A sutureless tumor suction procedure wound assessment method and system
By combining infrared thermal imaging and ultrasound tomography with dynamic deformation and spatial overlay techniques, subcutaneous cavities in sutureless tumor aspiration surgery can be identified and addressed. This solves the problem of early cavity identification and closure, ensuring wound closure meets standards and improving patient recovery.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FIRST AFFILIATED HOSPITAL OF GANNAN MEDICAL UNIV
- Filing Date
- 2025-12-27
- Publication Date
- 2026-07-10
Smart Images
Figure CN122350758A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent medical assistance technology, specifically to a method and system for trauma assessment in sutureless tumor aspiration surgery. Background Technology
[0002] With the development of minimally invasive surgery and tissue engineering technology, sutureless surgery has become increasingly widely used in tumor removal, plastic and reconstructive surgery due to its advantages such as small trauma, fast recovery and no suture scars. This type of surgery relies on bio-adhesive bonding, subcutaneous tissue self-healing or compression fixation to achieve wound closure. Its core feature is the lack of physical anchoring effect of traditional sutures. The adhesion stability of the subcutaneous fat layer and fascia layer depends entirely on the tissue's own healing ability and local fixation effect.
[0003] However, in sutureless surgery, the wound is a three-dimensional space formed by incision or natural tearing. Aspiration procedures can easily lead to separation of subcutaneous tissue layers, creating hidden potential cavities. Furthermore, the sudden increase in abdominal pressure and postural shearing forces generated by daily activities such as coughing and turning after surgery further exacerbate the risk of cavity enlargement. Nursing staff often focus on surface signs such as bleeding and swelling of the epidermal wound, neglecting to monitor the tightness of subcutaneous tissue adhesion. Potential cavities often show no obvious surface abnormalities in the early stages; symptoms such as swelling, pain, and increased skin temperature only appear when fluid or blood accumulates within the cavity and progresses to infection. By this time, the optimal intervention time has been missed, severely impacting patient recovery.
[0004] Therefore, the present invention provides a method and system for trauma assessment in sutureless tumor aspiration surgery. Summary of the Invention
[0005] The purpose of this invention is to provide a method and system for trauma assessment in sutureless tumor aspiration surgery to solve the aforementioned background problems.
[0006] The objective of this invention can be achieved through the following technical solution: a method for trauma assessment in sutureless tumor aspiration surgery, comprising: The patient's wound area was divided into equal tissue contact units, and infrared thermal imaging and ultrasound tomography images were acquired. Subcutaneous adhesion was obtained by adhering analysis based on the infrared thermal imaging and ultrasound tomography images. Suspected non-adhering areas were identified based on the subcutaneous adhesion. The displacement of subcutaneous tissue in the tissue contact unit of the patient in an easily separable state was collected. Dynamic deformation analysis was performed based on the subcutaneous tissue displacement to obtain a dynamic deformation group. Suspected deformation areas were identified based on the dynamic deformation group. Suspected cavity regions are obtained by spatial overlay analysis of suspected non-adhesive regions and suspected deformed regions; ultrasound images of suspected cavity regions are acquired and pixel analysis is performed to determine the actual subcutaneous cavity regions. Based on the actual subcutaneous cavity area, cavity closure operation is performed. After the operation, the actual subcutaneous cavity area is analyzed to obtain the cavity closure rate and the proportion of stable units. The closure rate and the proportion of stable units are used to determine whether the actual subcutaneous cavity area has met the standards.
[0007] Furthermore, the bonding analysis is performed as follows: Infrared images of the wound area were acquired and the infrared temperature value corresponding to each pixel was extracted. Based on the infrared temperature values, standard difference analysis was performed to obtain the temperature uniformity value of the tissue contact unit. Acquire ultrasound tomographic images of the wound area and perform edge detection analysis to obtain the tissue contact ratio of the tissue contact unit; The subcutaneous adhesion is calculated by summing the temperature uniformity of each tissue contact unit with the tissue contact ratio.
[0008] Furthermore, the process of performing the standard deviation analysis is as follows: The standard deviation of the temperature of the tissue contact unit is obtained by acquiring the pixel corresponding to the tissue contact unit and calculating the standard deviation of the infrared temperature value of the corresponding pixel of the tissue contact unit. The temperature uniformity value of the tissue contact unit is obtained by calculating the ratio of the temperature control value to the temperature standard deviation of the tissue contact unit.
[0009] Furthermore, the edge detection analysis is performed as follows: An edge detection algorithm was used to identify the overlapping area between subcutaneous fat and fascia in ultrasound tomographic images, and the area occupied by the overlapping area in each tissue contact unit was obtained and marked as the tissue overlap area. The tissue contact ratio of the tissue contact unit is calculated by dividing the tissue overlap area by the area of the tissue contact unit.
