Dynamic path planning method and system for anterior cruciate ligament tendon dissection

Abstract

The invention discloses a dynamic path planning method and system for anterior cruciate ligament tendon dissection, and relates to the technical field of ligament tendon dissection, and the method comprises the following steps: obtaining anatomical structure data of an anterior cruciate ligament tendon and tissue characteristic parameters of a to-be-dissected region; marking key monitoring points in the to-be-stripped area to obtain stripping monitoring points; counting the space distance and the tissue density difference value between the adjacent stripping monitoring points to obtain a to-be-measured space parameter; historical tissue stress distribution characteristics of different anatomical structure types are extracted from historical anterior cruciate ligament tendon dissection operation data, and according to the historical tissue stress distribution characteristics, a dissection path deviation value in the historical operation data and a path correction correlation factor between the to-be-measured spatial parameters are counted; according to the anatomical structure data and the tissue characteristic parameters of the current tendon to be stripped, the effects are that operation errors caused by path deviation are reduced, and the accuracy of operation is improved.

Description

Technical Field

[0001] This invention relates to the field of ligament and tendon stripping technology, and more specifically, to a dynamic path planning method and system for anterior cruciate ligament and tendon stripping. Background Technology

[0002] In orthopedic surgery, anterior cruciate ligament (ACL) tendon stripping significantly impacts surgical outcomes and patient recovery. Traditional methods rely heavily on surgeon experience, thus presenting significant limitations. The anatomy of the ACL tendon varies greatly among individuals, with complex and diverse tendon lengths, diameters, and surrounding tissue relationships. Experience-based procedures struggle to provide precise adaptation, often resulting in incomplete or excessive stripping due to poor path planning, thus affecting subsequent ligament reconstruction. Furthermore, the lack of assessment of tissue stress and injury risks allows for deviations in surgical path, increasing the likelihood of tendon and surrounding neurovascular damage, thereby prolonging postoperative complications and extending the patient's recovery period. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a dynamic path planning method and system for anterior cruciate ligament tendon stripping.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A dynamic path planning method for anterior cruciate ligament tendon stripping, the method comprising the following steps: Obtain anatomical data of the anterior cruciate ligament tendon and tissue characteristic parameters of the area to be dissected, and mark key monitoring points in the area to be dissected to obtain dissection monitoring points; The spatial parameters to be measured are obtained by statistically analyzing the spatial distance and tissue density differences between adjacent stripping monitoring points; From historical anterior cruciate ligament tendon stripping surgery data, historical tissue stress distribution characteristics of different anatomical structure types were extracted, and the path correction correlation factor between the stripping path deviation value and the spatial parameter to be measured in the historical surgical data was statistically determined based on the historical tissue stress distribution characteristics. Based on the anatomical structure data and tissue characteristic parameters of the tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor. The stripping path trajectory at the key location of the tendon to be stripped is determined based on the preprocessing correction factor, and the first path trajectory and the second path trajectory are obtained. Based on the first and second path trajectories, a comprehensive path deviation factor is calculated. Based on the comprehensive path deviation factor, the risk value of tissue damage caused by path deviation during the dissection of the tendon to be dissected is judged, and the current damage risk value is obtained. The optimal adjustment path can be obtained based on the current damage risk value.

[0005] Preferably, the stripping monitoring points are obtained by marking key monitoring points in the area to be stripped, specifically including the following steps: Obtain the total length and cross-sectional diameter of the tendon to be stripped in the area to be stripped; The first monitoring point is obtained by marking the key monitoring points at the tendon attachment points in the area to be stripped. The key monitoring points near the free end of the tendon in the area to be dissected were marked to obtain the second monitoring point; Sub-monitoring points are obtained by marking sub-monitoring points in the area to be dissected based on the total length of the tendon, the cross-sectional diameter, the first monitoring point, and the second monitoring point. The first monitoring point, the second monitoring point, and the sub-monitoring point are combined to form the stripping monitoring point.

[0006] Preferably, the path correction correlation factor between the dissection path deviation value and the spatial parameter to be measured in historical surgical data is statistically determined based on the historical tissue stress distribution characteristics. This specifically includes the following steps: Based on the historical stress distribution characteristics, the historical peeling path deviation value of the peeling monitoring point is extracted from the historical surgical data. The deviation value is obtained by subtracting the path deviations of adjacent stripping monitoring points in the historical stripping path deviation values ​​based on the spatial parameters to be measured. The ratio of the deviation value to the measured spatial parameter is used to obtain the measured deviation ratio. The path correction correlation factor is obtained by averaging all the deviation ratios to be measured.

