Auxiliary dressing, wound healing state collection method, wound healing state management method and device

Abstract

The invention discloses an auxiliary dressing, a wound healing state collecting method, a wound healing state managing method and wound healing state managing equipment, the auxiliary dressing comprises a dressing layer and a collecting device, and the collecting device comprises a flexible biosensor assembly arranged on a base layer, a flexible multi-dimensional scanning assembly and a flexible hyperspectral imaging assembly, the flexible multi-dimensional scanning assembly and the flexible hyperspectral imaging assembly are distributed at the same height or different heights of the middle layer and are arranged in a staggered mode in the middle layer in the direction perpendicular to the wound surface of the wound. The collection device is integrated into the dressing layer, the wound state collection method is optimized according to the arrangement position, the structure and the like of the collection device on the dressing layer, the healing state of the target wound is automatically evaluated, the risk of wound secondary infection caused by frequent uncovering of the dressing is reduced, and the wound healing efficiency is improved. Moreover, comprehensive analysis is carried out based on various wound data, so that the incredibility of single data is avoided, and the accuracy of wound healing management results is improved.

Description

Technical Field

[0001] This invention relates to the field of medical and nursing technology, and in particular to an auxiliary dressing, a method for collecting and managing wound healing status, and a device. Background Technology

[0002] Chronic wounds refer to wounds that, due to various causes, have not healed despite regular treatment for more than a month and show no tendency to heal. With an aging population and changing disease patterns, the incidence of chronic wounds is rising annually. In my country, the incidence rate is 0.17%, with diabetes being a major cause, resulting in 40 million cases of chronic wounds annually. Currently, the healing of these chronic wounds relies on the subjective judgment of medical personnel based on wound morphology, leading to strong experience dependence and insufficient convenience and accuracy. Therefore, there is an urgent need to apply efficient and precise methods to improve the assessment and treatment of chronic wounds and promote patient healing. Summary of the Invention

[0003] This invention provides an auxiliary dressing, a method for collecting and managing wound healing status, and an apparatus, which solves the technical problems mentioned above.

[0004] A first aspect of the present invention provides an auxiliary dressing, including a dressing layer and a data acquisition device for acquiring real-time wound status. The dressing layer includes a base layer and an intermediate layer. The data acquisition device includes a flexible biosensor component for acquiring wound bed environmental data, a flexible multidimensional scanning component for acquiring wound morphology data, and a flexible hyperspectral imaging component for acquiring wound biochemical index data. The flexible biosensor component is disposed in the base layer. The flexible multidimensional scanning component and the flexible hyperspectral imaging component are distributed at the same height or different heights in the intermediate layer and are staggered in the intermediate layer along a direction perpendicular to the wound surface.

[0005] A second aspect of the present invention provides a method for collecting wound healing status data, comprising the following steps: Step 1: Acquire initial hyperspectral imaging data of the target wound using the flexible hyperspectral imaging component; Step 2: Obtain the imaging influence parameter value of the auxiliary dressing, correct the initial hyperspectral imaging data according to the imaging influence parameter value, and generate target hyperspectral imaging data. The imaging influence parameter includes the structural information of the acquisition device and the dressing material and dressing thickness of the base layer and / or intermediate layer. Step 3: Generate biochemical index data of the target wound based on the target hyperspectral imaging data. The biochemical index data includes biochemical characteristic data and microenvironment state data of the target wound.

[0006] A third aspect of this invention provides a method for managing wound healing status, comprising the following steps: Step 21: Collect and preprocess the current status data of the target wound to construct an initial feature vector. The current status data includes wound bed environment data, morphological data and / or biochemical index data. Step 22: Normalize, temporally align, and fuse the initial feature vector to generate the target feature vector; Step 23: Invoke the pre-trained wound healing assessment model and generate the current healing status of the target wound based on the target feature vector to generate the corresponding wound management plan.

[0007] A fourth aspect of the present invention provides a wound healing status management device, including a control module and an auxiliary dressing connected to the control module, wherein the control module is used to perform the steps of a wound healing status management method.

[0008] The beneficial effects of this invention are as follows: This invention provides an auxiliary dressing, a method for collecting and managing wound healing status, and an apparatus. It integrates sensors and other data acquisition devices for obtaining wound status information into the dressing layer. Furthermore, the wound status acquisition method is optimized based on the placement and structure of the acquisition devices within the dressing layer. This allows for real-time, automatic monitoring and auxiliary management of the healing status of the target wound. During the wound healing process, there is no need to remove the dressing for inspection, improving the convenience of wound management and reducing the risk of secondary infection due to frequent dressing removal. Simultaneously, this invention performs comprehensive analysis based on multiple wound data points, avoiding the unreliability of single data points. This not only reduces reliance on medical personnel but also yields more accurate and effective test results.

