Orthopedic implant position monitoring method and system
By processing structured multimodal ultrasound image data packages, the dependence on and difficulty in distinguishing implant locations in existing technologies have been resolved, enabling accurate assessment and reliable monitoring of implant location and structural integrity.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-04
- Publication Date
- 2026-04-10
AI Technical Summary
Current ultrasound monitoring technology for monitoring the position of orthopedic implants relies on the operator's experience, making it difficult to perform accurate three-dimensional spatial measurements and distinguish between normal folding and early intracapsular rupture, thus affecting the accuracy of monitoring.
A structured multimodal ultrasound image data acquisition and processing method is adopted. The contour is optimized by calculating the curvature and gradient distance of the contour pixels. Combined with edge detection and cluster analysis, normal folds and suspected broken line segments are distinguished. The response hysteresis is obtained by using the dynamic sequence of induced stress, and a decision is formed by combining it with the clinical decision rule base.
It enables objective assessment of implant location and structural integrity, improves the standardization and reliability of monitoring, and provides traceable data for long-term follow-up.
Smart Images
Figure CN121817956A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of image processing technology and relates to a method and system for monitoring the position of orthopedic implants. Background Technology
[0002] In the field of plastic surgery, breast implants (including silicone gel and saline implants) are the most commonly used implants in breast augmentation or breast reconstruction surgery. In addition, there are various other plastic implants such as nasal implants. These implants require long-term monitoring of their positional stability and structural integrity in clinical applications. Once the implant moves out of the initial surgical cavity, it can easily lead to problems such as asymmetry and deformity (such as breast implant displacement or nasal implant deviation). When patients experience unexplained pain or changes in appearance, accurate positioning and morphological assessment of the implant become crucial for clinical diagnosis and treatment. Therefore, efficient and accurate positional monitoring of plastic surgery implants has important clinical value and safety significance.
[0003] Currently, ultrasound technology is the most commonly used first-line screening tool for implant placement monitoring. Its core advantage lies in its ability to effectively distinguish the prosthesis from surrounding soft tissue, enabling preliminary assessment of the prosthesis capsule and assisting in the detection of abnormalities such as fluid accumulation or rupture. However, existing ultrasound monitoring protocols have significant limitations: on the one hand, monitoring results are highly dependent on the operator's clinical experience, and different operators have different judgment criteria, leading to poor monitoring consistency; on the other hand, ultrasound technology struggles to achieve precise three-dimensional spatial measurement of implants and lacks a standardized archiving and comparison system, which is not conducive to long-term follow-up and dynamic evaluation.
[0004] A more critical technical challenge lies in the fact that the capsule of implants such as silicone prostheses undergoes normal folding within the body's cavities. This folded area often appears as a single, transient hyperechoic line on ultrasound imaging. However, early intracapsular rupture of the prosthesis results in a multi-layered, parallel, wavy "step sign" at the site of the rupture. The ultrasound manifestations of the two are easily confused. This confusion directly leads to the difficulty in accurately distinguishing between "normal folding" and "early rupture" using ultrasound imaging in clinical practice. Consequently, it interferes with the assessment of implant positional stability and structural integrity, seriously affecting the accuracy of monitoring results and posing potential risks to clinical diagnosis and patient safety. Summary of the Invention
[0005] The purpose of this invention is to solve the problems in the existing technology of traditional ultrasound monitoring of plastic surgery implants, which are highly dependent on the operator's experience, difficult to perform accurate three-dimensional spatial measurement and archiving comparison, and the ultrasound manifestation of normal folding of silicone implant shell is easily confused with the "step sign" of early intracapsular rupture, affecting the accuracy of monitoring the position and structural integrity of implants. The invention provides a method and system for monitoring the position of plastic surgery implants.
[0006] To achieve the above objectives, the present invention employs the following technical solution:
[0007] A method for monitoring the position of orthopedic implants, comprising:
[0008] Acquire static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package;
[0009] The static images in the ultrasound image data package are preprocessed to obtain the initial contour of the prosthesis implant shell; the loss value is obtained by calculating the curvature of the contour pixels and the gradient distance of the surrounding pixels; the contour is adjusted and optimized until the loss value meets the preset conditions, and the main boundary contour of the prosthesis implant shell is obtained.
[0010] Edge detection is performed on pixels outside the main boundary contour of the capsule in static images. Candidate folded line segments that meet the preset distance and angle conditions are selected and clustered. The step coefficient is obtained by calculating the distance variance and angle variance between line segments to distinguish between normal folds and suspected damaged line segment clusters.
[0011] The induced stress dynamic sequence in the ultrasound image data package is processed, and the minimum circumcircle of the cluster of normal folded line segments is taken as the candidate region. The vector sequence of feature points in the candidate region is extracted.
[0012] The vector sequence is divided, and the pressure period and rebound period obtained from the division are fitted to obtain the fitting curve. The response hysteresis is obtained by combining the step coefficient.
[0013] The response lag is compared with the implant's historical data and clinical decision rule base to determine the implant location and form a clinical decision.
[0014] A further improvement of the present invention is that:
[0015] Furthermore, the acquisition of static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package specifically involves:
[0016] A high-frequency linear array probe was selected, and coupling gel was applied before gently placing the probe on the skin surface. The target area of the patient was scanned using the high-frequency linear array probe to obtain a complete two-dimensional sonographic image including the overall outline of the prosthesis, adjacent breast and pectoralis major muscle structures, and the axillary region. High-resolution static images of key sections were acquired along the clock face of the prosthesis at the 12 o'clock, 3 o'clock, 6 o'clock, and 9 o'clock positions. The prosthesis's central and superior and inferior pole regions were focused on to capture the physiological movement trajectory of the prosthesis and its internal structures and surrounding tissues over at least two complete respiratory cycles, obtaining a dynamic image sequence of spontaneous breathing. A specific region of interest was selected, and a gentle, vertical, short-term pressure release stress was applied using the high-frequency linear array probe. Simultaneously, a dynamic image sequence of induced stress was recorded, recording the entire process from pressurization, steady state to sudden release and rebound. All image data were integrated and archived into a structured multimodal ultrasound image data package.