[0007] Preferably, based on the anatomical structure data and tissue characteristic parameters of the tendon to be dissected, a preprocessing correction factor is extracted from the path correction correlation factor, specifically including the following steps: Extract the current tissue stress distribution characteristics based on the anatomical structure data and tissue feature parameters of the tendon to be stripped; The current stress distribution characteristics of the organization are matched with the historical stress distribution characteristics to extract the preprocessing correction factor from the path correction correlation factor.

[0008] Preferably, the dissection path trajectory at the key location of the tendon to be dissected is determined based on the preprocessing correction factor to obtain the first path trajectory and the second path trajectory, specifically including the following steps: Obtain the key functional areas of the tendon to be stripped; The location of the key functional areas within the area to be stripped is processed to obtain the target location points; The spatial distance and tissue density difference between the target location point and the first monitoring point are statistically analyzed to obtain the first preprocessing parameters; The spatial distance and tissue density difference between the target location point and the second monitoring point are statistically analyzed to obtain the second preprocessing parameter; Obtain the tissue hardness data of the main peeling area of ​​the current area to be peeled; The initial peeling force value to be measured is obtained by detecting the initial peeling force of the first monitoring point based on the tissue hardness data of the main peeling area, and the initial peeling force of the second monitoring point is the same as that of the first monitoring point. The first path trajectory to which the target location point belongs is determined based on the first preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor. The second path trajectory to which the target location point belongs is determined based on the second preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor.

[0009] Preferably, the comprehensive path deviation factor is calculated based on the first path trajectory and the second path trajectory, specifically including the following steps: The pretreatment strain frequency was obtained by detecting the strain frequency of the tissue at the first monitoring point; The first damage risk factor is obtained by multiplying the path deviation value of the first path trajectory with the pre-treatment strain frequency. The second damage risk factor is obtained by multiplying the path deviation value of the second path trajectory with the pre-treatment strain frequency. The comprehensive path deviation factor is obtained by summing the first damage risk factor and the second damage risk factor.

[0010] Preferably, the current damage risk value is obtained by judging the tissue damage risk value of the tendon to be dissected due to path deviation during the dissection process based on the comprehensive path deviation factor, specifically including the following steps: Obtain a damage database under different path deviation values ​​and different tissue strain frequencies; The current injury risk value of the tendon to be stripped is obtained by matching the comprehensive path deviation factor with the injury database.

[0011] Preferably, the optimal adjustment path is obtained based on the current damage risk value, specifically including the following steps: If the current damage risk value is greater than or equal to the preset tissue damage warning threshold, a path adjustment notification will be output. Based on the path adjustment notification information, the optimized removal path is obtained by determining the optimized adjustment path of the tendon to be removed. The high-stress areas of the auxiliary paths are obtained by collecting data from the high-stress areas of each optimized stripping path. Stress characteristic data of the auxiliary path are obtained by detecting the stress intensity and area of ​​the high-stress region in the auxiliary path. The stress intensity and area of ​​the high-stress region in the main peeling area are detected to obtain the stress characteristic data of the main path. The optimal adjustment path is extracted from the optimized stripping path, where the stress characteristic data of the auxiliary path is less than that of the main path.

[0012] A dynamic path planning system for anterior cruciate ligament tendon dissection includes: Acquisition module: Acquires anatomical structure data of the anterior cruciate ligament tendon and tissue characteristic parameters of the area to be dissected, and marks key monitoring points in the area to be dissected to obtain dissection monitoring points; Statistical module: Calculates the spatial parameters to be measured by analyzing the differences in spatial distance and tissue density between adjacent stripping monitoring points; Extraction module: Extracts historical tissue stress distribution characteristics of different anatomical structure types from historical anterior cruciate ligament tendon stripping surgery data, and calculates the path correction correlation factor between the stripping path deviation value and the spatial parameter to be measured in the historical surgical data based on the historical tissue stress distribution characteristics. Path determination module: Based on the anatomical structure data and tissue feature parameters of the tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor. Based on the preprocessing correction factor, the stripping path trajectory at the key position of the tendon to be stripped is determined, and the first path trajectory and the second path trajectory are obtained. Statistical judgment module: Based on the first path trajectory and the second path trajectory, a comprehensive path deviation factor is calculated. Based on the comprehensive path deviation factor, the risk value of tissue damage caused by path deviation during the dissection of the tendon to be dissected is judged to obtain the current damage risk value. Path determination module: Optimizes and adjusts the path based on the current damage risk value.