[0010] Furthermore, the process of performing the dynamic deformation analysis is as follows: Obtain the basic displacement; Obtain the maximum displacement of the tissue contact unit within N complete cough cycles of the patient, and label it as d1, d2, d3; The average values of d1, d2, and d3 for each tissue contact unit are calculated, and the difference between the average calculation result and the basic displacement is processed to obtain the cough action difference. Obtain the maximum displacement of the tissue contact unit within N standard turning cycles of the patient, and label them as t1, t2, and t3; The t1, t2, and t3 values of each tissue contact unit are averaged and calculated. The difference between the averaged calculation result and the basic displacement is processed to obtain the turning action difference. The differences in coughing and turning movements were integrated into a dynamic deformation group.
[0011] Furthermore, the spatial overlay analysis is performed as follows: Each tissue contact unit is assigned a unique unit coordinate. Obtain the unit coordinates of all suspected misfit areas and integrate them into unit coordinate set A; obtain the unit coordinates of all suspected deformed areas and integrate them into unit coordinate set B. Calculate the intersection C of the unit coordinates of unit coordinate set A and unit coordinate set B, and mark the tissue contact units corresponding to the unit coordinates in the intersection C as candidate cavity units.
[0012] Furthermore, the process of performing the pixel analysis is as follows: Acquire continuous candidate regions and candidate ultrasound images; Obtain the pixel grayscale threshold, obtain the grayscale value of all pixels in the candidate ultrasound image, mark the pixels with grayscale values less than or equal to the pixel grayscale threshold as candidate cavity pixels, and count the total number of candidate cavity pixels. Obtain the cavity depth and area of a single pixel for each candidate cavity pixel; The cavity volume is calculated by multiplying the total number of candidate cavity pixels, the average cavity depth, and the area of a single pixel.
[0013] Furthermore, the cavity depth of the candidate cavity pixel is obtained as follows: By using the depth calibration function of ultrasound images, the vertical distance h1 from the candidate cavity pixel to the lower boundary of the subcutaneous fat layer and the vertical distance h2 to the upper boundary of the fascia layer are measured respectively. The cavity depth of the candidate cavity pixel is obtained by averaging h1 and h2.
[0014] Furthermore, the process of determining whether the closure of the actual subcutaneous cavity region meets the standard is as follows: Acquire the cavity closure rate and the proportion of stable units in the patient's wound area after cavity closure procedures; Obtain the reference closure rate and the ratio of reference stable elements; If the lacunar closure rate is greater than or equal to the baseline closure rate, and the proportion of stable units is greater than the baseline proportion of stable units, then the patient's actual subcutaneous lacunar region is deemed to have achieved the target closure.
[0015] A trauma assessment system for sutureless tumor aspiration surgery includes the following modules: Adhesion Analysis Module: The patient's wound area is equally divided into tissue contact units, and infrared thermal imaging and ultrasound tomography images are acquired. Adhesion analysis is performed based on the infrared thermal imaging and ultrasound tomography images to obtain the subcutaneous adhesion degree; suspected non-adhesion areas are identified based on the subcutaneous adhesion degree. Dynamic Deformation Module: Collects the subcutaneous tissue displacement of the tissue contact unit when the patient is in an easily separable state, performs dynamic deformation analysis based on the subcutaneous tissue displacement to obtain a dynamic deformation group, and identifies suspected deformation areas based on the dynamic deformation group; Cavity determination module: Based on the spatial superposition analysis of suspected non-adhesive areas and suspected deformed areas, suspected cavity areas are obtained; Ultrasound images of suspected cavity areas are acquired and pixel analysis is performed to determine the actual subcutaneous cavity areas; Closure Compliance Module: Based on the actual subcutaneous cavity area, cavity closure operation is performed. After the operation, the closure judgment analysis of the actual subcutaneous cavity area is performed to obtain the cavity closure rate and the proportion of stable units. Based on the cavity closure rate and the proportion of stable units, it is determined whether the closure of the actual subcutaneous cavity area meets the standard.
[0016] The beneficial effects of this invention are as follows: 1. The patient's wound area is equally divided into tissue contact units, and infrared thermography and ultrasound tomography images are acquired. Subcutaneous adhesion is obtained through adhesion analysis based on the infrared thermography and ultrasound tomography images. Suspected non-adhesion areas are identified based on the subcutaneous adhesion. Through dual-image adhesion analysis and unitized detection, suspected non-adhesion areas under the skin of the wound are efficiently identified. The subcutaneous tissue displacement of the tissue contact units in the easily separable state is collected, and dynamic deformation analysis is performed based on the subcutaneous tissue displacement to obtain dynamic deformation groups. Suspected deformation areas are identified based on the dynamic deformation groups. By collecting the subcutaneous displacement of the tissue contact units in the easily separable state and conducting dynamic deformation analysis, suspected deformation areas are identified, supporting dynamic wound monitoring and targeted treatment.