[0017] Furthermore, the static images in the ultrasound image data package are preprocessed to obtain the initial contour of the prosthesis implant shell. Specifically, anisotropic diffusion filtering is performed on the multi-section high-resolution static images. Based on the characteristic that the gray value of the prosthesis implant shell in the ultrasound image is higher than that of the body tissue and the filling material inside the prosthesis, the filtered static images are subjected to Otsu threshold segmentation to extract the regions above the threshold. The largest connected component is selected from the high-threshold region, and the contour line of the connected component is used as the initial contour of the shell boundary.
[0018] Furthermore, the loss value is obtained by calculating the curvature of the contour pixels and the gradient distance of the surrounding pixels. The contour is then adjusted and optimized until the loss value meets the preset conditions to obtain the main boundary contour of the prosthesis implant shell. Specifically:
[0019] For each pixel on the initial contour line of the capsule boundary, obtain the fitted curve between the pixel and its left and right adjacent pixels. Get the x-coordinate of the current pixel on the fitted curve. ;
[0020] Based on the fitted curve Obtaining the second derivative curve Thus, the second derivative curve is obtained. At the current pixel The value at that point is denoted as the curvature of the pixel on the current contour line. ;
[0021] Obtain the 24-neighborhood of each pixel on the contour line, and filter out the pixels on the contour line. The remaining pixels are recorded as the surrounding pixels of the pixels on the contour line.
[0022] Canny edge detection is used on static images to obtain the edges in the image and the gradient value of each pixel;
[0023] Mark the pixel with the largest gradient value among the surrounding pixels, which is greater than that of the center pixel; if no pixel among the surrounding pixels has a gradient value greater than that of the center pixel, then mark it as the center pixel; then obtain the distance between the marked pixel and the center pixel. ;
[0024] Based on formula Normalization yields the loss value for each pixel. Calculate the loss value for all pixels. The average value is used to obtain the loss value at the shell boundary. ;in, It is a very small integer, with a value of 0.001;
[0025] The pixels on the initial contour of the capsule boundary are gradually adjusted to both sides, and the loss value is recalculated after each adjustment. When the loss value Adjustment stops when the value is less than the preset threshold, and the boundary line at this point is the main boundary contour of the capsule.
[0026] Furthermore, the step of performing edge detection on pixels outside the main boundary contour of the capsule in the static image, filtering out candidate folded line segments that meet preset distance and angle conditions, and then clustering them, specifically involves:
[0027] In a static image, all pixels except those representing the main boundary contour of the capsule are denoted as the remaining pixels.
[0028] Perform Canny edge detection on the remaining pixels to obtain the edge lines and record them as candidate line segments;
[0029] For each candidate line segment, the minimum distance between the two endpoints and the main boundary contour of the capsule is obtained, and the endpoint with the smaller distance is regarded as the near endpoint; then the minimum distance between the near endpoint of the candidate line segment and the main boundary contour of the capsule is obtained. ;
[0030] Based on the tangent at the near endpoint of the candidate line segment Obtain the intersection point of the tangent and the main boundary contour of the capsule;
[0031] Based on the tangent at the intersection of the main boundary contour of the capsule Get the endpoint tangent Tangent at the intersection point The angle between ;
[0032] Minimum distance of the candidate line segments and included angle The minimum distance is obtained by comparing it with a preset threshold. Less than 5 and the included angle Line segments with an angle less than 90° are designated as candidate folded line segments;
[0033] Clustering is performed on the selected folded line segments to obtain several clusters of selected folded line segments, where each cluster represents several selected folded line segments within a local region.
[0034] Furthermore, the step coefficient is obtained by calculating the variance of distance and the variance of angle between line segments to distinguish between normal folds and clusters of line segments suspected of being damaged. Specifically:
[0035] For any line segment in the cluster of candidate folded line segments, obtain the distance between each line segment and its nearest line segment. and variance ;
[0036] For any line segment in the cluster of candidate folded line segments, the least squares method is used to obtain the fitted straight line of the line segment. ; Fitted straight line based on each line segment Angle with the horizontal line Get the included angle of all line segments in the same cluster. variance between The horizontal line refers to the reference straight line parallel to the horizontal axis of the ultrasound image coordinate system.
[0037] Step coefficient for each cluster of candidate folded line segments For each line segment in the cluster The mean is then normalized to obtain the result.
[0038] for The smaller the value, the smaller the minimum distance variance between line segments in the candidate folded line segment cluster, and the smaller the variance of the angle between the fitted straight line of the line segment and the horizontal line. Therefore, it indicates that the distances between line segments in the candidate folded line segment cluster are similar and more parallel. Thus, the larger the step coefficient of the cluster, the greater the possibility of it belonging to a suspected broken line segment cluster.
[0039] Furthermore, the process of processing the induced stress dynamic sequence in the ultrasound image data package, and using the minimum circumcircle of the cluster of normal folded line segments as the candidate region, extracting the vector sequence of feature points in the candidate region, specifically involves:
[0040] Based on the acquired dynamic sequence of induced stress, the sequence is registered between frames and aligned with the static image; and the minimum circumcircle of the cluster of normal folded line segments is recorded as the candidate region.
[0041] Dense optical flow is applied to adjacent frames in a dynamic sequence to obtain the optical flow vector field between all adjacent frames in the candidate region, thereby obtaining the vector sequence of each feature point; the feature point is a high-recognition pixel point obtained in the candidate region based on the Shi-Tomasi corner detection algorithm for tracking the deformation between dynamic image frames.
[0042] Furthermore, the process of dividing the vector sequence and fitting the resulting compression and rebound periods to obtain a fitting curve, combined with the step coefficient to obtain the response hysteresis, specifically involves:
[0043] Based on the time node of pressure application, the vector sequence of feature points is clearly divided into the pressurization period, the stabilization period, and the release and rebound period; the vector sequence of feature points in the candidate region located in the pressurization period and the rebound period is obtained;
[0044] Based on the vector magnitudes in the vector sequence during the pressurization period, a magnitude sequence is obtained; and the magnitude sequence is then subjected to least squares curve fitting to obtain a fitted curve. and its derivative curve ; and then obtain the derivative curve average slope Furthermore, based on the above operations, the fitted curve of the rebound period is obtained. And obtain the fitted curve. average ;
[0045] Based on formula Each feature point in the cluster The response hysteresis of the candidate region is obtained after mean normalization. Among them, for The smaller the value, the better the fitted curve. average slope The larger the value, and the better the fitted curve. The larger the average value, the larger the vector magnitude of the feature points in the candidate area becomes during the pressurization period, which is consistent with the elastic deformation process, and the deformation is large during the rebound period; therefore, it is consistent with the elastic deformation process, and thus the response hysteresis is lower.