[0013] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic path planning method for anterior cruciate ligament tendon stripping.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes statistical analysis of spatial parameters to meticulously grasp the spatial and tissue differences between adjacent monitoring points, allowing path planning to better align with actual anatomical features. By extracting path correction correlation factors from historical data and combining them with current surgical data to extract preprocessing correction factors, path trajectory judgment becomes more accurate. This ensures the dissection path conforms maximally to the tendon's anatomical structure, reducing surgical errors caused by path deviations and improving surgical precision. By detecting tissue strain frequency and calculating damage risk factors based on path deviation values, and then matching this with a damage database to obtain the current damage risk value, the risk of tissue damage during surgery can be predicted in advance. Based on the risk value, the path is optimized and adjusted to proactively avoid high-stress areas, neurovascular bundles, and other dangerous areas, effectively reducing the probability of surgical damage to the tendon and surrounding tissues. Attached Figure Description

[0015] Figure 1 A schematic diagram illustrating the steps of a dynamic path planning method for anterior cruciate ligament tendon stripping proposed in this invention; Figure 2 A schematic diagram of a dynamic path planning system for anterior cruciate ligament tendon stripping proposed in this invention. Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0016] 610. Processor; 620. Communication interface; 630. Memory; 640. Communication bus. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-3 As shown.

[0021] The embodiments further illustrate the dynamic path planning method and system for anterior cruciate ligament tendon stripping proposed in this invention.

[0022] A dynamic path planning method for anterior cruciate ligament tendon stripping, the method comprising the following steps: Obtain anatomical data of the anterior cruciate ligament tendon and tissue characteristic parameters of the area to be dissected, and mark key monitoring points in the area to be dissected to obtain dissection monitoring points; The spatial parameters to be measured are obtained by statistically analyzing the spatial distance and tissue density differences between adjacent stripping monitoring points; From historical anterior cruciate ligament tendon stripping surgery data, historical tissue stress distribution characteristics of different anatomical structure types were extracted, and the path correction correlation factor between the stripping path deviation value and the spatial parameter to be measured in the historical surgical data was statistically determined based on the historical tissue stress distribution characteristics. Based on the anatomical structure data and tissue characteristic parameters of the tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor. The stripping path trajectory at the key location of the tendon to be stripped is determined based on the preprocessing correction factor, and the first path trajectory and the second path trajectory are obtained. Based on the first and second path trajectories, a comprehensive path deviation factor is calculated. Based on the comprehensive path deviation factor, the risk value of tissue damage caused by path deviation during the dissection of the tendon to be dissected is judged, and the current damage risk value is obtained. The optimal adjustment path can be obtained based on the current damage risk value.

[0023] This application first marks the dissection monitoring points. The total length and cross-sectional diameter of the tendon in the area to be dissected are obtained by high-frequency ultrasound imaging equipment to clarify the basic size parameters of the tendon. The tendon attachment point is marked as the first monitoring point, and the free end of the tendon is marked as the second monitoring point. These two endpoints correspond to the key positions of the start and end of the dissection operation, respectively. Sub-monitoring points are obtained by marking sub-monitoring points in the area to be dissected based on the total tendon length, cross-sectional diameter, first monitoring point and second monitoring point.

[0024] The three-dimensional coordinates of adjacent peeling monitoring points are obtained by an electromagnetic positioning system, and then the spatial distance between the two points is calculated. At the same time, the tissue density data of the location of the adjacent peeling monitoring points are extracted by using the gray value of the high-frequency ultrasound image, and the density difference is calculated. These two parameters are correlated and integrated to obtain the spatial parameters to be measured. The spatial parameters to be measured reflect the differences in spatial location and tissue composition of adjacent areas.