[0017] 2. Based on the suspected non-adhesive areas and suspected deformed areas, spatial overlay analysis is performed to obtain suspected cavity areas; ultrasound images of the suspected cavity areas are acquired and pixel analysis is performed to determine the actual subcutaneous cavity areas; through spatial overlay screening of suspected areas and verification by ultrasound pixel analysis, the actual subcutaneous cavity areas are effectively identified; cavity closure operations are performed based on the actual subcutaneous cavity areas, and after the operation, closure judgment analysis is performed on the actual subcutaneous cavity areas to obtain the cavity closure rate and stable unit ratio, and the closure rate and stable unit ratio are used to determine whether the closure of the actual subcutaneous cavity areas meets the standards; through cavity closure operations and analysis of closure rate and stable unit ratio, the closure status of the actual subcutaneous cavity is clarified, ensuring the wound treatment effect. Attached Figure Description
[0018] The invention will now be further described with reference to the accompanying drawings.
[0019] Figure 1 This is a flowchart of the steps of a trauma assessment method for sutureless tumor aspiration in this invention; Figure 2 This is a logic diagram for identifying suspected non-fitting areas in this invention; Figure 3 This is a system module diagram of a trauma assessment system for sutureless tumor aspiration surgery according to the present invention. Detailed Implementation
[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example 1
[0021] like Figures 1-2 As shown, a trauma assessment method for sutureless tumor aspiration includes: Step 1: Divide the patient's wound area into equal tissue contact units and acquire infrared thermal imaging and ultrasound tomography images. Based on the infrared thermal imaging and ultrasound tomography images, perform adhesion analysis to obtain the subcutaneous adhesion degree; identify suspected non-adhesion areas based on the subcutaneous adhesion degree. In step one, the process of equally dividing the patient's wound area into tissue contact units and acquiring infrared thermographic images and ultrasound tomographic images is as follows: An adaptive grid segmentation method was used to divide the wound area into tissue contact units of equal area; It should be noted that the wound area is a three-dimensional space formed by surgical incision or natural tearing during sutureless tumor aspiration surgery. It includes the interface of the epidermal incision, dermis, subcutaneous fat layer and fascia layer. Its boundary is defined by the preoperative incision design range and the postoperative natural tissue healing range. The adaptive mesh partitioning method is used for the following partitioning: Obtain the longest and widest values of the wound region as the length and width of the minimum bounding rectangle of the wound region; divide the minimum bounding rectangle into tissue contact units according to the unit size of Q×Q; Preferably, Q is 0.5cm; A portable infrared acquisition device was used to acquire infrared images of the wound area, and infrared images of the corresponding area on the healthy side of the patient were also acquired and marked as control infrared images. It should be noted that the corresponding area on the healthy side is a normal area in which the patient has not undergone surgery, has no trauma, and whose anatomical structure is consistent with the body part on the affected side where the wound is located (e.g., a wound on the left abdomen corresponds to a wound on the right abdomen). It is used to provide baseline data for comparison such as temperature and tissue condition. A layered focused ultrasound detector was used to acquire ultrasound tomographic images of tissue contact units in the wound area; In step one, the method for obtaining the subcutaneous adhesion degree based on the adhesion analysis of infrared thermal imaging and ultrasound tomography images is as follows: The infrared temperature value of each pixel in the control infrared image of the corresponding area of the healthy side is obtained and averaged to obtain the temperature control value. Obtain the infrared temperature value corresponding to each pixel in the infrared image of the wound area; The standard deviation of the temperature of the tissue contact unit is obtained by acquiring the pixel corresponding to the tissue contact unit and calculating the standard deviation of the infrared temperature value of the corresponding pixel of the tissue contact unit. The temperature uniformity value of the tissue contact unit is obtained by calculating the ratio of the temperature control value to the temperature standard deviation of the tissue contact unit. An edge detection algorithm was used to identify the overlapping area between subcutaneous fat and fascia in ultrasound tomographic images, and the area occupied by the overlapping area in each tissue contact unit was obtained and marked as the tissue overlap area. It should be noted that the method of identifying the contact area in ultrasound tomographic images using the edge detection algorithm is as follows: the boundary contours of the subcutaneous fat layer and the fascia layer in the ultrasound image are extracted by the edge detection algorithm, the number of pixels in the spatial intersection area of the two contours is calculated, the area of each pixel and the total number of pixels are obtained, and the area of the overlapping area is obtained. The tissue contact ratio of the tissue contact unit is calculated by dividing the tissue overlap area by the area of the tissue contact unit. Obtain the tissue contact percentage for each tissue contact unit; The subcutaneous adhesion is calculated by summing the uniform temperature value of each tissue contact