[0046] Furthermore, the step of comparing the response lag with historical implant data and a clinical decision rule base to obtain the implant location and form a clinical decision specifically involves: comparing the response lag with historical implant data and a clinical decision rule base to obtain the implant location and form a clinical decision. After comparison with implant history records, the data is input into the clinical decision rule base, and when there is a response lag... When the value is in the range of 0-0.3, it is considered "stable," and the system archives the three-dimensional coordinates, XZ values, and analytical evidence chain of that region, updating it to the patient's long-term electronic follow-up atlas; when the response lag is... When the value is in the range of 0.3-0.7, it is judged as "suspicious." In addition to archived information, the system highlights this area in the structured report and automatically prompts for shortening the ultrasound follow-up interval or, under specific conditions, MRI confirmation. When the response lag is... When the value is in the range of 0.7-1.0, it is judged as "high risk". The system initiates an emergency warning and sends an alert message to the doctor's terminal, automatically retrieving multimodal original images of the area for the doctor's review; ultimately integrating response lag. Values, imaging evidence, system recommendations, and final physician opinions form a complete record of monitoring events.
[0047] A plastic surgery implant placement monitoring system, comprising:
[0048] The acquisition module acquires static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package.
[0049] The acquisition module preprocesses the static image in the ultrasound image data package to obtain the initial contour of the prosthesis implant shell; it calculates the curvature of the contour pixels and the gradient distance of the surrounding pixels to obtain the loss value, adjusts and optimizes the contour until the loss value meets the preset conditions, and obtains the main boundary contour of the prosthesis implant shell.
[0050] The differentiation module performs edge detection on pixels outside the main boundary contour of the capsule in the static image, filters out candidate folded line segments that meet the preset distance and angle conditions and clusters them, and obtains the step coefficient by calculating the distance variance and angle variance between line segments to distinguish between normal folds and suspected damaged line segment clusters.
[0051] The extraction module processes the induced stress dynamic sequence in the ultrasound image data package and uses the smallest circumcircle of the cluster of normal folded line segments as the candidate region to extract the vector sequence of feature points in the candidate region.
[0052] The fitting module divides the vector sequence and fits the resulting compression period and rebound period to obtain a fitting curve, and combines the step coefficient to obtain the response hysteresis.
[0053] The comparison module compares the response lag with the implant's historical data and the clinical decision rule base to obtain the implant's location and form a clinical decision.
[0054] Compared with the prior art, the present invention has the following beneficial effects:
[0055] This invention acquires structured multimodal ultrasound image data packages, combines anisotropic diffusion filtering and Otsu threshold segmentation to obtain an initial contour, and then optimizes it through curvature and gradient distance calculations to obtain a precise main boundary contour. Through edge detection, line segment screening and clustering, and step coefficient calculation, it efficiently distinguishes between normal folds and clusters of suspected damaged line segments. Utilizing induced stress dynamic sequence processing, feature point vector extraction, and compression / rebound period fitting, combined with step coefficient quantification of response hysteresis, it finally compares the results with historical data and a clinical decision rule base to form a tiered decision. This invention achieves objective assessment of implant location and structural integrity, provides traceable data for long-term follow-up, significantly improves the standardization and reliability of clinical monitoring, and provides precise evidence for diagnosis and treatment. Attached Figure Description
[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0057] Figure 1 This is a flowchart illustrating a method for monitoring the position of orthopedic implants according to the present invention.
[0058] Figure 2 This is a schematic diagram of the structure of a plastic surgery implant position monitoring system according to the present invention. Detailed Implementation
[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0060] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0061] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0062] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0063] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.
[0064] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.
[0065] The present invention will now be described in further detail with reference to the accompanying drawings:
[0066] See Figure 1 This invention discloses a method for monitoring the position of orthopedic implants, comprising:
[0067] S101: Acquire static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package;
[0068] A high-frequency linear array probe was selected, and coupling gel was applied before gently placing the probe on the skin surface. The target area of the patient was scanned using the high-frequency linear array probe to obtain a complete two-dimensional sonographic image including the overall outline of the prosthesis, adjacent breast and pectoralis major muscle structures, and the axillary region. High-resolution static images of key sections were acquired along the clock face of the prosthesis at the 12 o'clock, 3 o'clock, 6 o'clock, and 9 o'clock positions. The prosthesis's central and superior and inferior pole regions were focused on to capture the physiological movement trajectory of the prosthesis and its internal structures and surrounding tissues over at least two complete respiratory cycles, obtaining a dynamic image sequence of spontaneous breathing. A specific region of interest was selected, and a gentle, vertical, short-term pressure release stress was applied using the high-frequency linear array probe. Simultaneously, a dynamic image sequence of induced stress was recorded, recording the entire process from pressurization, steady state to sudden release and rebound. All image data were integrated and archived into a structured multimodal ultrasound image data package.
[0069] S102: Preprocess the static image in the ultrasound image data package to obtain the initial contour of the prosthesis implant shell; obtain the loss value by calculating the curvature of the contour pixels and the gradient distance of the surrounding pixels, adjust and optimize the contour until the loss value meets the preset conditions, and obtain the main boundary contour of the prosthesis implant shell.
[0070] The static images in the ultrasound image data package are preprocessed to obtain the initial contour of the prosthesis implant shell. Specifically, anisotropic diffusion filtering is performed on the multi-section high-resolution static images. Based on the characteristic that the gray value of the prosthesis implant shell is higher than that of the body tissue and the internal filling material of the prosthesis in the ultrasound image, Otsu threshold segmentation is performed on the filtered static images to extract the regions above the threshold. The largest connected component is selected from the high-threshold region, and the contour line of the connected component is used as the initial contour of the shell boundary.