[0025] First, a large database of historical surgical cases was used to classify tendon anatomy and extract historical tissue stress distribution characteristics corresponding to different types, including key information such as stress peak location and stress transmission path. Based on these stress distribution characteristics, the location corresponding to the current monitoring point in each historical surgery was located, and the historical dissection path deviation value at these locations was extracted, i.e., the spatial deviation between the actual dissection path and the ideal path. For each group of adjacent monitoring points, the historical path deviation value was calculated by combining the corresponding spatial parameters to be measured to obtain the deviation in the local area. Then, the deviation value was calculated as a ratio to the corresponding spatial parameters to be measured to obtain the deviation ratio, thereby quantifying the degree of path deviation corresponding to a unit spatial parameter. The deviation ratios of historical cases with the same anatomical type and similar spatial parameters to be measured were averaged to obtain the path correction correlation factor. This factor clearly reflects the influence of spatial parameters on path deviation under specific anatomical and tissue characteristics.

[0026] Stress data is collected by intraoperative tension sensors, and tissue deformation is analyzed using ultrasound images to obtain the stress distribution characteristics of the tendon to be dissected. The current stress distribution characteristics are then matched with stress characteristics of different anatomical types in a historical database to identify path correction correlation factors corresponding to historical cases with high matching degrees. These factors are used as preprocessing correction factors to ensure a high degree of fit between the factor and the anatomical features of the current surgery. Image recognition algorithms are then used to extract key functional areas of the tendon from the ultrasound images, such as areas with dense fiber bundles and areas adjacent to neurovascular bundles. Path deviations in these areas are prone to serious damage and therefore require special attention. Three-dimensional coordinate positioning of the key functional areas is used to obtain the target location point. The spatial distance and tissue density difference between the target location point and the first and second monitoring points are calculated to obtain the first and second preprocessing parameters. Simultaneously, the tissue hardness of the main dissection area is detected by tension sensors, and the initial dissection force value to be measured at the first monitoring point is inferred to ensure the stability of the dissection initiation force. Finally, combining the first preprocessing parameters, the initial dissection force value, and the preprocessing correction factor, the optimal dissection path at the target location point is obtained using a mechanical simulation model, thus obtaining the first path trajectory. The second path trajectory is obtained by combining the second preprocessing parameters in the same way.

[0027] First, strain gauge sensors are used to detect the tissue strain frequency at the first monitoring point during the dissection preparation stage. This frequency reflects tissue stability; the higher the frequency, the stronger the tissue's sensitivity to path deviation. The actual deviation values ​​of the first and second path trajectories are multiplied by the strain frequency to obtain the first and second damage risk factors, quantifying the risk level of a single path. The two risk factors are summed to obtain a comprehensive path deviation factor, which comprehensively reflects the overall risk level of the two paths. A pre-established damage database is called, which contains correlation data between different path deviation values, different strain frequencies, and corresponding tissue damage risk values. The current comprehensive path deviation factor is matched with the database data to obtain the current damage risk value, intuitively reflecting the safety level of path implementation.

[0028] If the current damage risk value is greater than or equal to the preset tissue damage warning threshold, a path adjustment notification is output. Multiple optimized dissection paths are generated based on the current tendon anatomy, stress distribution, and spatial parameters to be measured, with all paths avoiding high-risk areas. The high-stress areas of each optimized path are located, and the stress intensity and area of ​​these areas are collected to obtain auxiliary path stress characteristic data. Simultaneously, stress data from the original main dissection area is collected as the main path stress characteristic data. The stress characteristics of the auxiliary and main paths of each optimized path are compared, and the optimized path with lower auxiliary path stress intensity and smaller high-stress area is selected as the final preferred adjustment path. This ensures that the path meets the dissection requirements while minimizing tissue damage risk, providing dynamic guidance for the surgical procedure.

[0029] The process of marking key monitoring points in the area to be stripped to obtain stripping monitoring points includes the following steps: Obtain the total length and cross-sectional diameter of the tendon to be stripped in the area to be stripped; The first monitoring point is obtained by marking the key monitoring points at the tendon attachment points in the area to be stripped. The key monitoring points near the free end of the tendon in the area to be dissected were marked to obtain the second monitoring point; Sub-monitoring points are obtained by marking sub-monitoring points in the area to be dissected based on the total length of the tendon, the cross-sectional diameter, the first monitoring point, and the second monitoring point. The first monitoring point, the second monitoring point, and the sub-monitoring point are combined to form the stripping monitoring point.

