Intelligent skin wound area measurement and analysis terminal

Abstract

The application discloses a skin wound area intelligent measurement and analysis terminal and relates to the technical field of medical apparatus and instruments. In order to solve the problems that traditional soft ruler measurement is easy to cause cross infection, has great error for irregular and sensitive wounds, cannot capture the three-dimensional characteristics such as wound depth to judge the severity and healing potential, lacks healing risk prediction ability and often misses the intervention opportunity, the application cooperatively collects data through a laser projection component, a non-contact depth sensor and an optical imaging camera, avoids cross infection caused by direct contact of the traditional soft ruler with the wound, accurately calculates the area through a grid ruler and three-dimensional point cloud data, generates a complete three-dimensional wound model through a wound model construction module, quantitatively extracts three-dimensional characteristic parameters such as depth, texture and color proportion, provides multi-dimensional objective basis for judging the severity and healing potential of the wound, generates future healing trend prediction and risk early warning and helps medical staff to avoid risks in advance.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to an intelligent measurement and analysis terminal for skin wound area. Background Technology

[0002] In clinical nursing, accurate assessment of skin wounds is crucial for developing treatment plans, tracking healing progress, and evaluating treatment effectiveness. However, traditional flexible measuring tapes rely on healthcare professionals directly manipulating the wound surface, which not only easily leads to cross-infection between the wound and the measuring tool, but also makes it difficult to obtain accurate length x width data for irregularly shaped or sensitive wounds, resulting in significant measurement errors. Furthermore, while existing projected rulers achieve non-contact area measurement, they only provide two-dimensional planar data and cannot capture three-dimensional features such as wound depth, depression layers, and tissue defects. Consequently, these indicators cannot be used to determine wound severity and healing potential. The lack of predictive ability regarding wound healing risks often leads to adjustments in treatment plans only after problems such as infection and delayed healing have emerged, missing the optimal intervention opportunity. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent measurement and analysis terminal for skin wound area. It collects data through a laser projection component, a non-contact depth sensor, and an optical imaging camera. It does not rely on the regular shape of the wound. It accurately calculates the area through a grid ruler and three-dimensional point cloud data, generates predictions of future healing trends and risk warnings, helps medical staff avoid risks in advance, and improves treatment efficiency, thereby solving the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A smart measurement and analysis terminal for skin wound area, including a handheld smart measurement terminal, which integrates a wound data acquisition module, a wound model construction module, and a wound analysis module.

[0006] The wound data acquisition module is used to simultaneously capture wound data information based on depth sensors and optical imaging cameras, process and integrate the acquired wound data information, and generate corresponding 3D point cloud data.

[0007] The wound model construction module is used to preprocess 3D point cloud data, construct a complete 3D wound model based on 3D point cloud data corresponding to multiple views, and determine the wound edge contour.

[0008] The wound analysis module is used to retrieve historical wound measurement data of the same patient, generate a dynamic healing curve based on time series, and mark key healing nodes. At the same time, it integrates multi-dimensional information to generate a report predicting changes in the wound in the later stage and adjustment suggestions.

[0009] Furthermore, the wound data acquisition module includes:

[0010] The data acquisition and calibration unit is used to calibrate each component before acquiring skin wound data. After calibration, the laser projection mode and acquisition parameters are determined based on the pre-input operation requirements and wound type information.

[0011] The data synchronization capture unit is used to synchronously acquire wound area, raw depth data and raw optical data of the patient's wound area based on a determined laser projection mode and acquisition parameters, and to monitor the working synchronization of each component in real time during the acquisition process.

[0012] The wound data integration unit is used to preprocess the acquired raw depth data and raw optical data, establish the correspondence between the raw optical data and raw depth data, extract wound color feature data by combining projection scale data, and generate three-dimensional point cloud data.

[0013] Furthermore, the wound data acquisition module also includes:

[0014] The distance between the handheld intelligent measurement terminal and the surface of the skin wound area is extracted based on the depth sensor, and the imaging distance of the optical imaging camera is extracted at the same time. The imaging distance is then compared and calibrated with the acquisition reference distance.

[0015] The matching degree between the calibrated acquisition reference distance and the preset laser projection adaptation threshold is calculated, and the current distance is determined to be within the effective measurement range based on the calculation result.

[0016] Establish the matching relationship between the acquisition reference distance and the grid density, combine the preset matching distance parameter reference value, determine the matching distance parameter of each component, and set the acquisition parameters of each component based on the matching distance parameter.

[0017] Furthermore, the data synchronization capture unit also includes:

[0018] Based on the determined laser projection mode, a grid-like projection scale and a corresponding skin damage color comparison map are projected onto the patient's wound and surrounding normal skin area. The corresponding components are controlled to scan and collect data on the patient's wound area according to the preset acquisition parameters.

[0019] The laser projection component continuously projects a grid-like projection scale, counts the number of complete grids and half grids covering the wound, obtains preliminary wound area data in real time, and compares the actual color of the wound with the projected standard color comparison map to determine the wound color type.

[0020] The depth sensor collects depth information at different locations of the wound in real time, while the optical imaging camera simultaneously collects image data of the patient's wound area. It also simultaneously acquires raw depth data of the internal depression layers and tissue defects of the wound, as well as raw optical data containing information on the wound surface texture, edge contour, projection scale, and color contrast map.