[0009] To make the above-mentioned objects, features and advantages of the invention more apparent and understandable, preferred embodiments of the invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a scenario application diagram of the wound healing status management device provided in the embodiments of this application; Figure 2 This is a schematic diagram of the structure of the auxiliary dressing provided in the embodiments of this application; Figure 3This is a cross-sectional structural diagram of the dressing layer provided in an embodiment of this application; Figure 4 This is another cross-sectional structural diagram of the dressing layer provided in the embodiments of this application; Figure 5 This is a cross-sectional structural diagram of the auxiliary dressing provided in an embodiment of this application; Figure 6 This is another cross-sectional structural diagram of the auxiliary dressing provided in the embodiments of this application; Figure 7 This is a flowchart illustrating the wound healing status acquisition method provided in the embodiments of this application; Figure 8 This is a flowchart illustrating the wound healing status management method provided in the embodiments of this application; Figure 9 This is a schematic diagram of the wound healing status management device provided in the embodiments of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] In the description of the embodiments of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0015] In related technologies, wound healing status relies on the subjective judgment of medical personnel, which suffers from strong reliance on experience and insufficient convenience and accuracy. In view of this, embodiments of this application provide an auxiliary dressing, a method for collecting and managing wound healing status, and a management method and device. The method integrates sensors and other data acquisition devices for obtaining wound status into the dressing layer, and optimizes the wound status acquisition method based on the placement structure of the acquisition devices within the dressing layer. This allows for real-time, automatic monitoring and auxiliary management of the healing status of the target wound. During the wound healing process, there is no need to remove the dressing for inspection, improving the convenience of wound management and reducing the risk of secondary infection due to frequent dressing removal. Furthermore, this invention performs comprehensive analysis based on multiple wound data, avoiding the unreliability of single data points. This not only reduces reliance on medical personnel but also yields more accurate and effective detection results.

[0016] Please see Figure 1 The embodiments of this application can be applied to, for example, Figure 1 The application scenario shown includes a terminal device 102 and a server 104. The terminal device 102 can be a device that includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. The terminal device 102 and the server 104 can communicate bidirectionally via a network.

[0017] For example, terminal device 102, such as a wound healing status management device, can collect wound bed environment data, multidimensional morphological data, and biochemical index data of the target wound to determine the healing status of the target wound. Alternatively, the above steps can also be performed by server 104, that is, terminal device 102 receives wound bed environment data, multidimensional morphological data, and biochemical index data of the target wound sent by server 104, and determines the healing status of the target wound based on this data. Alternatively, the above method can be performed collaboratively by terminal device 102 and server 104. For example, terminal device 102 can call server 104 to obtain wound bed environment data, multidimensional morphological data, and biochemical index data of the target wound, or terminal device 102 can obtain wound bed environment data, multidimensional morphological data, and biochemical index data of the target wound from server 104 and store them in local storage space, and so on.

[0018] The terminal device 102 includes, but is not limited to, one or more of mobile phones, computers, IoT devices, and portable wearable devices. The IoT device may be a wound healing status management device, etc. The portable wearable device may be one or more of smartwatches, smart bracelets, smart glasses, and head-mounted devices.

[0019] The server can be a standalone server, or a server network, server cluster, or distributed system composed of multiple servers. Servers include, but are not limited to, computers, network hosts, single network servers, sets of multiple network servers, or cloud servers composed of multiple servers. Cloud servers, in particular, consist of a large number of computers or network servers based on cloud computing.

[0020] The following is a detailed explanation in conjunction with the accompanying drawings.

[0021] Please see Figures 2-6An auxiliary dressing 10 is provided, comprising a dressing layer 20 and a data acquisition device 30 for acquiring real-time wound status. The dressing layer 20 includes a base layer 201 and an intermediate layer 202. The data acquisition device 30 includes a flexible biosensor component 301 for acquiring wound bed environmental data, a flexible multidimensional scanning component 302 for acquiring wound morphology data, and a flexible hyperspectral imaging component 303 for acquiring wound biochemical index data. The flexible biosensor component 301 is disposed on the base layer 201. The flexible multidimensional scanning component 302 and the flexible hyperspectral imaging component 303 are distributed at the same height or different heights on the intermediate layer 202, and are staggered in the intermediate layer 202 along a direction perpendicular to the wound surface. This allows the dressing layer 20 to be attached to the target wound, while the data acquisition device 30 acquires wound bed environmental data, multidimensional morphological data, and biochemical index data of the target wound, thereby determining the healing status of the target wound and providing auxiliary management based on the healing status.