[0071] The loss value is obtained by calculating the curvature of the contour pixels and the gradient distance of the surrounding pixels. The contour is then adjusted and optimized until the loss value meets the preset conditions, thus obtaining the main boundary contour of the implant capsule. Specifically:
[0072] For each pixel on the initial contour line of the capsule boundary, obtain the fitted curve between the pixel and its left and right adjacent pixels. Get the x-coordinate of the current pixel on the fitted curve. ;
[0073] Based on the fitted curve Obtaining the second derivative curve Thus, the second derivative curve is obtained. At the current pixel The value at that point is denoted as the curvature of the pixel on the current contour line. ;
[0074] Obtain the 24-neighborhood of each pixel on the contour line, and filter out the pixels on the contour line. The remaining pixels are recorded as the surrounding pixels of the pixels on the contour line.
[0075] Canny edge detection is used on static images to obtain the edges in the image and the gradient value of each pixel;
[0076] Mark the pixel with the largest gradient value among the surrounding pixels, which is greater than that of the center pixel; if no pixel among the surrounding pixels has a gradient value greater than that of the center pixel, then mark it as the center pixel; then obtain the distance between the marked pixel and the center pixel. ;
[0077] Based on formula Normalization yields the loss value for each pixel. Calculate the loss value for all pixels. The average value is used to obtain the loss value at the shell boundary. ;in, It is a very small integer, with a value of 0.001;
[0078] The pixels on the initial contour of the capsule boundary are gradually adjusted to both sides, and the loss value is recalculated after each adjustment. When the loss value Adjustment stops when the value is less than the preset threshold, and the boundary line at this point is the main boundary contour of the capsule.
[0079] S103: Perform edge detection on pixels outside the main boundary contour of the capsule in the static image, filter out candidate folded line segments that meet the preset distance and angle conditions and cluster them, obtain the step coefficient by calculating the distance variance and angle variance between line segments, and distinguish between normal folds and suspected damaged line segment clusters.
[0080] The step of edge detection of pixels outside the main boundary contour of the capsule in the static image, filtering out candidate folded line segments that meet preset distance and angle conditions, and clustering them is specifically as follows:
[0081] In a static image, all pixels except those representing the main boundary contour of the capsule are denoted as the remaining pixels.
[0082] Perform Canny edge detection on the remaining pixels to obtain the edge lines and record them as candidate line segments;
[0083] For each candidate line segment, the minimum distance between the two endpoints and the main boundary contour of the capsule is obtained, and the endpoint with the smaller distance is regarded as the near endpoint; then the minimum distance between the near endpoint of the candidate line segment and the main boundary contour of the capsule is obtained. ;
[0084] Based on the tangent at the near endpoint of the candidate line segment Obtain the intersection point of the tangent and the main boundary contour of the capsule;
[0085] Based on the tangent at the intersection of the main boundary contour of the capsule Get the endpoint tangent Tangent at the intersection point The angle between ;
[0086] Minimum distance of the candidate line segments and included angle The minimum distance is obtained by comparing it with a preset threshold. Less than 5 and the included angle Line segments with an angle less than 90° are designated as candidate folded line segments;
[0087] Clustering is performed on the selected folded line segments to obtain several clusters of selected folded line segments, where each cluster represents several selected folded line segments within a local region.
[0088] The step coefficient is obtained by calculating the variance of distance and the variance of angle between line segments, and this is used to distinguish between normal folds and clusters of line segments that are suspected of being damaged. Specifically:
[0089] For any line segment in the cluster of candidate folded line segments, obtain the distance between each line segment and its nearest line segment. and variance ;
[0090] For any line segment in the cluster of candidate folded line segments, the least squares method is used to obtain the fitted straight line of the line segment. ; Fitted straight line based on each line segment Angle with the horizontal line Get the included angle of all line segments in the same cluster. variance between The horizontal line refers to the reference straight line parallel to the horizontal axis of the ultrasound image coordinate system.
[0091] Step coefficient for each cluster of candidate folded line segments For each line segment in the cluster The mean is then normalized to obtain the result.
[0092] for The smaller the value, the smaller the minimum distance variance between line segments in the candidate folded line segment cluster, and the smaller the variance of the angle between the fitted straight line of the line segment and the horizontal line. Therefore, it indicates that the distances between line segments in the candidate folded line segment cluster are similar and more parallel. Thus, the larger the step coefficient of the cluster, the greater the possibility of it belonging to a suspected broken line segment cluster.
[0093] S104: Process the induced stress dynamic sequence in the ultrasound image data package, and use the minimum circumcircle of the cluster of normal folded line segments as the candidate region to extract the vector sequence of feature points in the candidate region;
[0094] Based on the acquired dynamic sequence of induced stress, the sequence is registered between frames and aligned with the static image; and the minimum circumcircle of the cluster of normal folded line segments is recorded as the candidate region.
[0095] Dense optical flow is applied to adjacent frames in a dynamic sequence to obtain the optical flow vector field between all adjacent frames in the candidate region, thereby obtaining the vector sequence of each feature point; the feature point is a high-recognition pixel point obtained in the candidate region based on the Shi-Tomasi corner detection algorithm for tracking the deformation between dynamic image frames.
[0096] S105: Divide the vector sequence and fit the obtained pressurization period and rebound period to obtain the fitting curve, and combine the step coefficient to obtain the response hysteresis.
[0097] Based on the time node of pressure application, the vector sequence of feature points is clearly divided into the pressurization period, the stabilization period, and the release and rebound period; the vector sequence of feature points in the candidate region located in the pressurization period and the rebound period is obtained;
[0098] Based on the vector magnitudes in the vector sequence during the pressurization period, a magnitude sequence is obtained; and the magnitude sequence is then subjected to least squares curve fitting to obtain a fitted curve. and its derivative curve ; and then obtain the derivative curve average slope Furthermore, based on the above operations, the fitted curve of the rebound period is obtained. And obtain the fitted curve. average ;
[0099] Based on formula Each feature point in the cluster The response hysteresis of the candidate region is obtained after mean normalization. Among them, for The smaller the value, the better the fitted curve. average slope The larger the value, and the better the fitted curve. The larger the average value, the larger the vector magnitude of the feature points in the candidate area becomes during the pressurization period, which is consistent with the elastic deformation process, and the deformation is large during the rebound period; therefore, it is consistent with the elastic deformation process, and thus the response hysteresis is lower.