[0030] First, obtain the total length and cross-sectional diameter of the tendon to be dissected in the area to be dissected. Measuring the length and diameter of the tendon is necessary for the subsequent reasonable distribution of monitoring points, ensuring that the entire monitoring point system conforms to the actual anatomical shape of the tendon.

[0031] The tendon attachment point in the area to be dissected is marked as the first monitoring point. The tendon attachment point is a key location for the connection between the tendon and the bone structure and is the core reference for the initial stage of the dissection operation. The free end of the tendon is marked as the second monitoring point. The free end serves as a key transition location for the dissection operation. Marking it clarifies the beginning and end boundaries of the dissection path and provides a spatial range reference for the distribution of subsequent sub-monitoring points.

[0032] Based on the total length and cross-sectional diameter of the tendon, and considering the spatial positions of the first and second monitoring points, sub-monitoring points are added between them. If the total tendon length is 50mm, a sub-monitoring point is marked every 5-10mm. Alternatively, considering differences in tissue density, sub-monitoring points can be densified in areas with large density variations, ensuring that the sub-monitoring points cover all key anatomical areas along the tendon's course, filling the monitoring gap between the first and second monitoring points, and making the entire monitoring system more detailed and comprehensive.

[0033] The first monitoring point, the second monitoring point, and the sub-monitoring points are combined to form a complete stripping monitoring point system. When calculating parameters such as the spatial distance and tissue density difference between adjacent monitoring points, these monitoring points are used as the benchmark to provide localization and anatomical feature references for subsequent derivation of the stripping path and assessment of damage risk.

[0034] Based on the historical tissue stress distribution characteristics, the path correction correlation factor between the dissection path deviation value and the spatial parameter to be measured in historical surgical data is statistically determined, specifically including the following steps: Based on the historical stress distribution characteristics, the historical peeling path deviation value of the peeling monitoring point is extracted from the historical surgical data. The deviation value is obtained by subtracting the path deviations of adjacent stripping monitoring points in the historical stripping path deviation values ​​based on the spatial parameters to be measured. The ratio of the deviation value to the measured spatial parameter is used to obtain the measured deviation ratio. The path correction correlation factor is obtained by averaging all the deviation ratios to be measured.

[0035] This involves mining path deviation information related to the current monitoring point from historical surgical data. Cases matching the anatomical structure and tissue characteristics of the tendon to be dissected are selected based on historical stress distribution characteristics. The locations of the dissection monitoring points in these cases are then identified, and the historical dissection path deviation values ​​for these monitoring points are extracted—that is, the spatial deviation between the actual dissection path and the standard path at the monitoring point location in historical surgeries.

[0036] After obtaining the historical stripping path deviation value, the path deviation of adjacent stripping monitoring points is subtracted based on the spatial parameters to be measured. This is to eliminate the interference caused by the overall path offset. For example, in a certain historical surgery, the overall path may be biased to the left, but the local deviations between adjacent monitoring points may have different patterns. Subtracting the deviations can highlight the true differences in path deviations in local areas, thus obtaining a deviation value that better reflects the differences in anatomical features between adjacent monitoring points.

[0037] The deviation value is obtained by comparing it with the corresponding spatial parameter to be measured. This quantifies the degree of path deviation corresponding to a unit spatial parameter, and links the differences in anatomical features and path deviations in the form of a numerical ratio, which facilitates subsequent pattern summarization.

[0038] By averaging all the measured deviation ratios, the average influence pattern of path deviation under specific anatomical structures and tissue characteristics is summarized from a large amount of historical data. This average value is the path correction correlation factor. It reflects the average path deviation in historical surgeries when the spatial distance and tissue density differences between monitoring points are at a certain level. The path correction correlation factor is used to correct the initially planned path when planning the current surgical path.

[0039] Based on the anatomical structure data and tissue characteristic parameters of the tendon to be dissected, preprocessing correction factors are extracted from the path correction correlation factors, specifically including the following steps: Extract the current tissue stress distribution characteristics based on the anatomical structure data and tissue feature parameters of the tendon to be stripped; The current stress distribution characteristics of the organization are matched with the historical stress distribution characteristics to extract the preprocessing correction factor from the path correction correlation factor.

