers time factors (such as inflation and policy changes) and spatial factors (such as regional differences and institutional characteristics), and analyzes the composition and main sources of deviation, forming a charging deviation assessment table that visually displays the relative position of charges. Based on the fee deviation assessment table and medical service pricing theory, a scientific rationality evaluation standard is established. This standard includes dimensions such as cost coverage principle, value reflection principle, affordability principle, and sustainability principle. Each dimension has quantitative indicators and acceptable ranges, constructing a rationality evaluation framework to provide an objective basis for judging the rationality of fees. The rationality evaluation framework is applied to the dynamic fee model for multi-dimensional evaluation, calculating scores for each indicator and an overall rationality score, identifying potential unreasonable factors and optimization space. The evaluation results include detailed indicator analysis and problem diagnosis, forming a fee rationality analysis result. Based on the fee rationality analysis result, targeted fee adjustment suggestions are generated, including the direction, magnitude, and rationale for the adjustment, while considering feasibility and impact prediction, forming a systematic fee rationality evaluation report to provide professional support for fee decision-making. Based on the suggestions and analysis in the fee rationality evaluation report, the optimal fee scheme balancing the medical institution's cost recovery, patient affordability, and medical insurance policy requirements is selected. The scheme selection adopts a multi-objective decision-making method, considering the balance of interests of all parties and long-term sustainability, forming the final fee decision and determining the actual implemented fee standards and items. Based on the final billing decision and precise dressing change plan, a standardized electronic prescription that conforms to medical standards is automatically generated. The prescription includes the patient's basic information, wound diagnosis results, details of the treatment plan, details of the drugs and consumables used, key points of operation and precautions, etc. Standard medical terminology and coding are used to ensure the standardization and feasibility of the prescription, and electronic prescription data is generated to provide direct guidance for clinical implementation.The electronic prescription data is systematically integrated with the previously generated wound parameter feature vectors, wound classification assessment matrices, precise dressing change plans, and reasonableness assessment reports on charges to construct a structured medical service file. The file adopts a standardized data format and organizational structure to ensure the integrity, consistency, and traceability of information. It includes a complete assessment record of the wound, a detailed dressing change plan, transparent charging basis, and scientific treatment recommendations, forming a standardized medical service file that provides a comprehensive basis for patient management, medical quality control, and medical insurance review.

[0137] In this embodiment of the invention, the standardized wound image dataset, optimized image data, precise wound contour map, wound parameter feature vector, wound classification assessment matrix, precise dressing change plan, dynamic charging model, charging rationality assessment report, and standardized medical service records are all stored in the secure medical data platform, forming a complete wound management information chain. The wound management information chain uses blockchain technology to ensure data integrity and immutability. The secure medical data platform has a hierarchical access control mechanism to protect patient privacy and data security.

[0138] In this embodiment, a dedicated secure medical data platform is established, employing a high-performance server cluster and distributed storage architecture to ensure efficient data processing and reliable storage. The platform is equipped with comprehensive network security measures, including firewalls, intrusion detection systems, and security audit systems, to prevent unauthorized access and data leakage. All data generated by the system is stored on this platform, including standardized wound image datasets, optimized image data, precise wound contour maps, wound parameter feature vectors, wound classification and assessment matrices, precise dressing change plans, dynamic billing models, billing rationality assessment reports, and standardized medical service records. This data is organized according to a standardized format, forming a complete wound management information chain, achieving end-to-end data flow from image acquisition to billing management. Blockchain technology is applied to securely manage the wound management information chain. Each data processing step and result forms a block, linked together in an immutable blockchain using cryptographic methods. Every operation and modification is recorded on the blockchain with a timestamp and operator identifier, ensuring data integrity, authenticity, and traceability, preventing unauthorized modification or forgery, making it particularly suitable for the secure management needs of medical data. A strict hierarchical access control mechanism is implemented on the secure medical data platform. Different data access permissions are assigned according to user roles (such as doctors, nurses, administrators, and patients) and responsibilities. This ensures that users can only access the minimum dataset required for their duties. All access operations undergo strict identity authentication (such as multi-factor authentication) and behavior auditing. The system automatically records all data access and operation logs, conducts regular security audits, and promptly detects and handles abnormal access behaviors, comprehensively protecting patient privacy and data security.