[0021] Furthermore, the wound model building module includes:

[0022] The edge contour determination unit is used to extract the depth difference, color difference, and texture difference between the wound area and the surrounding normal skin area in the preprocessed 3D point cloud data, and to construct a boundary judgment index based on multi-feature fusion.

[0023] Using preset normal skin pixels as seed points, and combining boundary judgment indicators, the normal skin area is gradually grown and expanded to determine the growth boundary of the area not covered by growth.

[0024] The growth boundary is smoothed to generate continuous wound edge contour data. At the same time, the area of ​​the region enclosed by the contour is calculated and the difference is calculated with the wound area calculated by the wound data acquisition module. If the difference exceeds the preset area deviation threshold, the three-dimensional point cloud data preprocessing operation is performed again.

[0025] The model output unit is used to construct a complete 3D model of the wound based on the determined wound edge contour, combined with the original optical data and the original depth data, and synchronously transmits the constructed complete 3D model of the wound and the wound edge contour data to the wound analysis module.

[0026] Furthermore, the wound model building module also includes a wound feature quantization unit, used to extract features from the complete 3D wound model and generate multi-dimensional wound feature parameters:

[0027] The edge contour is generated based on the wound edge contour determination unit, and the actual area of ​​the wound is obtained based on the coordinates of the contour discrete points.

[0028] Traverse each surface point in the 3D wound model, calculate the vertical distance between each surface point and the normal skin surface around the wound, record the maximum distance as the maximum depression depth of the wound, and calculate the average distance of all depression points as the average depression depth.

[0029] Extract the gray-level co-occurrence matrix of the wound surface model, calculate the texture feature parameters such as contrast, correlation, and energy of the gray-level co-occurrence matrix, and quantitatively characterize the roughness and tissue uniformity of the wound surface.

[0030] Based on the wound color feature data, the wound area is divided into multiple sub-regions. The color features of each sub-region are extracted and statistically analyzed to obtain the proportion of each color type in the wound area and generate a color distribution histogram.

[0031] Furthermore, the wound analysis module includes:

[0032] The historical data retrieval unit is used to receive patient information input by medical staff, retrieve the patient's historical wound measurement data, including historical wound 3D models, multi-dimensional wound feature parameters and corresponding measurement timestamps, and construct the wound data time series corresponding to the patient.

[0033] The healing curve generation unit is used to determine the target values ​​of multi-dimensional wound feature parameters based on the wound data time series, and to draw the patient's area healing curve, depth healing curve and color healing curve based on the target values;

[0034] The healing prediction and early warning unit is used to extract multi-dimensional information features of the current patient, perform feature matching on standardized cases in the clinical healing case database, screen reference cases, generate corresponding prediction reports, provide risk warnings, and generate corresponding intervention suggestions.

[0035] The report output unit integrates the dynamic healing curve, the future healing trend prediction curve, the risk warning results, and the intervention suggestions into a wound post-change prediction report.

[0036] Furthermore, the risk warning steps in the healing prediction and early warning unit include:

[0037] Based on the correspondence between target values ​​and historical measurement points in historical wound measurement data, the historical healing rate for each healing type is calculated;

[0038] Based on the historical healing rate, calculate the mean and standard deviation of the historical healing rate for each healing type.

[0039] Calculate the dynamic risk threshold for each healing type based on the historical mean healing rate and the historical standard deviation of the healing rate;

[0040] If the current healing rate is less than the corresponding dynamic risk threshold, a delayed healing warning of the corresponding healing type is generated, and the delayed healing time is calculated;

[0041] The delayed healing warning and the corresponding delayed healing time will be presented together as risk information.

[0042] Furthermore, the handheld intelligent measurement terminal also integrates a supplementary lighting module and a power module. The supplementary lighting module can automatically adjust the brightness according to the ambient light intensity, and the power module is a rechargeable lithium battery.

[0043] Furthermore, the supplementary lighting module also includes:

[0044] Based on the acquisition reference distance between the handheld intelligent measurement terminal and the surface of the skin wound area, a three-dimensional model of the terminal is constructed in the three-dimensional space of the wound three-dimensional model.

[0045] Based on the pre-set supplementary lighting strategy of the supplementary lighting device, supplementary lighting is simulated in three-dimensional space, and the first direction vector from the light source center of the virtual supplementary lighting device to each surface point is determined under each supplementary lighting strategy.

[0046] Construct a second direction vector based on the perpendicular lines from the surface point to the normal skin surface surrounding the wound;

[0047] Calculate the vector angle between the first direction vector and the second direction vector, and use it as the supplementary lighting angle for the corresponding surface point;

[0048] Based on the preset supplementary lighting angle suitability evaluation library, the first suitable value of the supplementary lighting angle is matched and associated with the surface point;

[0049] Determine the illumination parameters for each surface point under each supplementary lighting strategy;

[0050] Based on the illumination suitability evaluation model, a second suitable value of the illumination parameter is determined according to the color and texture features of the surface points and associated with the surface points. The illumination suitability evaluation model is trained by multiple evaluation records of human evaluation of whether the known illumination parameters are conducive to the clear presentation of wound features based on wound images labeled with color and texture features. During training, the labeled color features, texture features and known illumination parameters are used as input parameters of the machine learning model, and the human evaluation value is used as the output parameter.