unit with the tissue contact ratio; It should be noted that the physical significance of subcutaneous adhesion lies in assessing the synergistic state of tissue contact units in the wound area in terms of both temperature distribution uniformity and structural contact integrity. Specifically, subcutaneous adhesion is calculated by summing the temperature uniformity value and the tissue contact ratio: the temperature uniformity value, based on infrared thermography data, reflects the degree of temperature difference between the wound area and the healthy control area, and is directly related to local blood circulation, metabolic activity, and inflammatory response; the tissue contact ratio, based on ultrasound tomography images, quantifies the tightness of tissue interface contact and structural continuity by the proportion of the spatial intersection area of the subcutaneous fat layer and fascia layer boundary contours; the subcutaneous adhesion formed by the superposition of the two reflects both the physiological activity of the wound area at the thermodynamic level and the structural adhesion at the anatomical level. In step one, the process of identifying suspected non-adhesive areas based on subcutaneous adhesion is as follows: In some embodiments, the degree of subcutaneous adhesion is compared with a subcutaneous adhesion threshold; If the subcutaneous adhesion is greater than or equal to the subcutaneous adhesion threshold, it indicates that the tissue contact unit has a good adhesion condition. If the subcutaneous adhesion is less than the subcutaneous adhesion threshold, it indicates that there is a significant risk of non-adhesion in the tissue contact unit, and the tissue contact unit is marked as a suspected non-adhesion area. Step 2: Collect the subcutaneous tissue displacement of the tissue contact unit when the patient is in an easily separable state, perform dynamic deformation analysis based on the subcutaneous tissue displacement to obtain a dynamic deformation group, and identify suspected deformation areas based on the dynamic deformation group; In step two, the dynamic deformation analysis is performed as follows: Piezoresistive pressure sensors are placed at the center of each tissue contact unit to form a miniature monitoring matrix for the wound area; It should be noted that the piezoresistive pressure sensor has a deformation sensitivity of 0.01 cm and its biocompatibility meets medical standards. Static basic data collection: When the patient is in a state of no muscle contraction, the displacement of the tissue contact unit is collected within a standard detection cycle, and the acquisition frequency is set to 1Hz. Preferably, the standard monitoring cycle is 10 minutes; The basic displacement is obtained by averaging the displacement data collected by all piezoresistive pressure sensors. It should be noted that the basic displacement reflects the natural adhesion displacement of the tissue contact unit under static conditions, reflecting the initial adhesion state of the subcutaneous tissue. The smaller the value, the tighter the static adhesion. Dynamic data acquisition: Displacement data of tissue contact units in a patient's easily separable state were collected; For example, easily separable states are coughing and turning over; It should be noted that coughing and turning over were chosen as the easiest separation states because they can cover the sudden increase in abdominal pressure and internal traction and external shear force caused by changes in body position that are most likely to trigger separation of the postoperative wound, and are also the most frequent high-risk movements of patients after surgery. Instruct the patient to perform N standard coughs, maintaining abdominal pressure at approximately 1.2 kPa; Preferably, N is 3; Displacement data of tissue contact units were collected during each complete cough cycle (inhalation-breath-exhalation), with the sampling frequency set to 50Hz; Extract the maximum displacement of the tissue contact unit within each complete cough cycle and label it as d1, d2, and d3; The average values of d1, d2, and d3 for each tissue contact unit are calculated, and the difference between the average calculation result and the basic displacement is processed to obtain the cough action difference. Instruct the patient to perform the standard turning over movement N times (supine → lateral position → supine); Displacement data of tissue contact units were collected during each standard rolling movement cycle (inhalation-breath holding-exhalation), with the sampling frequency set to 50Hz; Extract the maximum displacement of the tissue contact unit within each standard rolling movement cycle and label it as t1, t2, and t3; The t1, t2, and t3 values of each tissue contact unit are averaged and calculated. The difference between the averaged calculation result and the basic displacement is processed to obtain the turning action difference. It should be noted that the difference between coughing and turning over reflects the degree of activity of the subcutaneous tissue interface corresponding to the tissue contact unit when subjected to external stress, resulting in separation or slippage. The differences in coughing movements and turning over movements are integrated into a dynamic deformation group; In step two, the process of identifying suspected deformation areas based on the dynamic deformation group is as follows: A deformation determination matrix is constructed based on the dynamic deformation group of each tissue contact unit; Good dynamic fit area: The difference in coughing movements is less than or equal to the coughing movement threshold, and the difference in turning movements is less than or equal to the turning movement threshold; the difference in coughing movements is greater than the coughing movement threshold, and the difference in turning