[0139] This invention acquires high-quality wound image data through a standardized image acquisition and processing process, applies deep learning algorithms to accurately identify and segment wound boundaries, automatically measures wound parameters and performs three-dimensional reconstruction, identifies wound type and assesses severity based on multi-dimensional features, achieves precise matching of dressing change plans and prediction of consumables, constructs a transparent and reasonable dynamic charging model, and generates standardized medical service records. The entire process forms a complete information chain, and blockchain technology ensures data security and immutability.

[0140] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0141] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] In the description of this invention, it should be understood that the terms "first", "second", etc. are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0143] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0144] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0145] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0146] All formulas in this manual are dimensionless and calculated numerically. 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 and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0147] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A wound recognition billing system based on precision medicine, characterized in that, The method comprises the following steps: An image acquisition and standardization module is used to acquire multi-angle digital images of a wound and perform standardization processing to obtain a standardized image dataset of the wound; An image enhancement and noise reduction processing module is used to perform enhancement and noise reduction processing on the standardized image dataset of the wound to obtain optimized image data; A boundary identification and segmentation module is used to perform accurate identification and segmentation of a wound boundary based on the optimized image data by applying a deep learning algorithm, including: applying a pre-trained convolutional neural network to the optimized image data to perform preliminary positioning of a wound area, to obtain a candidate region proposal image, and applying a pixel-level semantic segmentation network to the candidate region proposal image to obtain a preliminary segmentation mask; An active contour model is applied to the preliminary segmentation mask to perform fine adjustment of the boundary, to obtain a fine boundary image, and a region growing algorithm is applied to the fine boundary image to process a fuzzy boundary region, to obtain a complete contour image; A conditional random field is applied to the complete contour image to optimize spatial consistency, to obtain a spatially optimized contour, and multi-scale feature fusion verification is performed on the spatially optimized contour, to obtain a verified contour image; Medical expert knowledge rule constraint correction is performed on the verified contour image, to obtain a clinically verified contour, and boundary coordinate extraction and vectorization processing are performed on the clinically verified contour, to obtain contour vector data; The contour vector data is superimposed on the original image, and boundary markers and measurement reference points are added, to obtain a wound accurate contour image; A parameter measurement and reconstruction module is used to perform automatic measurement of area, length and depth and three-dimensional reconstruction based on the wound accurate contour image, including: calculating the wound pixel area based on the wound accurate contour image and combining a scale to perform actual area conversion, to obtain accurate area data, and measuring the wound length along the maximum extension direction of the wound, to obtain length parameters; A wound depth map is constructed using multi-angle image information, to obtain a depth distribution map, and the average depth and maximum depth of the wound are calculated according to the depth distribution map, to obtain a depth parameter set; A three-dimensional mesh model of the wound is constructed based on the wound accurate contour image and the depth parameter set, to obtain a three-dimensional wound model, and the volume and surface area of the three-dimensional wound model are calculated, to obtain stereoscopic parameter data; Morphological analysis is performed on the wound accurate contour image, to extract morphological features such as perimeter, ellipticity and complexity, to obtain a morphological feature set, and tissue type identification and area proportion analysis are performed on the wound interior, to obtain tissue feature data; The accurate area data, the length parameters, the depth parameter set, the stereoscopic parameter data, the morphological feature set and the tissue feature data are integrated, to obtain a wound parameter feature vector; A wound type discrimination and evaluation module is used to perform intelligent discrimination of the wound type and severity evaluation based on the wound parameter feature vector, to obtain a wound classification and evaluation matrix. The matching and consumable prediction module is used for dressing type matching and consumable prediction based on the wound classification evaluation matrix, and includes: automatically determining the dressing type according to the area, length and depth data of the wound classification evaluation matrix to obtain a dressing type preliminary determination result, and querying a standard dressing type definition library based on the dressing type preliminary determination result to obtain a standard dressing specification; The tissue characteristics and severity evaluation indexes in the wound classification evaluation matrix are subjected to secondary determination to obtain an auxiliary determination correction result, and the auxiliary determination correction result and the dressing type preliminary determination result are fused to obtain a final dressing type confirmation; The required medical consumable type and quantity are predicted according to the final dressing type confirmation and the wound classification evaluation matrix to obtain a consumable list estimation table, and the consumable cost is calculated according to the consumable list estimation table to obtain material cost accounting data; The required medical staff working hours and professional level are predicted based on the final dressing type confirmation to obtain human resource demand prediction, and the labor cost is calculated according to the human resource demand prediction to obtain labor cost accounting data; The material cost accounting data, the labor cost accounting data and the final dressing type confirmation are integrated to obtain a precise dressing scheme; The charging calculation and verification module is used for individualized charging calculation and transparency verification based on the precise dressing scheme to obtain a dynamic charging model; The analysis and archive generation module is used for historical data comparison and reasonableness analysis based on the dynamic charging model to obtain a charging reasonableness evaluation report; and a dressing prescription and a charging detail are automatically generated based on the charging reasonableness evaluation report and the precise dressing scheme to obtain a standardized medical service archive.