[0051] The first and second suitability values ​​associated with the surface points are weighted and fused to obtain the target suitability value corresponding to the surface points.

[0052] Summing the target suitability values ​​corresponding to all surface points under the same supplementary lighting strategy yields the suitability of the corresponding supplementary lighting strategy.

[0053] Apply the most suitable supplemental lighting strategy.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] By using a laser projection component in conjunction with a non-contact depth sensor and optical imaging camera to collect data, the cross-infection caused by direct contact of traditional soft measuring tapes with wounds is avoided. At the same time, it does not rely on the regular shape of the wound. Even for irregular and sensitive wounds, the area can be accurately calculated using a grid ruler and 3D point cloud data, solving the problem of large measurement errors in traditional methods. The wound model building module generates a complete 3D model of the wound, quantifies and extracts three-dimensional feature parameters such as depth, texture, and color proportion, and provides multi-dimensional objective evidence for judging the severity of the wound and its healing potential. It generates predictions of future healing trends and risk warnings, and outputs intervention suggestions to help medical staff avoid risks in advance. Attached Figure Description

[0056] Figure 1 This is a diagram of the intelligent skin wound area measurement and analysis terminal module of the present invention;

[0057] Figure 2 This is a schematic diagram of the projection scanning of the wound data acquisition module of the present invention. Detailed Implementation

[0058] 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.

[0059] To address the technical problems of traditional flexible measuring tapes, which are prone to cross-infection and have large errors for irregular and sensitive wounds, and the fact that existing projection scales, while enabling non-contact area measurement, only provide two-dimensional data and cannot capture three-dimensional features such as wound depth to assess severity and healing potential, and also lack the ability to predict healing risks, often missing opportunities for intervention, please refer to [the relevant documentation]. Figure 1-2 This embodiment provides the following technical solution:

[0060] A smart terminal for measuring and analyzing skin wound area includes a handheld smart measurement terminal. The handheld smart measurement terminal integrates a wound data acquisition module, a wound model construction module, and a wound analysis module. The handheld smart measurement terminal features an arc-shaped edge design to accommodate hand grip, ensuring portability for medical staff during ward rounds and bedside operations. The main body of the handheld smart measurement terminal also integrates a supplementary lighting module and a power module. The supplementary lighting module is an adjustable color temperature LED, located on both sides of the data acquisition module, with a color temperature range of 3000K-6500K. It can automatically adjust its brightness according to ambient light intensity, ensuring that the data acquisition module acquires clear wound images and depth information under different lighting conditions, such as natural light in wards and surgical shadowless lamps. The power module is a rechargeable lithium battery to meet the power needs of medical staff for all-day mobile diagnosis and treatment.

[0061] The wound data acquisition module integrates a high-resolution depth sensor, an optical imaging camera, and laser projection components on both sides of the camera. It simultaneously captures wound data based on the depth sensor and optical imaging camera, including surface texture, edge contours, internal depression layers, and tissue defect information. The acquired wound data is processed and integrated to generate corresponding 3D point cloud data. The high-resolution depth sensor is integrated into the top area of ​​the handheld intelligent measurement terminal, while the optical imaging camera simultaneously acquires high-definition images of the wound surface, ensuring spatial matching between the 3D point cloud data and the optical image.

[0062] The wound model construction module is used to preprocess the 3D point cloud data, performing noise reduction, stitching and reconstruction in sequence. Based on the 3D point cloud data corresponding to multiple views, a complete 3D wound model is constructed, and the wound edge contour is determined.

[0063] The wound analysis module is used to retrieve historical wound measurement data of the same patient, generate a dynamic healing curve based on time series, and mark key healing nodes. At the same time, it integrates multi-dimensional information to generate a report predicting changes in the wound in the later stage and adjustment suggestions.

[0064] In this embodiment, the multi-dimensional information includes historical measurement data, wound type, patient's basic health status, and treatment plan; wound type includes pressure injury, diabetic foot ulcer, burn wound, etc.; patient's basic health status includes age, blood glucose level, and nutritional status; treatment plan includes dressing type and medication.

[0065] In this embodiment, the clinical healing case database contains standardized wound healing cases. Through machine learning algorithms such as random forest and neural networks, feature matching is performed to generate wound healing trend predictions and risk warnings for the next 7-14 days, such as the probability of delayed healing and the level of infection risk.

[0066] In this embodiment, a non-contact acquisition method is used to avoid the risk of cross-infection caused by direct contact with traditional soft rulers. Furthermore, three-dimensional point cloud data is used to capture three-dimensional features such as wound depth and indentation layers, overcoming the limitation of existing projection rulers that can only provide two-dimensional data. This allows for accurate assessment of wound severity and healing potential. By automatically generating dynamic healing curves and marking key nodes, medical staff can quickly assess treatment effectiveness without manually organizing data. At the same time, multi-dimensional information is integrated to generate dynamic curves and adjustment suggestions, providing a scientific basis for treatment plan formulation, predicting healing trends, and issuing risk warnings, giving medical staff sufficient time to adjust the plan.