[0100] S106: Compare the response lag with the implant’s historical data and clinical decision rule base to obtain the implant’s location and form a clinical decision.
[0101] Response lag After comparison with implant history records, the data is input into the clinical decision rule base, and when there is a response lag... When the value is in the range of 0-0.3, it is considered "stable," and the system archives the three-dimensional coordinates, XZ values, and analytical evidence chain of that region, updating it to the patient's long-term electronic follow-up atlas; when the response lag is... When the value is in the range of 0.3-0.7, it is judged as "suspicious." In addition to archived information, the system highlights this area in the structured report and automatically prompts for shortening the ultrasound follow-up interval or, under specific conditions, MRI confirmation. When the response lag is... When the value is in the range of 0.7-1.0, it is judged as "high risk". The system initiates an emergency warning and sends an alert message to the doctor's terminal, automatically retrieving multimodal original images of the area for the doctor's review; ultimately integrating response lag. Values, imaging evidence, system recommendations, and final physician opinions form a complete record of monitoring events.
[0102] See Figure 2 This invention discloses a plastic surgery implant position monitoring system, comprising:
[0103] The acquisition module acquires static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package.
[0104] The acquisition module preprocesses the static image in the ultrasound image data package to obtain the initial contour of the prosthesis implant shell; it calculates the curvature of the contour pixels and the gradient distance of the surrounding pixels to obtain the loss value, adjusts and optimizes the contour until the loss value meets the preset conditions, and obtains the main boundary contour of the prosthesis implant shell.
[0105] The differentiation module performs edge detection on pixels outside the main boundary contour of the capsule in the static image, filters out candidate folded line segments that meet the preset distance and angle conditions and clusters them, and obtains the step coefficient by calculating the distance variance and angle variance between line segments to distinguish between normal folds and suspected damaged line segment clusters.
[0106] The extraction module processes the induced stress dynamic sequence in the ultrasound image data package and uses the smallest circumcircle of the cluster of normal folded line segments as the candidate region to extract the vector sequence of feature points in the candidate region.
[0107] The fitting module divides the vector sequence and fits the resulting compression period and rebound period to obtain a fitting curve, and combines the step coefficient to obtain the response hysteresis.
[0108] The comparison module compares the response lag with the implant's historical data and the clinical decision rule base to obtain the implant's location and form a clinical decision.
[0109] Example:
[0110] This invention discloses a method for monitoring the position of orthopedic implants, comprising:
[0111] I. Constructing a Structured Multimodal Ultrasound Image Data Package
[0112] In a standard examination room, the patient lies supine with the ipsilateral arm raised to fully expose the breast and axillary region. The examiner applies coupling gel to a high-frequency linear array probe and gently places it on the skin. A systematic scan is first performed to acquire a complete two-dimensional sonographic image including the overall outline of the implant, adjacent breast and pectoralis major muscle structures, and the axillary region. High-resolution static images of key sections are then acquired along the clock face of the implant (e.g., 12 o'clock, 3 o'clock, 6 o'clock, 9 o'clock). The patient is then instructed to take calm, deep breaths, during which continuous dynamic images are acquired in the center and upper and lower poles of the implant to capture at least two complete images. The physiological movement trajectory of the prosthesis and its internal structure and surrounding tissues during the respiratory cycle; subsequently, with clear safety instructions and patient cooperation, the examiner selects a specific region of interest, applies a gentle, vertical, short-term pressure-release stress through the probe, and simultaneously records a dynamic image sequence of the entire process from pressurization, steady state to sudden release and rebound; finally, all standardized image data obtained from this examination—including multi-sectional static images, spontaneous breathing dynamic sequences, and induced stress dynamic sequences—are simultaneously integrated and archived to form a structured multimodal ultrasound image data package for subsequent in-depth analysis.
[0113] II. Analysis and extraction of the outer contour of the implant capsule
[0114] To monitor the position of a prosthesis implant, it is first necessary to determine the stability and integrity of the implant itself. When a prosthesis ruptures, the capsule on the surface of the implant will be damaged first, so it is necessary to analyze the contour of the capsule on the surface of the implant.
[0115] First, high-resolution static images of the patient with multiple cross-sections are acquired. Anisotropic diffusion filtering is applied to the high-resolution static images to preserve the edges in the images and suppress speckle noise. The capsule has a stronger reflective ability in ultrasound images compared to body tissues and silicone implants, so the capsule will show a higher grayscale value in the image.
[0116] Perform Otsu thresholding on the static image to obtain the portion of the image above the threshold; for the obtained high-threshold region, obtain the largest connected component and its contour line as the initial contour of the capsule boundary;
[0117] For each pixel on the acquired contour line, obtain the fitted curve between the pixel and its left and right adjacent pixels. And obtain the x-coordinate of the current pixel on the fitted curve. Based on the fitted curve Obtaining the second derivative curve Thus, the second derivative curve is obtained. At the current pixel The value at that point is denoted as the curvature of the pixel on the current contour line. ;
[0118] Obtain the 24-neighborhood of each pixel on the contour line, and filter out the contour line pixels. The remaining pixels are recorded as the surrounding pixels of the contour line pixels. Apply Canny edge detection to the static image to obtain the edges in the image and the gradient value of each pixel.
[0119] Mark the pixel with the largest gradient value among the surrounding pixels, which is greater than that of the center pixel; if no pixel among the surrounding pixels has a gradient value greater than that of the center pixel, then mark it as the center pixel; then obtain the distance between the marked pixel and the center pixel. ;
[0120] Since the normal capsule outline is continuous, smooth, and a bright echo line, the smaller the curvature and the larger the gradient value of the capsule edge in the image.
[0121] Based on formula Normalization yields the loss value for each pixel. ;in, The larger the value, the greater the distance between the pixel with the largest gradient value and the pixel on the initial contour of the capsule boundary, and the smaller the curvature of the pixel; therefore, it indicates that the location of the pixel changes more obviously and the gradient is smaller, which indicates that it is not completely the boundary of the capsule.