[0022] Specifically, the base layer 201 is the layer that contacts the target wound and promotes wound healing. The intermediate layer 202 mainly integrates optical detection functions, using optical technology to detect multidimensional morphology, biochemical indicators, and other data of the target wound. For example, in a preferred embodiment, the dressing layer 20 further includes a protective layer 203, located on the side away from the target wound, which mainly provides waterproofing, stain resistance, and antibacterial protection for the base layer 201 and the intermediate layer 202.

[0023] In actual use, when the user places the auxiliary dressing 10 on the target wound, the auxiliary dressing 10 can be of various sizes, or the specific size of the auxiliary dressing 10 can be freely cut, and the specific situation can be set according to actual needs. For example Figure 3 and Figure 4 As shown, the base layer 201, intermediate layer 202, and protective layer 203 are stacked, and the areas of the three layers can be completely consistent, or the area of ​​the protective layer can be larger than that of the base layer and intermediate layer, thereby providing better protection for the base layer 201 and intermediate layer 202. Furthermore, during the manufacturing process, the three layers can be produced together or separately. Separate production can improve production efficiency and reduce the mutual interference between different functions; the specific choice can be made according to actual needs.

[0024] For example, in order to ensure the data acquisition effect of the acquisition device 30 in the dressing layer 20, the base layer 201 and the intermediate layer 202 can all be made of light-transmitting material or light-transmitting structure; or in other embodiments, the corresponding positions of the light path where the acquisition device 30 is located in the base layer 201 and the intermediate layer 202 are made of light-transmitting material or light-transmitting structure. For components that cannot be made transparent, an avoidance design can be adopted, that is, avoidance on the path of light propagation.

[0025] Specifically, in some embodiments, the base layer 201 may include a flexible biocompatible adhesive layer attached to the target wound. This flexible biocompatible adhesive layer is a transparent or semi-transparent structure, such as an ultrathin protein nanofilm (UPN) adhesive layer. This flexible biocompatible adhesive layer possesses characteristics such as biocompatibility, adhesion stability, mechanical flexibility, dynamic environmental adaptability, and functional scalability. Specifically, biocompatibility means that upon contact with the target wound, it will not cause an inflammatory response or rejection, and its degradation products can be metabolized and absorbed by the human body, avoiding the risk of long-term retention. Adhesion stability means achieving strong adhesion (adhesion strength ≥ 1.5 N / cm²) on moist tissue surfaces (such as skin), resisting peeling during physiological activities (such as joint movement). Mechanical flexibility and dynamic environmental adaptability mean that the modulus is close to that of human soft tissue (kPa level), avoiding stress shielding or mechanical damage, and the elongation is > 200%, adapting to dynamic deformation. Functional scalability means that it can integrate functions such as conductivity, antibacterial properties, and self-healing.

[0026] Meanwhile, the flexible biosensor assembly 301, disposed on the substrate layer 201, is a flexible structure that can better adapt to the flexible biocompatible adhesive layer. Please refer to [link / reference]. Figures 5-6 In some embodiments, the flexible biosensor assembly 301 includes at least a temperature sensing module, a pH sensing module, and an exudate component detection module. Each module can be arrayed in the substrate layer 201 to collect wound bed environmental data, including temperature, pH value, and exudate components. This provides richer detection data and improves the accuracy of subsequent assessment of the healing status of the target wound.

[0027] In some embodiments, the flexible biocompatible adhesive layer is provided with a microfluidic network structure. The microfluidic network structure connects the outer side of the flexible biocompatible adhesive layer and the edge of the flexible biocompatible adhesive layer, and passes through the flexible biosensor assembly 301 to conduct the exudate at the target wound to the flexible biosensor assembly 301 and / or the edge of the flexible biocompatible adhesive layer. This facilitates the detection of exudate by the flexible biosensor assembly 301, while preventing the exudate from accumulating on the surface of the target wound, avoiding secondary infection, and improving the healing efficiency of the target wound.