[0067] In this embodiment, the wound data acquisition module includes:

[0068] The data acquisition and calibration unit is used to calibrate various components, including the depth sensor, optical imaging camera, and laser projection component, before acquiring skin wound data. First, it calibrates the spatial coordinate matching of the high-resolution depth sensor and the optical imaging camera to eliminate initial deviations. Then, it controls the laser projection component to project a grid-like projection scale and a standard color comparison image onto a preset calibration benchmark. The optical imaging camera acquires the projected calibration benchmark image, analyzes the size deviation of the projection scale and the color accuracy deviation of the color comparison image, and automatically adjusts the projection angle, focal length, color temperature, and brightness of the laser projection component until the deviation is within a preset accuracy threshold. After calibration, the laser projection mode and acquisition parameters are determined based on pre-input operational requirements and wound type information.

[0069] The data synchronization capture unit is used to synchronously acquire the wound area, original depth data and original optical data of the patient's wound area based on the determined laser projection mode and acquisition parameters. During the acquisition process, it monitors the working synchronization of each component in real time, compares the acquisition / projection timestamps of the three, and if the time difference exceeds the preset synchronization threshold, it immediately pauses the acquisition and recalibrates the start sequence. After calibration, it continues the acquisition to ensure that the original depth data and the original optical data containing projection information correspond one-to-one in the time dimension.

[0070] In this embodiment, the data synchronization capture unit further includes:

[0071] Based on the determined laser projection mode, a grid-like projection scale and a corresponding skin damage color comparison map are projected onto the patient's wound and surrounding normal skin area. The corresponding components are controlled to scan and collect data on the patient's wound area according to the preset acquisition parameters.

[0072] The laser projection component continuously projects a grid-like projection scale, counts the number of complete grids and half grids covering the wound, obtains preliminary wound area data in real time, and compares the actual color of the wound with the projected standard color comparison map to determine the wound color type.

[0073] The depth sensor collects depth information at different locations of the wound in real time, and the optical imaging camera simultaneously collects image data of the patient's wound area. It also simultaneously acquires the original depth data of the internal depression layers and tissue defects of the wound, as well as the original optical data containing information on the wound surface texture, edge contour, projection scale and color contrast map.

[0074] The wound data integration unit is used to preprocess the acquired raw depth data and raw optical data, including outlier removal and missing point interpolation for the raw depth data, noise reduction and image segmentation for the raw optical data, separating the wound area, projection scale area and color contrast map area; establishing the correspondence between the raw optical data and raw depth data, calculating the actual wound area by combining the projection scale data, and extracting wound color feature data by associating with the color contrast map data; and fusing the optical information of pixels with the depth information of depth data points to generate three-dimensional point cloud data containing wound area, color features, surface texture, edge contour, internal depression layers and tissue defect information.

[0075] In this embodiment, the wound data acquisition module further includes:

[0076] The distance between the handheld intelligent measurement terminal and the surface of the skin wound area is extracted based on the depth sensor, and the imaging distance of the optical imaging camera is extracted at the same time. The imaging distance is then compared and calibrated with the acquisition reference distance.

[0077] The matching degree between the calibrated acquisition reference distance and the preset laser projection adaptation threshold is calculated. Based on the calculation result, it is determined whether the current distance is within the effective measurement range, ensuring that the projection scale completely covers the wound area and the grid is clearly distinguishable.

[0078] Establish the matching relationship between the acquisition reference distance and the grid density, combine the preset matching distance parameter reference value, determine the matching distance parameter of each component, and set the acquisition parameters of each component based on the matching distance parameter;

[0079] In this embodiment, the acquisition reference distance is the initial detection distance between the depth sensor and the surface of the skin wound area, and the initial value is preset based on the conventional measurement range of the skin wound.

[0080] In this embodiment, the imaging distance is the straight-line distance between the center of the optical imaging camera lens and the center point of the skin wound area, and this imaging distance is consistent with the acquisition reference distance of the high-resolution depth sensor.

[0081] In this embodiment, the sampling frequency of the high-resolution depth sensor is adjusted according to the wound depression depth detection results. The deeper the wound depression, the higher the sampling frequency, ensuring complete data acquisition of the depression layers. The imaging resolution of the optical imaging camera is adjusted according to the wound area size. The smaller the wound area, the higher the imaging resolution, ensuring clear wound surface texture. The projection focal length of the laser projection component is adjusted according to the adaptation distance parameter, ensuring clear imaging of the grid-like projection scale in the wound area without edge distortion or blurring, providing parameter support for accurate acquisition of multi-dimensional wound data.

[0082] In this embodiment, by synchronously extracting distance data from the depth sensor and the optical camera, and combining the laser projection characteristics to calculate the appropriate distance parameters, it is possible to ensure that the sensor combination maintains the optimal measurement distance from the wound. This effectively avoids the problem of insufficient acquisition range due to too close a distance, or decreased data accuracy due to too far a distance. It achieves automatic synchronization of distance data and automatic calculation of appropriate parameters, eliminating the need for manual measurement and adjustment by medical staff. It is suitable for scenarios such as mobile ward rounds and bedside operations using handheld terminals, significantly improving operational convenience and data accuracy. By optimizing the sampling frequency of the depth sensor, the resolution of the camera, and the focal length of the laser projection, the acquisition parameters can be accurately matched for different types of wounds, ensuring that information from all dimensions of the wound can be completely captured, and avoiding acquisition omissions or distortions caused by fixed parameters.