movements is less than or equal to the turning movement threshold; the difference in coughing movements is less than or equal to the coughing movement threshold, and the difference in turning movements is greater than the turning movement threshold. Dynamic fit to abnormal areas: The difference in coughing movements is greater than the coughing movement threshold, and the difference in turning movements is greater than the turning movement threshold; It is understandable that the cough action threshold and the turning action threshold are set by those skilled in the art based on clinical pre-experimental data and tissue healing biomechanical models; It should be noted that a well-fitting dynamic area is one where the deformation of the tissue contact unit under dynamic load is within an acceptable range; an abnormally fitted dynamic area is one where the subcutaneous tissue corresponding to the tissue contact unit has displaced beyond the safe range during activity, posing a high risk of increased interlayer separation and disruption of early healing. Mark the abnormal dynamic fitting areas as suspected deformation areas; The technical solution of this invention is as follows: the patient's wound area is equally divided into tissue contact units, and infrared thermal imaging and ultrasound tomography images are acquired. Subcutaneous adhesion is obtained through adhesion analysis based on the infrared thermal imaging and ultrasound tomography images. Suspected non-adhesion areas are identified based on the subcutaneous adhesion. Suspected non-adhesion areas under the skin of the wound are efficiently identified through dual-image adhesion analysis and unitized detection. The subcutaneous tissue displacement of the tissue contact units in a easily separable state is collected, and dynamic deformation analysis is performed based on the subcutaneous tissue displacement to obtain dynamic deformation groups. Suspected deformation areas are identified based on the dynamic deformation groups. By collecting the subcutaneous displacement of the tissue contact units in an easily separable state and conducting dynamic deformation analysis, suspected deformation areas are identified, supporting dynamic wound monitoring and targeted treatment. Example 2
[0022] Please see Figure 1 As shown, a trauma assessment method for sutureless tumor aspiration includes: Step 3: Based on the suspected non-adhesive area and the suspected deformed area, perform spatial overlay analysis to obtain the suspected cavity area; acquire ultrasound images of the suspected cavity area and perform pixel analysis to determine the actual subcutaneous cavity area; In step three, the method for obtaining the suspected cavity region through spatial overlay analysis is as follows: A spatial overlay analysis model based on tissue contact units was constructed to identify suspected cavity regions; Specifically, the process of constructing a spatial overlay analysis model is as follows: Based on the tissue contact units in the wound area, each tissue contact unit is assigned a unique unit coordinate. Extract the coordinates of all suspected misfit areas and integrate them into a set of coordinate sets A; extract the coordinates of all suspected deformed areas and integrate them into a set of coordinate sets B. Calculate the intersection C of the unit coordinates of unit coordinate set A and unit coordinate set B, and mark the tissue contact units corresponding to the unit coordinates in the unit coordinate intersection C as candidate cavity units; It should be noted that marking the tissue contact units in the intersection of unit coordinates C as candidate cavity units is based on the principle of dual verification: if the tissue contact unit only has low static fit, it may be caused by local microenvironment differences, and if it only has large dynamic deformation, it may also be due to normal tissue elasticity; only when poor structural contact and poor mechanical stability occur simultaneously in the same spatial location, the risk of persistent separation of subcutaneous tissue and the formation of potential cavities at that location is significantly increased. It should also be noted that the candidate cavity unit is a tissue contact unit that is simultaneously marked as a suspected non-fitting area and a suspected deformation area, that is, a unit that simultaneously exhibits static structural non-fitting and dynamic stress deformation anomaly. All candidate cavity elements are obtained by connecting them using the eight-neighborhood connectivity method to obtain continuous candidate regions; For example, the eight-neighbor connectivity method detects the neighboring units in eight directions (up, down, left, right and four diagonal directions) around each candidate cavity unit. If they are consecutively adjacent and are all candidate cavity units, they are connected to form a continuous candidate region. This method is used to identify and integrate potential cavity regions in subcutaneous tissue with both static and dynamic abnormalities. In step three, the pixel analysis process is as follows: Candidate ultrasound images are obtained by scanning continuous candidate regions using a high-resolution ultrasound scanner, and standard ultrasound images are obtained by scanning the corresponding healthy side regions of the continuous candidate regions. Calculate the average gray value and gray standard deviation of the standard ultrasound image, and calculate the pixel gray threshold by subtracting the average gray value from the gray standard deviation by L times. Preferably, L is 1.5; It should be noted that the advantage of calculating the pixel gray threshold by the difference between the average gray value and L times the gray standard deviation is that: this setting takes the gray value of the healthy side normal tissue as the individual benchmark, and the