[0122] Calculate the loss value for all pixels. The average value is used to obtain the loss value at the shell boundary. ;in, The value is a very small integer, taking the value 0.001; the pixels on the initial contour of the capsule boundary are gradually adjusted to both sides, and the loss value is recalculated after each adjustment. When the loss value Adjustment stops when the value is less than the preset threshold. At this point, the set threshold is 0.3; the boundary line at this point is the main boundary contour of the capsule.
[0123] III. Analysis of damage outside the perimeter of the implant capsule
[0124] After obtaining the main boundary contour of the capsule, if the capsule boundary is damaged, it may cause fine linear echo signals related to the damaged capsule fragments to appear outside the main boundary contour. Therefore, capsule boundary detection is required. Pixels other than the main boundary contour pixels in the static image are obtained and recorded as remaining pixels. Canny edge detection is then performed on the remaining pixels to obtain edge lines, which are recorded as candidate line segments. Candidate line segments may include edges caused by damage or edges caused by capsule folding, so they need to be filtered.
[0125] For each candidate line segment, the minimum distance between the two endpoints and the main boundary contour of the capsule is obtained, and the endpoint with the smaller distance is regarded as the near endpoint; then the minimum distance between the near endpoint of the candidate line segment and the main boundary contour of the capsule is obtained. Based on the tangent at the near endpoint of the candidate line segment Obtain the intersection point of the tangent line and the main boundary contour of the capsule; based on the tangent line at the intersection point on the main boundary contour of the capsule... Get the endpoint tangent Tangent at the intersection point The angle between ; the minimum distance of the line segments to be selected and included angle The minimum distance is obtained by comparing it with a preset threshold. Less than 5 and the included angle Line segments with an angle less than 90° are designated as candidate folded line segments;
[0126] Because early shell damage may result in fragments at the damaged part of the shell not completely detaching from the shell, the selected line segments are smoothly connected to the shell and are relatively close.
[0127] Under normal circumstances, folds and wrinkles are caused by compression, so the wrinkled areas will appear rather messy in the image; however, after the capsule is damaged, the collapsed capsule will be stacked in multiple layers, thus forming a specific step-like feature of capsule damage; clustering is performed on the obtained candidate fold segments to obtain several candidate fold segment clusters, where each cluster represents several candidate fold segments in a local area.
[0128] For any line segment in the cluster of candidate folded line segments, obtain the distance between each line segment and its nearest line segment. and variance ;
[0129] For any line segment in the cluster of candidate folded line segments, the least squares method is used to obtain the fitted straight line of the line segment. ; Fitted straight line based on each line segment Angle with the horizontal line Get the included angle of all line segments in the same cluster. variance between The horizontal line refers to the reference straight line in the ultrasound image coordinate system that is parallel to the horizontal axis of the image.
[0130] Step coefficient for each cluster of candidate folded line segments For each line segment in the cluster The mean is then normalized to obtain the result.
[0131] for The smaller the value, the smaller the minimum distance variance between line segments in the candidate folded line segment cluster, and the smaller the variance of the angle between the fitted straight line of the line segment and the horizontal line. Therefore, it indicates that the distances between line segments in the candidate folded line segment cluster are similar and more parallel. Thus, the larger the step coefficient of the cluster, the greater the possibility of it belonging to a suspected broken line segment cluster.
[0132] IV. Analysis of the pressure response pattern of the implant capsule
[0133] After obtaining the step coefficient of the candidate folded line segment cluster, it can reflect whether the candidate line segment at the current position conforms to the step characteristics caused by damage; however, it may be due to the folding of the shell edge causing the folded edges to stack on each other, resulting in step characteristics similar to the damaged part; therefore, it is necessary to perform pressure response mode analysis on the candidate folded line segment cluster.
[0134] Based on the acquired dynamic sequence of induced stress, the sequence is registered between frames and aligned with the static image; and the minimum circumcircle of the cluster of normal folded line segments is recorded as the candidate region.
[0135] Dense optical flow is applied to adjacent frames in a dynamic sequence to obtain the optical flow vector field between all adjacent frames in the candidate region, thereby obtaining the vector sequence of each feature point; the feature point is a high-recognition pixel point obtained in the candidate region based on the Shi-Tomasi corner detection algorithm for tracking the deformation between dynamic image frames.
[0136] Based on the time node of pressure application, the vector sequence of feature points is clearly divided into the pressurization period, the stabilization period, and the release and rebound period; the vector sequence of feature points in the candidate region located in the pressurization period and the rebound period is obtained;
[0137] Based on the vector magnitudes in the vector sequence during the pressurization period, a magnitude sequence is obtained; and the magnitude sequence is then subjected to least squares curve fitting to obtain a fitted curve. and its derivative curve ; and then obtain the derivative curve average slope Furthermore, based on the above operations, the fitted curve of the rebound period is obtained. And obtain the fitted curve. average ;
[0138] Based on formula Each feature point in the cluster The response hysteresis of the candidate region is obtained after mean normalization. Among them, for The smaller the value, the better the fitted curve. average slope The larger the value, and the better the fitted curve. The larger the average value, the larger the vector magnitude of the feature points in the candidate area becomes during the pressurization period, which is consistent with the elastic deformation process, and the deformation is large during the rebound period; therefore, it is consistent with the elastic deformation process, and thus the response hysteresis is lower.
[0139] V. Monitoring the placement of plastic implants
[0140] The response lag (XZ) of the candidate region is used as the core quantitative indicator. After the system completes the analysis of the standardized full-modal data package and generates the response lag (XZ) of the candidate region, it immediately compares it with the implant's historical records and inputs it into a preset clinical decision rule base. The rule base automatically triggers a graded response based on the range of the XZ value (e.g., 0-0.3 is "stable", 0.3-0.7 is "questionable", and 0.7-1.0 is "high risk"). For "stable" regions, the system only archives its three-dimensional coordinates, X-value, and analytical evidence chain and updates it to the patient's long-term electronic follow-up map. For "questionable" regions, in addition to archiving, the system also updates the generated structured report. The system highlights high-risk areas and automatically suggests shortening the interval between ultrasound follow-ups or performing MRI confirmation under specific conditions. For "high-risk" areas, the system will activate an emergency warning, send a warning message to the doctor's terminal, and clearly indicate the type, location, and confidence level of the suspected rupture in the report. At the same time, it will automatically retrieve multimodal original images of the area (such as static feature maps and dynamic sequences) for the doctor to review urgently. Finally, all information (including XZ values, corresponding imaging evidence, system suggestions, and the doctor's final review opinion) will be integrated to form a complete monitoring event record, realizing a closed loop from image analysis to clinical decision-making, and providing continuous, quantitative, and traceable objective evidence for the stability assessment of implants.