[0083] In this embodiment, the wound model construction module includes:

[0084] The edge contour determination unit is used to extract the depth difference, color difference, and texture difference between the wound area and the surrounding normal skin area in the preprocessed 3D point cloud data, and to construct a boundary judgment index based on multi-feature fusion.

[0085] Using preset normal skin pixels as seed points, and combining boundary judgment indicators, the normal skin area is gradually grown and expanded to determine the growth boundary of the area not covered by growth.

[0086] The growth boundary is smoothed to generate continuous wound edge contour data. At the same time, the area of ​​the region enclosed by the contour is calculated and the difference is calculated with the wound area calculated by the wound data acquisition module. If the difference exceeds the preset area deviation threshold, the three-dimensional point cloud data preprocessing operation is performed again.

[0087] The model output unit is used to construct a complete 3D model of the wound based on the determined wound edge contour, combined with the original optical data and the original depth data, and synchronously transmits the constructed complete 3D model of the wound and the wound edge contour data to the wound analysis module.

[0088] In this embodiment, the wound model construction module further includes a wound feature quantization unit, used to perform feature quantization extraction on the complete three-dimensional wound model and generate multi-dimensional wound feature parameters:

[0089] The edge contour is generated based on the wound edge contour determination unit, and the actual area of ​​the wound is obtained based on the coordinates of the contour discrete points.

[0090] Traverse each surface point in the 3D wound model, calculate the vertical distance between each surface point and the normal skin surface around the wound, record the maximum distance as the maximum depression depth of the wound, and calculate the average distance of all depression points as the average depression depth.

[0091] Extract the gray-level co-occurrence matrix of the wound surface model, calculate the texture feature parameters such as contrast, correlation, and energy of the gray-level co-occurrence matrix, and quantitatively characterize the roughness and tissue uniformity of the wound surface.

[0092] Based on the wound color feature data, the wound area is divided into multiple sub-regions. The color features of each sub-region are extracted and statistically analyzed to obtain the proportion of each color type in the wound area and generate a color distribution histogram.

[0093] In this embodiment, by combining multi-feature fusion and region growing algorithms with smoothing processing and area difference verification, the accuracy of wound edge contour and area calculation is significantly improved, avoiding the bias of single feature judgment. Multi-source data is integrated to construct a complete three-dimensional model, intuitively presenting the three-dimensional shape of the wound and solving the limitation of traditional two-dimensional assessment that cannot show depth and depression. Multi-dimensional parameters such as area, depth, texture, and color ratio are extracted to achieve standardization and quantification of wound features, providing an objective basis for wound severity classification and laying an accurate data foundation for subsequent dynamic healing curve generation and healing trend prediction.

[0094] In this embodiment, the wound analysis module includes:

[0095] The historical data retrieval unit is used to receive patient information input by medical staff, including medical record number, name, gender, etc., retrieve the patient's historical wound measurement data, including historical wound 3D model, multi-dimensional wound feature parameters and corresponding measurement timestamps, and construct the wound data time series corresponding to the patient.

[0096] The healing curve generation unit is used to determine the target values ​​of multi-dimensional wound feature parameters based on the wound data time series, and to draw the patient's area healing curve, depth healing curve and color healing curve based on the target values;

[0097] The healing prediction and early warning unit is used to extract multi-dimensional information features of the current patient, perform feature matching on standardized cases in the clinical healing case database, screen reference cases, generate corresponding prediction reports, provide risk warnings, and generate corresponding intervention suggestions.

[0098] The report output unit integrates the dynamic healing curve, the future healing trend prediction curve, the risk warning results, and the intervention suggestions into a wound post-change prediction report.

[0099] In this embodiment, by accurately linking patient information with historical measurement data, a complete wound data time series is constructed, and multi-dimensional parameters are transformed into intuitive healing curves of area, depth, and color, clearly presenting the dynamic changes in healing and helping medical staff to quickly grasp the healing process. Combined with the case library and patient characteristics to match reference cases, a healing prediction report and risk warning are generated.

[0100] In one embodiment, the risk warning step in the healing prediction and warning unit includes:

[0101] Based on the correspondence between target values ​​and historical measurement points in historical wound measurement data, the historical healing rate for each healing type is calculated:

[0102]

[0103] in, For the first Each historical measurement point corresponds to a healing type. The target value, For the first Each historical measurement point corresponds to a healing type. The target value, For the first Each historical measurement point corresponds to a healing type. healing rate For the first From the first historical measurement point to the... Time interval between historical measurement points; healing type Including: area healing, depth healing, and color healing. ;

[0104] Based on historical healing rates, calculate the mean and standard deviation of historical healing rates for each healing type:

[0105]

[0106]

[0107] in, Healing type at the current moment The historical average healing rate, Healing type at the current moment Historical healing rate standard deviation The total number of historical measurement points, the first The historical measurement points are the measurement points at the current moment;

[0108] Calculate the dynamic risk threshold for each healing type based on the historical mean healing rate and the historical standard deviation of the healing rate:

[0109]

[0110] in, Healing type at the current moment Dynamic risk threshold, For risk coefficient, ;

[0111] If the current healing rate If the value is less than the corresponding dynamic risk threshold, a delayed healing warning of the corresponding healing type is generated, and the delayed healing time is calculated:

[0112]

[0113] in, Healing type at the current moment Delayed healing time, For healing type The goal of healing The target healing time point;

[0114] The delayed healing warning and the corresponding delayed healing time will be presented together as risk information.