[0028] Please see Figures 5-6In some embodiments, the flexible multidimensional scanning component 302 is disposed on the intermediate layer 202, and the flexible multidimensional scanning component 302 is configured to acquire multidimensional morphological data of the target wound. For example, the flexible multidimensional scanning component 302 may include a laser emitting module and a photoelectric detection module. The laser emitting module is configured to emit a first light signal of a preset wavelength toward the target wound, and the photoelectric detection module is configured to detect a second light signal reflected from the target wound, so as to determine the multidimensional morphological data of the target wound based on the second light signal, including the physical morphological data and mechanical tension data of the target wound, etc.

[0029] In a preferred embodiment, such as Figures 5-6 As shown, the flexible hyperspectral imaging component 303 is disposed in the intermediate layer 202. At the same time, a hyperspectral filter component 304 for filtering incident light passing through the protective layer is disposed in the protective layer 203. This not only improves the accuracy of the acquired biochemical index data, but also enables time-division control of the working state of the flexible hyperspectral imaging component 303 and the flexible multidimensional scanning component 302, avoiding the influence of the first light signal generated by the flexible multidimensional scanning component 302 on the data acquisition of the flexible hyperspectral imaging component 303.

[0030] In some embodiments, such as Figures 5-6 As shown, the flexible multidimensional scanning component 302 and the flexible hyperspectral imaging component 303 can be located at the same height or different heights in the intermediate layer 202. Specifically, the laser emitting module can include multiple laser emitting units arranged in an array, the photoelectric detection module can include multiple photoelectric detection units arranged in an array, and the hyperspectral imaging component can include multiple hyperspectral imaging units, which can be arranged in an array. The projections of the multiple laser emitting units, multiple photoelectric detection units, and multiple hyperspectral imaging units in the direction perpendicular to the wound surface do not overlap.

[0031] It is understandable that when multiple laser emitting units, multiple photoelectric detection units, and multiple hyperspectral imaging units are set up in a non-overlapping manner in the projection space perpendicular to the wound surface, this avoids interference between them on the generated or received light signals, improves the accuracy of data acquisition by each component, and thus improves the accuracy of the target wound healing status analysis. In the specific implementation process, the relevant electronic devices, including various sensors and optical components, can be selected first. Then, each layer of the auxiliary dressing can be modeled separately, and the overall optical path can be simulated to achieve avoidance design. For example, the type, number, size, and distribution of flexible biosensor components in the basal layer can be determined first, and the basal layer can be modeled in combination with the material and light transmission structure (such as light transmittance and light transmission position) of the basal layer. Then, the intermediate layer is modeled based on the base layer, and its material, light-transmitting structure, etc., are identified to simulate the target light path that can illuminate the wound, i.e., the target light propagation path. By combining the selection results of flexible multi-dimensional scanning components and flexible hyperspectral imaging components, this target light propagation path is avoided to achieve the aforementioned avoidance design, thereby acquiring data from each wound. It should be noted that the order of description in the following embodiments is not intended to limit the preferred order of embodiments. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the figures.

[0032] Please see Figure 7 A method for collecting wound healing status is provided, which may include steps 1 to 3, specifically: Step 1: Acquire initial hyperspectral imaging data of the target wound using the flexible hyperspectral imaging component; Step 2: Obtain the imaging influence parameter value of the auxiliary dressing, correct the initial hyperspectral imaging data according to the imaging influence parameter value, and generate target hyperspectral imaging data. The imaging influence parameter includes the structural information of the acquisition device and the dressing material and dressing thickness of the base layer and / or intermediate layer. Step 3: Generate biochemical index data of the target wound based on the target hyperspectral imaging data. The biochemical index data includes biochemical characteristic data and microenvironment state data of the target wound.

[0033] For example, biochemical characteristic data includes the oxygen saturation and collagen maturity index of the target wound, while microenvironmental state data includes the bacterial load and tissue edema coefficient of the target wound. This invention utilizes a flexible hyperspectral imaging component adapted to an optical window formed by an auxiliary dressing. The wound area is scanned while covered by the dressing to acquire raw data including the subcutaneous tissue reflectance spectrum. Example sampling parameters: spatial resolution 50 μm, spectral resolution 10 nm, single scan time < 5 seconds. Interference factors include optical attenuation (absorption / scattering) of the dressing layer and stray noise from ambient light (such as operating room lighting). The raw data is then corrected using imaging influence parameters such as the acquisition device structure, dressing material, and dressing thickness. Preferably, Monte Carlo simulation can be used to estimate the scattering effect of ambient scattered light for scattering correction, eliminating scattering artifacts, thereby obtaining the target hyperspectral imaging data for subsequent wound healing status assessment. The specific methods for performing spectral analysis based on the target hyperspectral imaging data and calculating the above biochemical characteristics are described in existing technical documents and will not be repeated here.