2. The precision medicine based wound identification billing system of claim 1, wherein, The multi-angle digital images of the wound are collected and standardized processed to obtain a wound standardized image dataset, including: A medical-grade high-definition camera under standard light source conditions is used to collect multi-angle images of the wound to obtain an original wound image group, and the original wound image group is subjected to light equalization processing to obtain light correction images; The light correction images are subjected to color calibration and standard scale embedding to obtain proportion standardized images, and the proportion standardized images are subjected to perspective correction to obtain perspective consistency images; The perspective consistency images are subjected to image resolution unification processing to obtain standardized images, and the standardized images are subjected to metadata labeling to obtain labeled image data; The labeled image data are subjected to spatial registration and multi-angle fusion to obtain a multi-dimensional view model, and the multi-dimensional view model is subjected to image format standardization conversion to obtain standard format images; The standard format images are stored in association with patient medical information to obtain a wound standardized image dataset.

3. The precision medicine based wound identification billing system of claim 2, wherein, The wound standardized image dataset is subjected to enhancement and noise reduction processing to obtain optimized image data, including: An adaptive histogram equalization algorithm is applied to the wound standardized image dataset to obtain contrast enhancement images, and the contrast enhancement images are subjected to non-local mean filtering to obtain preliminary noise reduction images; Applying wavelet transform to the preliminary noise reduction image to remove high-frequency noise to obtain a frequency domain optimization image, and performing edge-preserving smoothing processing on the frequency domain optimization image to obtain a detail enhancement image; Performing image layer analysis on the detail enhancement image to obtain a layer feature map, and performing targeted image enhancement according to the layer feature map to obtain a feature highlighting image; Applying a deep learning denoising network to the feature highlighting image to remove high-level noise and obtain a high-quality denoised image, and performing image quality evaluation on the high-quality denoised image to obtain a quality score table; Based on the quality score table, the optimal processing parameters are selected to reprocess the image to obtain optimized image data.