offset of 1.5 times the gray standard deviation is suitable for the low gray ultrasound characteristics of the cavity, while excluding the interference of normal tissue fluctuation and slight edema, thus balancing the recognition sensitivity and specificity. Obtain the grayscale values of all pixels in the candidate ultrasound image, mark the pixels with grayscale values less than or equal to the pixel grayscale threshold as candidate cavity pixels, and count the total number of candidate cavity pixels. Using the depth calibration function of ultrasound images, the vertical distance h1 from the candidate cavity pixel to the lower boundary of the subcutaneous fat layer and the vertical distance h2 to the upper boundary of the fascia layer are measured respectively. The cavity depth of the candidate cavity pixel is obtained by averaging h1 and h2. The cavity depth of all candidate cavity pixels is obtained and averaged to obtain the average cavity depth. The cavity volume is calculated by multiplying the total number of candidate cavity pixels, the average cavity depth, and the area of a single pixel. Preferably, the area of a single pixel is 0.01 cm²; In some embodiments, the cavity volume is compared with a reference cavity volume; It is understandable that the baseline cavity volume is obtained by those skilled in the art through clinical pre-experiments; If the cavity volume is greater than or equal to the reference cavity volume, it indicates that the continuous candidate region is the true subcutaneous cavity region; If the cavity volume is smaller than the reference cavity volume, it indicates that the continuous candidate region is an ultrasound artifact or a physiological tissue gap and is not included in the real cavity. Step 4: Perform cavity closure operation based on the actual subcutaneous cavity area. After the operation, perform closure judgment analysis on the actual subcutaneous cavity area to obtain the cavity closure rate and the proportion of stable units. Determine whether the closure of the actual subcutaneous cavity area meets the standard based on the cavity closure rate and the proportion of stable units. In step four, the process of performing the cavity closure operation is as follows: The specific method for performing cavity closure based on a defined actual subcutaneous cavity region is as follows: Cavity closure is achieved by using modified chitosan biogel (compliant with ISO10993 medical biocompatibility standards, with a degradation cycle of 2-4 weeks, synchronized with the tissue healing cycle) combined with local negative pressure assisted bonding technology. The injection is performed at multiple points in eight directions along the edge of the actual subcutaneous cavity area using a 30G micro-injection needle. The injection dose is calculated as 1.2 times the preoperative cavity volume to ensure that the gel fully fills the cavity and infiltrates the fat-fascia interface to form temporary adhesive support. Immediately after injection, cover with a flexible, breathable negative pressure patch (negative pressure set at -8kPa, breathability ≥500g / (m²・24h)) and apply continuous negative pressure for 24 hours to promote tight adhesion of the tissue interface and prevent the reformation of cavities; It should be noted that the negative pressure value and injection dosage are set based on tissue biomechanical experiments: -8kPa negative pressure can provide effective adhesion stress without affecting subcutaneous capillary blood flow (blood flow velocity is maintained at more than 85% of the preoperative level), and 1.2 times the dosage can offset the gel volume loss caused by tissue fluid absorption, while avoiding local swelling caused by excessive filling. In step four, the process of performing the closure determination analysis is as follows: Based on the subcutaneous adhesion, dynamic deformation group (difference between coughing and turning movements) and cavity closure rate of each tissue contact unit in the wound area 24 hours after the cavity closure operation; It is understandable that the cavity closure rate is obtained by: obtaining the cavity volume after the cavity closure operation, calculating the difference between the cavity volume before the operation and the cavity volume after the operation, and calculating the ratio between the result of the difference calculation and the cavity volume before the operation to obtain the cavity closure rate. If the subcutaneous adhesion of the tissue contact unit is greater than or equal to the subcutaneous adhesion threshold, the cough action difference is less than or equal to the cough action threshold, and the turning action difference is less than or equal to the turning action threshold, then the tissue contact unit is determined to be a closed stable unit. Conversely, the tissue contact unit is identified as the unit to be observed; The proportion of stable units is calculated by comparing the total number of closed stable units with the total number of tissue contact units. In step four, the process of determining whether the closure of the actual subcutaneous cavity region meets the standard based on the cavity closure rate and the ratio of stable units is as follows: A comprehensive compliance judgment matrix is constructed based on the cavity closure rate and the proportion of stable units, as follows: Obtain the cavity closure rate and stable cell ratio 24 hours after the cavity closure operation from the historical cavity closure operation database, and perform a mean operation to obtain the baseline closure rate and baseline stable cell ratio; Compare the cavity closure rate with the reference closure rate, and compare the stable element ratio with the reference stable element ratio; If the cavity closure rate is greater than or equal to the baseline closure rate and the proportion of stable units is greater than the baseline