[0034] For example, in one specific embodiment, correcting the initial hyperspectral imaging data based on the imaging influence parameter value specifically includes: Step 201: Query the preset first mapping relationship table to obtain the comprehensive transmittance of the dressing material of the base layer and / or the intermediate layer at the target wavelength. Specifically, a library of optical parameters for common dressing materials (including transmittance, refractive index, scattering coefficient, etc.) can be established to support rapid querying. Alternatively, a transmittance curve measurement method can be used, with laboratory calibration performed first. That is, a spectrophotometer is used to measure the transmittance T(λ) of the dressing material (such as transparent silicone, hydrogel) at different wavelengths. Example data is: transmittance T=85% at 1550nm wavelength and T=92% at 1300nm wavelength.

[0035] Step 202: Invoke a preset compensation model and compensate the initial hyperspectral imaging data based on the overall transmittance of the dressing and the dressing thickness of the basal layer and / or intermediate layer to generate optimized hyperspectral imaging data. Specifically, the compensation model can adopt a modified Beer-Lambert law, i.e.:

[0036] in For the initial hyperspectral intensity, For the translucency of the dressing, d represents the attenuation coefficient of the dressing material, and d represents the dressing thickness, which can be measured by 3D scanning layers.

[0037] Step 203: Obtain the transmittance coefficient corresponding to the flexible hyperspectral imaging component based on the location information, and use the transmittance coefficient to correct the optimized hyperspectral imaging data to generate target hyperspectral imaging data. The location information includes the setting height, setting spacing, and setting gap of the flexible multidimensional scanning component and the flexible hyperspectral imaging component in the intermediate layer, respectively, and in the direction perpendicular to the wound surface.

[0038] In a preferred embodiment, step 203 specifically involves: establishing an auxiliary dressing model based on the location information, and obtaining the light transmission window size information of the flexible hyperspectral imaging component based on the auxiliary dressing model; The transmittance coefficient corresponding to the size information of the light-transmitting window is obtained by querying a preset second mapping table. In the above embodiment, by measuring the light-transmitting window of the flexible hyperspectral imaging component in the current dressing structure using location information, the amount of light transmitted from the flexible hyperspectral imaging component that can be captured from the current dressing structure can be measured by the light-transmitting window (characterized by the transmittance coefficient), thereby further correcting the optimized hyperspectral imaging data. Specifically, a mapping table can be established using historical test data from different locations, such as hyperspectral imaging data, spectral analysis data, and incident light detection data, to obtain the corresponding transmittance coefficient based on the current location information.

[0039] In a further preferred embodiment, the outer side of the protective layer of the auxiliary dressing is further provided with a multi-layer structure, including an antibacterial coating. A transfer matrix model M(λ) can be introduced for layer-by-layer dynamic correction, specifically: .

[0040] For example, in other embodiments, machine learning enhancement algorithms and real-time performance guarantee algorithms can also be selected. In machine learning enhancement, a convolutional neural network (CNN) can be trained to directly predict the corrected spectrum from the original data, reducing model dependency errors. In real-time performance guarantee, the algorithm can be deployed on an edge computing chip (such as an FPGA) to reduce single-frame processing latency.

[0041] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0042] Please see Figure 8 The embodiment also provides a method for collecting wound healing status, including the following steps: Step 21: Collect and preprocess the current status data of the target wound to construct an initial feature vector. The current status data includes wound bed environment data, morphological data and / or biochemical index data. Step 22: Normalize, temporally align, and fuse the initial feature vector to generate the target feature vector; Step 23: Invoke the pre-trained wound healing assessment model and generate the current healing status of the target wound based on the target feature vector to generate the corresponding wound management plan.

[0043] The above embodiments provide a wound healing status management method that enables real-time, automatic monitoring and auxiliary management of the healing status of a target wound. This eliminates the need to remove dressings during the wound healing process, improving the convenience of wound management and reducing the risk of secondary infection due to frequent dressing removal. Furthermore, this invention utilizes comprehensive analysis based on multiple wound data points, avoiding the unreliability of single data sources. This not only reduces reliance on medical personnel but also yields more accurate and effective test results.

[0044] For example, when a user has a wound on a certain part of their body due to surgery or injury, the auxiliary dressing of this application is used to set the target wound according to the preset usage rules. Then, the acquisition device can be activated to collect wound bed environment data, multidimensional morphological data and biochemical index data of the target wound. The activation method can be wirelessly controlled.