[0115] In this embodiment, the current healing rate The corresponding dynamic risk threshold refers to the corresponding healing type.

[0116] For example, let's analyze a wound of the type chronic diabetic foot ulcer:

[0117] The healing target is: Area healing target: The target healing time point is 120 days, the current time point is 100 days, the total number of historical measurement points is 100, and the time interval between historical measurement points is 1 day.

[0118] Known , Calculations show that , , Comparing 0.0263 with 0.0375, calculate... Output a warning for delayed wound healing (corresponding to the type of delayed healing warning), and include the expected delay in the warning. Warning details for healing in the sky.

[0119] The working principle and beneficial effects of the above technical solution are as follows:

[0120] This invention establishes a dynamic risk model through historical data statistics, avoiding misjudgments caused by using fixed thresholds, making the thresholds more adaptable, and improving the accuracy of healing risk alerts. The delayed healing time in the risk information provides quantitative predictions, offering time guidance for early intervention and further improving the rationality of subsequent interventions.

[0121] In one embodiment, the supplementary lighting module further includes:

[0122] Based on the acquisition reference distance between the handheld intelligent measurement terminal and the surface of the skin wound area, a three-dimensional model of the terminal is constructed in the three-dimensional space of the wound three-dimensional model.

[0123] Based on the pre-set supplementary lighting strategy of the supplementary lighting device, supplementary lighting is simulated in three-dimensional space, and the first direction vector from the light source center of the virtual supplementary lighting device to each surface point is determined under each supplementary lighting strategy.

[0124] Construct a second direction vector based on the perpendicular lines from the surface point to the normal skin surface surrounding the wound;

[0125] Calculate the vector angle between the first direction vector and the second direction vector, and use it as the supplementary lighting angle for the corresponding surface point;

[0126] Based on the preset supplementary lighting angle suitability evaluation library, the first suitable value of the supplementary lighting angle is matched and associated with the surface point;

[0127] Determine the illumination parameters for each surface point under each supplementary lighting strategy;

[0128] Based on the illumination suitability evaluation model, the second suitable value of the illumination parameter is determined according to the color and texture characteristics of the surface points, and then associated with the surface points.

[0129] The first and second suitability values ​​associated with the surface points are weighted and fused to obtain the target suitability value corresponding to the surface points.

[0130] Summing the target suitability values ​​corresponding to all surface points under the same supplementary lighting strategy yields the suitability of the corresponding supplementary lighting strategy.

[0131] Apply the most suitable supplemental lighting strategy.

[0132] In this embodiment, when constructing the terminal 3D model, a basic 3D model is first constructed based on the structural parameters of the handheld intelligent measurement terminal, and then the basic 3D model is mapped in the 3D space of the wound 3D model according to the acquisition reference distance.

[0133] In this embodiment, the supplementary lighting device is a light source system integrated into a handheld intelligent measurement terminal. The supplementary lighting strategy is a preset combination of supplementary lighting parameters, including: the position of the supplementary lighting device, the illumination angle, the light intensity, and the color temperature.

[0134] In this embodiment, the virtual supplementary lighting device is the virtual model part corresponding to the supplementary lighting device in the terminal's three-dimensional model.

[0135] In this embodiment, when determining the first direction vector from the light source center of the virtual supplementary lighting device to each surface point, the light source center of the virtual supplementary lighting device is used as the vector starting point, the surface point is used as the vector ending point, and the distance from the light source center to the surface point is used as the modulus to construct the vector, thus obtaining the first direction vector.

[0136] In this embodiment, when constructing the second direction vector, the starting point of the vector is the perpendicular point of the perpendicular line and the normal skin surface around the wound, the ending point of the vector is the surface point, and the distance from the perpendicular point to the surface point is the modulus to construct the vector, thus obtaining the second direction vector.

[0137] In this embodiment, the preset supplementary lighting angle suitability evaluation library is a pre-established database that records the suitability of viewing the supplementary lighting target under different supplementary lighting angles. The smaller the supplementary lighting angle, the higher the suitability (first suitability value).

[0138] In this embodiment, the lighting parameters are light intensity and color temperature.

[0139] In this embodiment, the lighting suitability evaluation model is a machine learning model that assesses whether lighting parameters are conducive to the clear presentation of wound features based on color and texture characteristics. Color features are the color information of surface points, and texture characteristics are the roughness, graininess, and moisture content of surface points.

[0140] In this embodiment, the lighting suitability evaluation model is trained by multiple evaluation records of human evaluations based on wound images labeled with color and texture features, assessing whether known lighting parameters are conducive to the clear presentation of wound features. During training, the labeled color features, texture features, and known lighting parameters are used as input parameters for the machine learning model, and the human evaluation values ​​are used as output parameters.

[0141] In this embodiment, the second suitability value is the evaluation value of whether the lighting parameters evaluated by the lighting suitability evaluation model are conducive to the clear presentation of wound features. The higher the evaluation value, the more conducive the lighting parameters are to the clear presentation of wound features.