[0141] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for monitoring the position of orthopedic implants, characterized in that, include: Acquire static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package; The static images in the ultrasound image data package are preprocessed to obtain the initial contour of the prosthesis implant shell; the loss value is obtained by calculating the curvature of the contour pixels and the gradient distance of the surrounding pixels; the contour is adjusted and optimized until the loss value meets the preset conditions, and the main boundary contour of the prosthesis implant shell is obtained. Edge detection is performed on pixels outside the main boundary contour of the capsule in static images. Candidate folded line segments that meet the preset distance and angle conditions are selected and clustered. The step coefficient is obtained by calculating the distance variance and angle variance between line segments to distinguish between normal folds and suspected damaged line segment clusters. The induced stress dynamic sequence in the ultrasound image data package is processed, and the minimum circumcircle of the cluster of normal folded line segments is taken as the candidate region. The vector sequence of feature points in the candidate region is extracted. The vector sequence is divided, and the pressure period and rebound period obtained from the division are fitted to obtain the fitting curve. The response hysteresis is obtained by combining the step coefficient. The response lag is compared with the implant's historical data and clinical decision rule base to determine the implant location and form a clinical decision.
2. The method for monitoring the position of orthopedic implants according to claim 1, characterized in that, The acquisition of static images and dynamic image sequences of the target area is used to construct a structured multimodal ultrasound image data package, specifically as follows: A high-frequency linear array probe was selected, and coupling gel was applied before gently placing the probe on the skin surface. The target area of the patient was scanned using the high-frequency linear array probe to obtain a complete two-dimensional sonographic image including the overall outline of the prosthesis, adjacent breast and pectoralis major muscle structures, and the axillary region. High-resolution static images of key sections were acquired along the clock face of the prosthesis at the 12 o'clock, 3 o'clock, 6 o'clock, and 9 o'clock positions. The prosthesis's central and superior and inferior pole regions were focused on to capture the physiological movement trajectory of the prosthesis and its internal structures and surrounding tissues over at least two complete respiratory cycles, obtaining a dynamic image sequence of spontaneous breathing. A specific region of interest was selected, and a gentle, vertical, short-term pressure release stress was applied using the high-frequency linear array probe. Simultaneously, a dynamic image sequence of induced stress was recorded, recording the entire process from pressurization, steady state to sudden release and rebound. All image data were integrated and archived into a structured multimodal ultrasound image data package.
3. The method for monitoring the position of orthopedic implants according to claim 2, characterized in that, The static images in the ultrasound image data package are preprocessed to obtain the initial contour of the prosthesis implant shell. Specifically, anisotropic diffusion filtering is performed on the multi-section high-resolution static images. Based on the characteristic that the gray value of the prosthesis implant shell in the ultrasound image is higher than that of the body tissue and the internal filling material of the prosthesis, the filtered static images are subjected to Otsu threshold segmentation to extract the regions above the threshold. The largest connected component is selected from the high-threshold region, and the contour line of the connected component is used as the initial contour of the shell boundary.
4. The method for monitoring the position of orthopedic implants according to claim 3, characterized in that, The loss value is obtained by calculating the curvature of the contour pixels and the gradient distance of the surrounding pixels. The contour is then adjusted and optimized until the loss value meets the preset conditions to obtain the main boundary contour of the prosthesis implant shell. Specifically: For each pixel on the initial contour line of the capsule boundary, obtain the fitted curve between the pixel and its left and right adjacent pixels. Get the x-coordinate of the current pixel on the fitted curve. ; Based on the fitted curve Obtaining the second derivative curve Thus, the second derivative curve is obtained. At the current pixel The value at that point is denoted as the curvature of the pixel on the current contour line. ; Obtain the 24-neighborhood of each pixel on the contour line, and filter out the pixels on the contour line. The remaining pixels are recorded as the surrounding pixels of the pixels on the contour line. Canny edge detection is used on static images to obtain the edges in the image and the gradient value of each pixel; Mark the pixel with the largest gradient value among the surrounding pixels, which is greater than that of the center pixel; if no pixel among the surrounding pixels has a gradient value greater than that of the center pixel, then mark it as the center pixel; then obtain the distance between the marked pixel and the center pixel. ; Based on formula Normalization yields the loss value for each pixel. Calculate the loss value for all pixels. The average value is used to obtain the loss value at the shell boundary. ;in, It is a very small integer, with a value of 0.001; The pixels on the initial contour of the capsule boundary are gradually adjusted to both sides, and the loss value is recalculated after each adjustment. When the loss value Adjustment stops when the value is less than the preset threshold, and the boundary line at this point is the main boundary contour of the capsule.
5. The method for monitoring the position of orthopedic implants according to claim 4, characterized in that, The step of edge detection of pixels outside the main boundary contour of the capsule in the static image, filtering out candidate folded line segments that meet preset distance and angle conditions, and clustering them is specifically as follows: In a static image, all pixels except those representing the main boundary contour of the capsule are denoted as the remaining pixels. Perform Canny edge detection on the remaining pixels to obtain the edge lines and record them as candidate line segments; For each candidate line segment, the minimum distance between the two endpoints and the main boundary contour of the capsule is obtained, and the endpoint with the smaller distance is regarded as the near endpoint; then the minimum distance between the near endpoint of the candidate line segment and the main boundary contour of the capsule is obtained. ; Based on the tangent at the near endpoint of the candidate line segment Obtain the intersection point of the tangent and the main boundary contour of the capsule; Based on the tangent at the intersection of the main boundary contour of the capsule Get the endpoint tangent Tangent at the intersection point The angle between ; Minimum distance of the candidate line segment and included angle The minimum distance is obtained by comparing it with a preset threshold. Less than 5 and the included angle Line segments with an angle less than 90° are designated as candidate folded line segments; Clustering is performed on the selected folded line segments to obtain several clusters of selected folded line segments, where each cluster represents several selected folded line segments within a local region.