[0045] Specifically, step 21 can acquire wound bed environment data, multidimensional morphological data, and biochemical index data of the target wound according to a preset acquisition strategy. The acquisition strategy includes real-time acquisition, followed by storing the acquired target data in a preset storage device according to time sequence. Alternatively, the acquisition strategy can also begin only upon receiving an acquisition command. In actual operation, the strategy can be configured according to specific needs.

[0046] For example, in a preferred embodiment, step 21 can use the wound healing status acquisition method described above to collect biochemical index data of the target wound. In addition, it is also necessary to collect wound bed environment data and multidimensional morphological data of the target wound. The wound bed environment data includes the temperature, pH value, and exudate composition of the target wound. The temperature difference between the wound center and the healthy skin can be acquired using a flexible thermistor array; the pH gradient between the wound center and edge can be acquired using an ion-sensitive field-effect transistor; and the exudate composition (such as albumin / fibrinogen) can be acquired using a microfluidic chip.

[0047] The multidimensional morphological data includes the physical morphology and mechanical tension of the target wound. The physical morphology includes wound depth, opening shape, volume reduction rate, and edge contraction index, which can be obtained through point cloud registration and deformation compensation after structured light scanning. The mechanical tension includes local pressure peaks, which are collected through a piezoresistive flexible sensor network. This allows for comprehensive assessment and management of the wound healing status through multiple data sources. Specific methods for acquiring these data are documented in existing technologies and will not be elaborated upon here.

[0048] In a preferred embodiment, step 22 normalizes, temporally aligns, and fuses the initial feature vector to generate a target feature vector, specifically as follows: For continuous features in the initial feature vector, such as temperature and pH value, Z-score is used for standardization, and for categorical features, such as exudate composition, one-hot encoding is used to generate normalized results. By aligning the sampling frequencies of all data in the normalization result using the Dynamic Time Warping (DTW) algorithm, a feature matrix with a unified timestamp is constructed. Graph Convolutional Networks (GCNs) are used to map the physical morphology data in the feature matrix to a biochemical heatmap, generating spatiotemporal correlated features. Specifically, the physical morphology data and biochemical property data are first spatially aligned to output a biochemical heatmap. Then, a vectorized embedding method is used to sequentially embed other data, including temperature / pH values, exudate composition, microenvironmental state data, and mechanical tension data, into the biochemical heatmap. Spatiotemporal Transformer fusion is then performed to generate a spatiotemporal correlated feature vector, thereby solving the problem of mismatch between different data dimensions.

[0049] In a preferred embodiment, the method further includes a training step for a wound healing assessment model, specifically comprising: Cloud-based model construction: An initial neural network model is built on a cloud server. The initial neural network model includes an input layer, a hidden layer, and an output layer. The input layer receives the target feature vector (i.e., the aforementioned 7-dimensional feature vector) and passes it to the hidden layer. The hidden layer includes a feature enhancement module based on a self-attention mechanism and a temporal analysis module based on bidirectional LSTM. The feature enhancement module is used to extract the correlation of multimodal features, and the temporal analysis module is used to capture a preset time window, such as a 7-day wound healing trajectory. The output layer generates the healing stage classification result and healing probability corresponding to the target feature vector. The healing stage classification result includes the inflammatory phase, proliferative phase, maturation phase, and deterioration phase.

[0050] Local model training: The initial neural network model is deployed on several hospital nodes, and historical clinical case data of different wound types in local hospitals are collected, including diabetic foot ulcers, burns, postoperative wound types, etc. The data is labeled based on histopathological results and double-blind evaluation results to form local training data. The initial neural network model is trained using the local training data to optimize the local model parameters of the initial neural network model until the corresponding first preset convergence condition is reached. The local evaluation model corresponding to each hospital node is generated, and the local model parameters are uploaded to the cloud server.

[0051] Cloud parameter aggregation: Obtain the local model parameters and corresponding aggregation weights uploaded by each hospital node, and use a preset aggregation algorithm to aggregate all local model parameters to generate the wound healing assessment model, protecting data privacy while improving generalization ability.

[0052] For example, after obtaining the healing status of a target wound, such as its current healing stage and probability, through a pre-trained wound healing assessment model, it can be integrated with knowledge graphs (including UpToDate and the NICE guidelines) to generate personalized interventions. This includes generating targeted clinical recommendations based on the assessment results, such as: continued observation if there are no obvious abnormalities (i.e., healing is normal); recommendations to change the dressing frequency or regimen if there are mild abnormalities; further surgical intervention if there is a risk of infection or necrosis; and suture removal upon completion of healing.