4. The precision medicine based wound identification billing system of claim 3, wherein, The wound parameter feature vector is used to intelligently identify the wound type and assess the severity, and a wound classification evaluation matrix is obtained, including: Applying a multi-classification machine learning algorithm to the wound parameter feature vector to obtain a preliminary wound type identification result, and querying a wound type knowledge base according to the preliminary wound type identification result to obtain a standard wound type definition; Based on the standard wound type definition and the wound parameter feature vector, a wound severity score is calculated to obtain a severity evaluation index, and a healing difficulty prediction is performed according to the severity evaluation index to obtain a healing evaluation report; Abnormal detection is performed on the wound parameter feature vector to identify special wound types, and a special type marker is obtained, and a processing priority weight is set according to the special type marker to obtain a priority coefficient table; The severity evaluation index, the healing evaluation report and the priority coefficient table are mapped with the clinical treatment guideline rule set to obtain a clinical intervention suggestion list, and the required dressing change resource level is determined according to the clinical intervention suggestion list to obtain a resource demand table; The standard wound type definition, the severity evaluation index, the healing evaluation report, the priority coefficient table and the resource demand table are integrated to obtain a wound classification evaluation matrix.

5. The precision medicine based wound identification billing system of claim 4, wherein, The individualized charging calculation and transparency verification are performed based on the precise dressing change scheme to obtain a dynamic charging model, including: According to the dressing change type and consumable cost in the precise dressing change scheme, a basic charge is calculated to obtain a basic charge standard, and a complexity adjustment coefficient is calculated in combination with the complexity index in the wound classification evaluation matrix to obtain an adjusted charge; The adjusted charge is applied to the medical insurance policy rules and regional difference adjustment to obtain a regionalized charging standard, and individualized adjustment is performed according to the regionalized charging standard combined with the special circumstances of the patient to obtain an individualized charging scheme; The cost-benefit analysis is performed on the individualized charging scheme to obtain a cost-benefit evaluation report, and a charging fairness verification mechanism is constructed according to the cost-benefit evaluation report to obtain a fairness verification result; Based on the fairness verification result, a charging detail explanation document is generated to obtain a charging transparency file, and a patient-friendly charging explanation interface is designed according to the charging transparency file to obtain a visual charging explanation; The individualized charging scheme, the cost-benefit evaluation report and the charging transparency file are integrated to build a dynamic charging model.

6. The precision medicine based wound identification billing system of claim 5, wherein, The history data comparison and reasonableness analysis based on the dynamic charging model are performed to obtain a charging reasonableness evaluation report; based on the charging reasonableness evaluation report and the precise dressing change scheme, a dressing change prescription and a charging detail are automatically generated to obtain a standardized medical service archive, including: The dynamic charging model is compared and analyzed with historical similar wound charging data to obtain a longitudinal comparison result, and is compared with similar service charging of medical institutions in the same region to obtain a horizontal comparison result; Based on the longitudinal comparison result and the horizontal comparison result, a charging deviation index is calculated to obtain a charging deviation evaluation table, and a reasonableness judgment standard is established according to the charging deviation evaluation table to obtain a reasonableness judgment framework; The dynamic charging model is evaluated by using the reasonableness judgment framework to obtain a charging reasonableness analysis result, and a charging suggestion is formed according to the charging reasonableness analysis result to obtain a charging reasonableness evaluation report; Based on the charging reasonableness evaluation report, an optimal charging scheme is selected to obtain a final charging decision, and a standardized electronic prescription is automatically generated according to the final charging decision and the precise dressing change scheme to obtain electronic prescription data; The electronic prescription data, the wound parameter feature vector, the wound classification evaluation matrix, the precise dressing change scheme and the charging reasonableness evaluation report are integrated and archived to obtain a standardized medical service archive, including wound evaluation records, dressing change schemes, charging basis and disposal suggestions.

7. The precision medicine based wound identification billing system of claim 6, wherein, The wound standardized image data set, the optimized image data, the wound precise contour map, the wound parameter feature vector, the wound classification evaluation matrix, the precise dressing change scheme, the dynamic charging model, the charging reasonableness evaluation report and the standardized medical service archive are all stored in a secure medical data platform to constitute a complete wound treatment information chain, the wound treatment information chain ensures data integrity and non-tamperability through blockchain technology, and the secure medical data platform is provided with a permission-based access control mechanism.

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