stable unit proportion, then the patient's actual subcutaneous cavity area closure is deemed to have met the standard, and the recorded data can be transferred to the routine observation stage. Conversely, if the wound does not meet the closure criteria, a second intervention procedure must be initiated. The technical solution of this invention is as follows: Suspected cavity regions are obtained through spatial overlay analysis of suspected non-adhesive regions and suspected deformed regions; ultrasound images of the suspected cavity regions are acquired and pixel-based analysis is performed to determine the actual subcutaneous cavity regions; the actual subcutaneous cavity regions are effectively identified through spatial overlay screening of suspected regions and ultrasound pixel analysis verification; cavity closure operations are performed based on the actual subcutaneous cavity regions; after the operation, closure determination analysis is performed on the actual subcutaneous cavity regions to obtain the cavity closure rate and stable unit ratio; the closure rate and stable unit ratio are used to determine whether the closure of the actual subcutaneous cavity regions meets the standards; through cavity closure operations and analysis of closure rate and stable unit ratio, the closure status of the actual subcutaneous cavity is clarified, ensuring the wound treatment effect. Example 3
[0023] Please see Figure 3 As shown, a trauma assessment system for sutureless tumor aspiration includes the following modules: Adhesion Analysis Module: The patient's wound area is equally divided into tissue contact units, and infrared thermal imaging and ultrasound tomography images are acquired. Adhesion analysis is performed based on the infrared thermal imaging and ultrasound tomography images to obtain the subcutaneous adhesion degree; suspected non-adhesion areas are identified based on the subcutaneous adhesion degree. Dynamic Deformation Module: Collects the subcutaneous tissue displacement of the tissue contact unit when the patient is in an easily separable state, performs dynamic deformation analysis based on the subcutaneous tissue displacement to obtain a dynamic deformation group, and identifies suspected deformation areas based on the dynamic deformation group; Cavity determination module: Based on the spatial superposition analysis of suspected non-adhesive areas and suspected deformed areas, suspected cavity areas are obtained; Ultrasound images of suspected cavity areas are acquired and pixel analysis is performed to determine the actual subcutaneous cavity areas; Closure Compliance Module: Based on the actual subcutaneous cavity area, cavity closure operation is performed. After the operation, the closure judgment analysis of the actual subcutaneous cavity area is performed to obtain the cavity closure rate and the proportion of stable units. Based on the cavity closure rate and the proportion of stable units, it is determined whether the closure of the actual subcutaneous cavity area meets the standard.
[0024] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.
Claims
1. A method for trauma assessment in sutureless tumor aspiration surgery, characterized in that: The patient's wound area was divided into equal tissue contact units, and infrared thermal imaging and ultrasound tomography images were acquired. Subcutaneous adhesion was obtained by adhering analysis based on the infrared thermal imaging and ultrasound tomography images. Suspected non-adhering areas were identified based on the subcutaneous adhesion. The displacement of subcutaneous tissue in the tissue contact unit of the patient in an easily separable state was collected. Dynamic deformation analysis was performed based on the subcutaneous tissue displacement to obtain a dynamic deformation group. Suspected deformation areas were identified based on the dynamic deformation group. Suspected cavity regions are obtained by spatial overlay analysis of suspected non-fitting regions and suspected deformed regions. Acquire ultrasound images of suspected cavity areas and perform pixel analysis to determine the actual subcutaneous cavity area; Based on the actual subcutaneous cavity area, cavity closure operation is performed. After the operation, the actual subcutaneous cavity area is analyzed to obtain the cavity closure rate and the proportion of stable units. The closure rate and the proportion of stable units are used to determine whether the actual subcutaneous cavity area has met the standards.
2. The trauma assessment method for sutureless tumor aspiration according to claim 1, characterized in that: The bonding analysis is performed as follows: Infrared images of the wound area were acquired and the infrared temperature value corresponding to each pixel was extracted. Based on the infrared temperature values, standard difference analysis was performed to obtain the temperature uniformity value of the tissue contact unit. Acquire ultrasound tomographic images of the wound area and perform edge detection analysis to obtain the tissue contact ratio of the tissue contact unit; The subcutaneous adhesion is calculated by summing the temperature uniformity of each tissue contact unit with the tissue contact ratio.
3. The trauma assessment method for sutureless tumor aspiration according to claim 2, characterized in that: The process of performing the standard deviation analysis is as follows: The standard deviation of the temperature of the tissue contact unit is obtained by acquiring the pixel corresponding to the tissue contact unit and calculating the standard deviation of the infrared temperature value of the corresponding pixel of the tissue contact unit. The temperature uniformity value of the tissue contact unit is obtained by calculating the ratio of the temperature control value to the temperature standard deviation of the tissue contact unit.