[0053] In one specific embodiment, the target wound is monitored: Day 1: Eigenvectors: [ΔT=0.8℃, pH=7.2, Albumin / Fibrinogen=1.1, Volume Reduction Rate=0.5% / d, Peak Pressure=18kPa, =82%, bacterial load =0.9kΩ -1 Model output: Inflammatory phase (confidence 92%), recommending the application of anti-inflammatory drugs and pressure therapy to the target wound surface.

[0054] Day 7: Eigenvectors: [ΔT=2.1℃, pH=7.8, Albumin / Fibrinogen=0.6, Volume Reduction Rate=-0.2% / d, Peak Pressure=22kPa, =73%, bacterial load = 2.3kΩ -1 Model output: Deterioration stage (confidence 96%), alarm indicates drug-resistant bacterial infection, recommend intravenous administration of vancomycin.

[0055] Day 14 (post-intervention): Eigenvectors: [ΔT=0.5℃, pH=7.3, albumin / fibrinogen=1.3, volume reduction rate=2.1% / d, peak pressure=15kPa, =89%, bacterial load = 0.7kΩ -1 Model output: Proliferative phase (confidence 88%), it is recommended to continue the current treatment plan and increase the frequency of collagenase debridement.

[0056] In a preferred embodiment, the above management method further includes an early warning step, specifically: obtaining the confidence level corresponding to the current healing status; if the confidence level is less than the confidence level threshold, generating an early warning instruction to trigger the expert review process. Alternatively, a preset rule can be queried to determine whether there are contradictions in the state data of the target feature vector. If so, for example... If the bacterial load is high but surges, an alert is generated to trigger an expert review process, thereby further improving the accuracy of wound healing management.

[0057] Please see Figure 9 The embodiment also provides a wound healing status management device, including a control module 20 and the auxiliary dressing 10 connected to the control module 20. The control module 20 is used to perform the steps of the above wound healing status management method. Specifically, the control module 20 includes: The data acquisition unit is used to collect and preprocess the current state data of the target wound and construct an initial feature vector. The current state data includes wound bed environment data, morphological data and / or biochemical index data. The data processing unit is used to normalize, temporally align, and fuse the initial feature vector to generate the target feature vector. The result generation unit is used to call the pre-trained wound healing assessment model and generate the current healing status of the target wound based on the target feature vector, so as to generate a corresponding wound management plan.

[0058] The above embodiments provide a wound healing status management device that integrates sensors and other data acquisition devices for obtaining wound status into a dressing layer. The wound status acquisition method is optimized based on the placement and structure of the acquisition devices within the dressing layer, enabling real-time, automatic monitoring and auxiliary management of the target wound's healing status. This eliminates the need to remove the dressing during the wound healing process, improving the convenience of wound management and reducing the risk of secondary infection due to frequent dressing removal. Furthermore, this invention utilizes comprehensive analysis based on multiple wound data points, avoiding the unreliability of single data sources. This not only reduces reliance on medical personnel but also yields more accurate and effective test results.

[0059] It should be noted that the foregoing explanation of the wound healing status management method embodiments also applies to the wound healing status management device of the above embodiments, and will not be repeated here.

[0060] Each module in the aforementioned devices can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the control device in hardware form or independently of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0062] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0063] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0064] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0065] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0066] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0067] The present invention is not limited to the description in the specification and embodiments, and thus other advantages and modifications can be readily realized by those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein without departing from the spirit and scope of the general concept as defined by the claims and their equivalents.

Claims

1. An auxiliary dressing, characterized in that, The device includes a dressing layer and a data acquisition device for acquiring real-time wound status. The dressing layer includes a base layer and an intermediate layer. The data acquisition device includes a flexible biosensor assembly for acquiring wound bed environmental data, a flexible multidimensional scanning assembly for acquiring wound morphology data, and a flexible hyperspectral imaging assembly for acquiring wound biochemical index data. The flexible biosensor assembly is disposed in the base layer. The flexible multidimensional scanning assembly and the flexible hyperspectral imaging assembly are distributed at the same height or different heights in the intermediate layer and are staggered in the intermediate layer along a direction perpendicular to the wound surface.

2. The auxiliary dressing according to claim 1, characterized in that, The dressing layer includes a base layer, an intermediate layer and a protective layer arranged in sequence, and the protective layer is provided with a hyperspectral filter component for filtering incident light passing through the protective layer.