[0142] In this embodiment, weighted fusion refers to assigning dynamic weights to the first suitability value and the second suitability value respectively to obtain a comprehensive suitability score (target suitability value).

[0143] In this embodiment, the "dynamic" in "dynamic weight" refers to the manual adjustment of the fusion weight of the first suitability value and the second suitability value based on the difference between the highest suitability supplementary lighting strategy determined by the system each time and the actual highest suitability supplementary lighting strategy, so that the subsequently determined highest suitability supplementary lighting strategy tends to the actual highest suitability supplementary lighting strategy.

[0144] The working principle and beneficial effects of the above technical solution are as follows:

[0145] When applying supplemental lighting to skin wounds, the setting of the lighting parameters greatly affects the wound measurement results. A more suitable lighting effect for viewing is more conducive to the extraction of subsequent analysis data. Therefore, this invention constructs a terminal 3D model within the 3D space of the wound 3D model and simulates supplemental lighting in 3D space according to the supplemental lighting strategy. A first direction vector and a second direction vector are introduced. By calculating the vector angle between the first and second direction vectors, the influence of the supplemental lighting angle on the suitability of wound viewing (first suitability value) is quantified. An illumination suitability evaluation model is introduced. Based on the color and texture characteristics of surface points, a second suitability value of the illumination parameters is determined. The first and second suitability values ​​associated with surface points are weighted and fused to obtain a target suitability value. The target suitability values ​​corresponding to all surface points under the same supplemental lighting strategy are summed to obtain the suitability of the corresponding supplemental lighting strategy. The supplemental lighting strategy with the highest suitability is applied. This invention achieves fully automatic and intelligent supplemental lighting decision-making by comprehensively considering the rationality of geometric illumination and the visibility of medical features. The dynamic weighting mechanism allows the system to continuously calibrate the evaluation criteria based on actual usage feedback, achieving adaptive optimal supplemental lighting control, improving the supplemental lighting effect, and further improving the accuracy of subsequent wound analysis.

[0146] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart terminal for measuring and analyzing skin wound area, characterized in that, It includes a handheld intelligent measurement terminal, which integrates a wound data acquisition module, a wound model construction module, and a wound analysis module. The wound data acquisition module is used to simultaneously capture wound data information based on depth sensors and optical imaging cameras, process and integrate the acquired wound data information, and generate corresponding three-dimensional point cloud data. The wound model construction module is used to preprocess 3D point cloud data, construct a complete 3D wound model based on 3D point cloud data corresponding to multiple views, and determine the wound edge contour. The wound analysis module is used to retrieve historical wound measurement data of the same patient, generate a dynamic healing curve based on time series, and mark key healing nodes. It also establishes a dynamic risk model through historical data statistics for risk warning, and integrates multi-dimensional information to generate a report on the prediction of later changes in the wound and adjustment suggestions. The handheld intelligent measurement terminal also integrates a supplementary lighting module and a power module. The supplementary lighting module automatically adjusts the brightness according to the ambient light intensity, and the power module is a rechargeable lithium battery. The supplementary lighting module also includes: Based on the acquisition reference distance between the handheld intelligent measurement terminal and the surface of the skin wound area, a three-dimensional model of the terminal is constructed in the three-dimensional space of the wound three-dimensional model. Based on the pre-set supplementary lighting strategy of the supplementary lighting device, supplementary lighting is simulated in three-dimensional space, and the first direction vector from the light source center of the virtual supplementary lighting device to each surface point is determined under each supplementary lighting strategy. Construct a second direction vector based on the perpendicular lines from the surface point to the normal skin surface surrounding the wound; Calculate the vector angle between the first direction vector and the second direction vector, and use it as the supplementary lighting angle for the corresponding surface point; Based on the preset supplementary lighting angle suitability evaluation library, the first suitable value of the supplementary lighting angle is matched and associated with the surface point; Determine the illumination parameters for each surface point under each supplementary lighting strategy; Based on the illumination suitability evaluation model, a second suitable value of the illumination parameter is determined according to the color and texture features of the surface points and associated with the surface points. The illumination suitability evaluation model is trained by multiple evaluation records of human evaluation of whether the known illumination parameters are conducive to the clear presentation of wound features based on wound images labeled with color and texture features. During training, the labeled color features, texture features and known illumination parameters are used as input parameters of the machine learning model, and the human evaluation value is used as the output parameter. The first and second suitability values ​​associated with the surface points are weighted and fused to obtain the target suitability value corresponding to the surface points. Summing the target suitability values ​​corresponding to all surface points under the same supplementary lighting strategy yields the suitability of the corresponding supplementary lighting strategy. Apply the most suitable supplemental lighting strategy.

2. The intelligent measurement and analysis terminal for skin wound area as described in claim 1, characterized in that, The wound data acquisition module includes: The data acquisition and calibration unit is used to calibrate each component before acquiring skin wound data. After calibration, the laser projection mode and acquisition parameters are determined based on the pre-input operation requirements and wound type information. The data synchronization capture unit is used to synchronously acquire wound area, raw depth data and raw optical data of the patient's wound area based on a determined laser projection mode and acquisition parameters, and to monitor the working synchronization of each component in real time during the acquisition process. The wound data integration unit is used to preprocess the acquired raw depth data and raw optical data, establish the correspondence between the raw optical data and raw depth data, extract wound color feature data by combining projection scale data, and generate three-dimensional point cloud data.