4. The trauma assessment method for sutureless tumor aspiration according to claim 2, characterized in that: The edge detection analysis is performed as follows: An edge detection algorithm was used to identify the overlapping area between subcutaneous fat and fascia in ultrasound tomographic images, and the area occupied by the overlapping area in each tissue contact unit was obtained and marked as the tissue overlap area. The tissue contact ratio of the tissue contact unit is calculated by dividing the tissue overlap area by the area of the tissue contact unit.
5. The trauma assessment method for sutureless tumor aspiration according to claim 1, characterized in that: The process of performing the dynamic deformation analysis is as follows: Obtain the basic displacement; Obtain the maximum displacement of the tissue contact unit within N complete cough cycles of the patient, and label it as d1, d2, d3; The average values of d1, d2, and d3 for each tissue contact unit are calculated, and the difference between the average calculation result and the basic displacement is processed to obtain the cough action difference. Obtain the maximum displacement of the tissue contact unit within N standard turning cycles of the patient, and label them as t1, t2, and t3; The t1, t2, and t3 values of each tissue contact unit are averaged and calculated. The difference between the averaged calculation result and the basic displacement is processed to obtain the turning action difference. The differences in coughing and turning movements were integrated into a dynamic deformation group.
6. The trauma assessment method for sutureless tumor aspiration according to claim 1, characterized in that: The method for performing the spatial overlay analysis is as follows: Each tissue contact unit is assigned a unique unit coordinate. Obtain the unit coordinates of all suspected misfit areas and integrate them into unit coordinate set A; obtain the unit coordinates of all suspected deformed areas and integrate them into unit coordinate set B. Calculate the intersection C of the unit coordinates of unit coordinate set A and unit coordinate set B, and mark the tissue contact units corresponding to the unit coordinates in the intersection C as candidate cavity units.
7. The trauma assessment method for sutureless tumor aspiration according to claim 1, characterized in that: The process of performing the pixel analysis is as follows: Acquire continuous candidate regions and candidate ultrasound images; Obtain the pixel grayscale threshold, obtain the grayscale value of all pixels in the candidate ultrasound image, mark the pixels with grayscale values less than or equal to the pixel grayscale threshold as candidate cavity pixels, and count the total number of candidate cavity pixels. Obtain the cavity depth and area of a single pixel for each candidate cavity pixel; The cavity volume is calculated by multiplying the total number of candidate cavity pixels, the average cavity depth, and the area of a single pixel.
8. The trauma assessment method for sutureless tumor aspiration according to claim 7, characterized in that: The cavity depth of the candidate cavity pixels is obtained as follows: By using the depth calibration function of ultrasound images, the vertical distance h1 from the candidate cavity pixel to the lower boundary of the subcutaneous fat layer and the vertical distance h2 to the upper boundary of the fascia layer are measured respectively. The cavity depth of the candidate cavity pixel is obtained by averaging h1 and h2.
9. The trauma assessment method for sutureless tumor aspiration according to claim 1, characterized in that: The process for determining whether the closure of the actual subcutaneous cavity region meets the standard is as follows: Acquire the cavity closure rate and the proportion of stable units in the patient's wound area after cavity closure procedures; Obtain the reference closure rate and the ratio of reference stable elements; If the lacunar closure rate is greater than or equal to the baseline closure rate, and the proportion of stable units is greater than the baseline proportion of stable units, then the patient's actual subcutaneous lacunar region is deemed to have achieved the target closure.
10. A trauma assessment system for sutureless tumor aspiration surgery, used to implement the trauma assessment method for sutureless tumor aspiration surgery as described in any one of claims 1-9, characterized in that: Includes the following modules: Adhesion Analysis Module: The patient's wound area is equally divided into tissue contact units and infrared thermal imaging and ultrasound tomography images are acquired. Adhesion analysis is performed based on the infrared thermal imaging and ultrasound tomography images to obtain the subcutaneous adhesion degree. Identify suspected non-adhesive areas based on subcutaneous adhesion; Dynamic Deformation Module: Collects the subcutaneous tissue displacement of the tissue contact unit when the patient is in an easily separable state, performs dynamic deformation analysis based on the subcutaneous tissue displacement to obtain a dynamic deformation group, and identifies suspected deformation areas based on the dynamic deformation group; Cavity determination module: Based on the spatial superposition analysis of suspected non-fitting areas and suspected deformed areas, suspected cavity areas are obtained; Acquire ultrasound images of suspected cavity areas and perform pixel analysis to determine the actual subcutaneous cavity area; Closure Compliance Module: Based on the actual subcutaneous cavity area, cavity closure operation is performed. After the operation, the closure judgment analysis of the actual subcutaneous cavity area is performed to obtain the cavity closure rate and the proportion of stable units. Based on the cavity closure rate and the proportion of stable units, it is determined whether the closure of the actual subcutaneous cavity area meets the standard.