3. The auxiliary dressing according to claim 1, characterized in that, The base layer and intermediate layer are all made of light-transmitting materials or have light-transmitting structures; Alternatively, light-transparent materials or structures may be used at the corresponding positions of the optical path where the acquisition device is located in the base layer and intermediate layer.

4. A method for collecting wound healing status data, based on the auxiliary dressing described in any one of claims 1-3, characterized in that, Includes the following steps: Step 1: Acquire initial hyperspectral imaging data of the target wound using the flexible hyperspectral imaging component; Step 2: Obtain the imaging influence parameter value of the auxiliary dressing, correct the initial hyperspectral imaging data according to the imaging influence parameter value, and generate target hyperspectral imaging data. The imaging influence parameter includes the structural information of the acquisition device and the dressing material and dressing thickness of the base layer and / or intermediate layer. Step 3: Generate biochemical index data of the target wound based on the target hyperspectral imaging data. The biochemical index data includes biochemical characteristic data and microenvironment state data of the target wound.

5. The method for collecting wound healing status according to claim 4, characterized in that, The initial hyperspectral imaging data is corrected based on the imaging influence parameter values, specifically including: Step 201: Query the preset first mapping relationship table to obtain the overall transmittance of the dressing material of the base layer and / or the intermediate layer at the target wavelength; Step 202: Invoke the preset compensation model and compensate the initial hyperspectral imaging data based on the overall transmittance of the dressing and the dressing thickness of the base layer and / or intermediate layer to generate optimized hyperspectral imaging data; Step 203: Obtain the transmittance coefficient corresponding to the flexible hyperspectral imaging component based on the location information, and use the transmittance coefficient to correct the optimized hyperspectral imaging data to generate target hyperspectral imaging data. The location information includes the setting height, setting spacing, and setting gap of the flexible multidimensional scanning component and the flexible hyperspectral imaging component in the middle layer, respectively.

6. The method for collecting wound healing status according to claim 5, characterized in that, Step 203 specifically involves: An auxiliary dressing model is established based on the location information, and the light transmission window size information of the flexible hyperspectral imaging component is obtained based on the auxiliary dressing model. Query the preset second mapping relationship table to obtain the light transmittance coefficient corresponding to the light transmittance window size information.

7. A method for managing wound healing status, based on the method described in any one of claims 4-6, characterized in that, Includes the following steps: Step 21: Collect and preprocess the current status data of the target wound to construct an initial feature vector. The current status data includes wound bed environment data, morphological data and / or biochemical index data. Step 22: Normalize, temporally align, and fuse the initial feature vector to generate the target feature vector; Step 23: Invoke the pre-trained wound healing assessment model and generate the current healing status of the target wound based on the target feature vector to generate the corresponding wound management plan.

8. The wound healing status management method according to claim 7, characterized in that, It also includes the training steps for the wound healing assessment model, specifically including: Cloud-based model construction: An initial neural network model is built on a cloud server. The initial neural network model includes an input layer, a hidden layer, and an output layer. The input layer receives the target feature vector and passes it to the hidden layer. The hidden layer includes a feature enhancement module based on a self-attention mechanism and a temporal analysis module based on bidirectional LSTM. The feature enhancement module is used to extract the correlation of multimodal features, and the temporal analysis module is used to capture the wound healing trajectory within a preset time window. The output layer generates the healing stage classification result and healing probability corresponding to the target feature vector. Local model training: The initial neural network model is deployed on several hospital nodes, historical clinical case data of different wound types in local hospitals are collected, and local training data is formed by labeling based on histopathological results and double-blind evaluation results. The initial neural network model is trained using the local training data to optimize the local model parameters of the initial neural network model until the corresponding first preset convergence condition is reached, generating local evaluation models for each hospital node, and uploading the local model parameters to the cloud server. Cloud parameter aggregation: Obtain the local model parameters and corresponding aggregation weights uploaded by each hospital node, and use a preset aggregation algorithm to aggregate all local model parameters to generate the wound healing assessment model.

9. The wound healing status management method according to claim 7, characterized in that, It also includes an early warning step, specifically: obtaining the confidence level corresponding to the current healing status; if the confidence level is less than the confidence level threshold, generating an early warning instruction to trigger the expert review process. Alternatively, a preset rule can be queried to determine whether there are contradictions in the state data of the target feature vector. If so, an early warning instruction is generated to trigger the expert review process.

10. A wound healing status management device, characterized in that, The method includes a control module and an auxiliary dressing as described in any one of claims 1-3 connected to the control module, wherein the control module is used to perform the steps of the wound healing status management method as described in any one of claims 7-9.

Images