6. The method for monitoring the position of orthopedic implants according to claim 5, characterized in that, The step coefficient is obtained by calculating the variance of distance and the variance of angle between line segments, and this is used to distinguish between normal folds and clusters of line segments that are suspected of being damaged. Specifically: For any line segment in the cluster of candidate folded line segments, obtain the distance between each line segment and its nearest line segment. and variance ; For any line segment in the cluster of candidate folded line segments, the least squares method is used to obtain the fitted straight line of the line segment. ; Fitted straight line based on each line segment Angle with the horizontal line Get the included angle of all line segments in the same cluster. variance between The horizontal line refers to the reference straight line parallel to the horizontal axis of the ultrasound image coordinate system. Step coefficient for each cluster of candidate folded line segments For each line segment in the cluster The mean is then normalized to obtain the result. for The smaller the value, the smaller the minimum distance variance between line segments in the candidate folded line segment cluster, and the smaller the variance of the angle between the fitted straight line of the line segment and the horizontal line. Therefore, it indicates that the distances between line segments in the candidate folded line segment cluster are similar and more parallel. Thus, the larger the step coefficient of the cluster, the greater the possibility of it belonging to a suspected broken line segment cluster.
7. The method for monitoring the position of orthopedic implants according to claim 6, characterized in that, The process involves processing the induced stress dynamic sequence in the ultrasound image data package and using the smallest circumcircle of the cluster of normal folded line segments as the candidate region. Specifically, the vector sequence of feature points within the candidate region is extracted as follows: Based on the acquired dynamic sequence of induced stress, the sequence is registered between frames and aligned with the static image; and the minimum circumcircle of the cluster of normal folded line segments is recorded as the candidate region. Dense optical flow is applied to adjacent frames in a dynamic sequence to obtain the optical flow vector field between all adjacent frames in the candidate region, thereby obtaining the vector sequence of each feature point; the feature point is a high-recognition pixel point obtained in the candidate region based on the Shi-Tomasi corner detection algorithm for tracking the deformation between dynamic image frames.
8. The method for monitoring the position of orthopedic implants according to claim 7, characterized in that, The process involves dividing the vector sequence, fitting the resulting pressurization and rebound periods to obtain a fitted curve, and combining this curve with the step coefficient to obtain the response hysteresis. Specifically: Based on the time node of pressure application, the vector sequence of feature points is clearly divided into the pressurization period, the stabilization period, and the release and rebound period; the vector sequence of feature points in the candidate region located in the pressurization period and the rebound period is obtained; Based on the vector magnitudes in the vector sequence during the pressurization period, a magnitude sequence is obtained; and the magnitude sequence is then subjected to least squares curve fitting to obtain a fitted curve. and its derivative curve ; and then obtain the derivative curve average slope Furthermore, based on the above operations, the fitted curve of the rebound period is obtained. And obtain the fitted curve. average ; Based on formula Each feature point in the cluster The response hysteresis of the candidate region is obtained after mean normalization. Among them, for The smaller the value, the better the fitted curve. average slope The larger the value, and the better the fitted curve. The larger the average value, the larger the vector magnitude of the feature points in the candidate area becomes during the pressurization period, which is consistent with the elastic deformation process, and the deformation is large during the rebound period; therefore, it is consistent with the elastic deformation process, and thus the response hysteresis is lower.
9. The method for monitoring the position of orthopedic implants according to claim 8, characterized in that, The process of comparing response lag with historical implant data and a clinical decision rule base to obtain implant location and form clinical decisions specifically involves: comparing response lag with historical implant data and a clinical decision rule base to obtain implant location and form clinical decisions. After comparison with implant history records, the data is input into the clinical decision rule base, and when there is a response lag... When the value is in the range of 0-0.3, it is considered "stable," and the system archives the three-dimensional coordinates, XZ values, and analytical evidence chain of that region, updating it to the patient's long-term electronic follow-up atlas; when the response lag is... When the value is in the range of 0.3-0.7, it is judged as "suspicious." In addition to archived information, the system highlights this area in the structured report and automatically prompts for shortening the ultrasound follow-up interval or, under specific conditions, MRI confirmation. When the response lag is... When the value is in the range of 0.7-1.0, it is judged as "high risk". The system initiates an emergency warning and sends an alert message to the doctor's terminal, automatically retrieving multimodal original images of the area for the doctor's review; ultimately integrating response lag. Values, imaging evidence, system recommendations, and final opinions from doctors are used to form a complete record of monitoring events.
10. A system for monitoring the position of orthopedic implants, characterized in that, include: The acquisition module acquires static images and dynamic image sequences of the target area to construct a structured multimodal ultrasound image data package. The acquisition module preprocesses the static image in the ultrasound image data package to obtain the initial contour of the prosthesis implant shell; it calculates the curvature of the contour pixels and the gradient distance of the surrounding pixels to obtain the loss value, adjusts and optimizes the contour until the loss value meets the preset conditions, and obtains the main boundary contour of the prosthesis implant shell. The differentiation module performs edge detection on pixels outside the main boundary contour of the capsule in the static image, filters out candidate folded line segments that meet the preset distance and angle conditions and clusters them, and obtains the step coefficient by calculating the distance variance and angle variance between line segments to distinguish between normal folds and suspected damaged line segment clusters. The extraction module processes the induced stress dynamic sequence in the ultrasound image data package and uses the smallest circumcircle of the cluster of normal folded line segments as the candidate region to extract the vector sequence of feature points in the candidate region. The fitting module divides the vector sequence and fits the resulting compression period and rebound period to obtain a fitting curve, and combines the step coefficient to obtain the response hysteresis. The comparison module compares the response lag with the implant's historical data and the clinical decision rule base to obtain the implant's location and form a clinical decision.