3. The intelligent measurement and analysis terminal for skin wound area as described in claim 2, characterized in that, The wound data acquisition module also includes: The distance between the handheld intelligent measurement terminal and the surface of the skin wound area is extracted based on the depth sensor, and the imaging distance of the optical imaging camera is extracted at the same time. The imaging distance is then compared and calibrated with the acquisition reference distance. The matching degree between the calibrated acquisition reference distance and the preset laser projection adaptation threshold is calculated, and the current distance is determined to be within the effective measurement range based on the calculation result. Establish the matching relationship between the acquisition reference distance and the grid density, combine the preset matching distance parameter reference value, determine the matching distance parameter of each component, and set the acquisition parameters of each component based on the matching distance parameter.

4. The intelligent measurement and analysis terminal for skin wound area as described in claim 3, characterized in that, The data synchronization capture unit also includes: Based on the determined laser projection mode, a grid-like projection scale and a corresponding skin damage color comparison map are projected onto the patient's wound and surrounding normal skin area. The corresponding components are controlled to scan and collect data on the patient's wound area according to the preset acquisition parameters. The laser projection component continuously projects a grid-like projection scale, counts the number of complete grids and half grids covering the wound, obtains preliminary wound area data in real time, and compares the actual color of the wound with the projected standard color comparison map to determine the wound color type. A depth sensor collects depth information at different locations of the wound in real time, while an optical imaging camera simultaneously collects image data of the patient's wound area, acquiring raw depth data and raw optical data simultaneously.

5. The intelligent measurement and analysis terminal for skin wound area as described in claim 4, characterized in that, The wound model building module includes: The edge contour determination unit is used to extract the depth difference, color difference, and texture difference between the wound area and the surrounding normal skin area in the preprocessed 3D point cloud data, and to construct a boundary judgment index based on multi-feature fusion. Using preset normal skin pixels as seed points, and combining boundary judgment indicators, the normal skin area is gradually grown and expanded to determine the growth boundary of the area not covered by growth. The growth boundary is smoothed to generate continuous wound edge contour data. At the same time, the area of ​​the region enclosed by the contour is calculated and the difference is calculated with the wound area calculated by the wound data acquisition module. If the difference exceeds the preset area deviation threshold, the three-dimensional point cloud data preprocessing operation is performed again. The model output unit is used to construct a complete 3D model of the wound based on the determined wound edge contour, combined with the original optical data and the original depth data, and synchronously transmits the constructed complete 3D model of the wound and the wound edge contour data to the wound analysis module.

6. The intelligent measurement and analysis terminal for skin wound area as described in claim 5, characterized in that, The wound model building module also includes a wound feature quantization unit, which is used to extract features from the complete 3D wound model and generate multi-dimensional wound feature parameters. The edge contour is generated based on the wound edge contour determination unit, and the actual area of ​​the wound is obtained based on the coordinates of the contour discrete points. Traverse each surface point in the 3D wound model, calculate the vertical distance between each surface point and the normal skin surface around the wound, record the maximum distance as the maximum depression depth of the wound, and calculate the average distance of all depression points as the average depression depth. Extract the gray-level co-occurrence matrix of the wound surface model, calculate the texture feature parameters such as contrast, correlation, and energy of the gray-level co-occurrence matrix, and quantitatively characterize the roughness and tissue uniformity of the wound surface. Based on the wound color feature data, the wound area is divided into multiple sub-regions. The color features of each sub-region are extracted and statistically analyzed to obtain the proportion of each color type in the wound area and generate a color distribution histogram.

7. The intelligent measurement and analysis terminal for skin wound area as described in claim 6, characterized in that, The wound analysis module includes: The historical data retrieval unit is used to receive patient information input by medical staff, retrieve the patient's historical wound measurement data, including historical wound 3D models, multi-dimensional wound feature parameters and corresponding measurement timestamps, and construct the wound data time series corresponding to the patient. The healing curve generation unit is used to determine the target values ​​of multi-dimensional wound feature parameters based on the wound data time series, and to draw the patient's area healing curve, depth healing curve and color healing curve based on the target values; The healing prediction and early warning unit is used to extract multi-dimensional information features of the current patient, perform feature matching on standardized cases in the clinical healing case database, screen reference cases, generate corresponding prediction reports, provide risk warnings, and generate corresponding intervention suggestions. The report output unit integrates the dynamic healing curve, the future healing trend prediction curve, the risk warning results, and the intervention suggestions into a wound post-change prediction report.

8. The intelligent measurement and analysis terminal for skin wound area as described in claim 7, characterized in that, The risk warning steps in the healing prediction and early warning unit include: Based on the correspondence between target values ​​and historical measurement points in historical wound measurement data, the historical healing rate for each healing type is calculated; Based on the historical healing rate, calculate the mean and standard deviation of the historical healing rate for each healing type. Calculate the dynamic risk threshold for each healing type based on the historical mean healing rate and the historical standard deviation of the healing rate; If the current healing rate is less than the corresponding dynamic risk threshold, a delayed healing warning of the corresponding healing type is generated, and the delayed healing time is calculated. The delayed healing warning and the corresponding delayed healing time will be presented together as risk information.

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