[0040] For example, tension sensors are used to collect stress data from different parts of the tendon, and combined with ultrasound images to analyze tissue deformation, thereby identifying the stress distribution characteristics of the tendon to be dissected. These current stress distribution characteristics are then compared and matched with various historical stress distribution characteristics accumulated in historical surgical data. Because different stress distribution characteristics in historical data correspond to different path correction correlation factors, the part that best matches the current tendon stress characteristics is selected from numerous path correction correlation factors and extracted as a preprocessing correction factor. When planning the dissection path for the current surgery, this preprocessing correction factor is used to specifically adjust the path, making it more suitable for the current tendon's biomechanical environment and reducing the risk of tissue damage due to stress mismatch.

[0041] Based on the preprocessing correction factor, the dissection path trajectory at key locations of the tendon to be dissected is determined, resulting in a first path trajectory and a second path trajectory. This process includes the following steps: Obtain the key functional areas of the tendon to be stripped; The location of the key functional areas within the area to be stripped is processed to obtain the target location points; The spatial distance and tissue density difference between the target location point and the first monitoring point are statistically analyzed to obtain the first preprocessing parameters; The spatial distance and tissue density difference between the target location point and the second monitoring point are statistically analyzed to obtain the second preprocessing parameter; Obtain the tissue hardness data of the main peeling area of ​​the current area to be peeled; The initial peeling force value to be measured is obtained by detecting the initial peeling force of the first monitoring point based on the tissue hardness data of the main peeling area, and the initial peeling force of the second monitoring point is the same as that of the first monitoring point. The first path trajectory to which the target location point belongs is determined based on the first preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor. The second path trajectory to which the target location point belongs is determined based on the second preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor.

[0042] First, clearly define the key functional areas and target locations. Identify the key functional areas of the tendon to be dissected. These areas are the core parts where the tendon performs its physiological functions, such as areas where tendon fiber bundles are densely packed and play a crucial role in mechanical transmission. The accuracy of the dissection path directly affects the surgical outcome. After determining the key functional areas, locate their specific positions within the area to be dissected, thus obtaining the target location. This step locks in the core reference points for subsequent path derivation, allowing path planning to focus on key anatomical sites.

[0043] The spatial distance and tissue density difference between the target location point and the first monitoring point are statistically analyzed to obtain the first preprocessing parameter; the spatial distance and tissue density difference between the target location point and the second monitoring point are statistically analyzed to obtain the second preprocessing parameter. Spatial distance reflects the spatial relationship between the target location and the endpoint of the dissection path, while tissue density difference reflects the changes in tissue characteristics in different regions. The combination of these two parameters forms the first and second preprocessing parameters, comprehensively characterizing the anatomical differences between the target location and key monitoring points. Tissue hardness data of the main dissection area of ​​the current area to be dissected is obtained. Tissue hardness is closely related to the initial dissection force required for dissection. Based on this hardness data, the initial dissection force at the first monitoring point is detected to obtain the initial dissection force value to be measured, ensuring that the initial dissection force at the second monitoring point is consistent with the initial dissection force at the first monitoring point.

[0044] The first path trajectory to which the target location belongs is determined based on the first preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor. The first preprocessing parameter includes the spatial distance and tissue density difference value between the target location and the first monitoring point, reflecting the differences in anatomical characteristics between these two key locations. The spatial distance determines the spatial extension scale of the path, and the tissue density difference value reflects the differences in the response of different tissue regions to the peeling operation. The initial peeling force value to be measured represents the magnitude of the force required at the beginning of the peeling stage under the tissue hardness of the main peeling area. This is the basic mechanical reference in path planning, because the magnitude of the peeling force directly affects the way the path acts on the tissue during advancement. The preprocessing correction factor is a correction basis extracted from historical experience and adapted to the current tissue stress distribution characteristics. It can adjust the path initially planned based on anatomical parameters and initial force values. A pre-constructed mechanics-anatomy coupling model is used. The first preprocessing parameters are input into this model, and the initial path is simulated based on the initial peeling force value to be measured. The initial path is then corrected using a preprocessing correction factor. This factor adjusts the direction and curvature parameters of the path based on historical deviations under similar anatomical and mechanical characteristics, ensuring the path conforms to the current anatomical structure and initial force requirements while also mitigating potential risks by drawing on historical experience. Through this comprehensive calculation and correction of the mechanics-anatomy coupling model, a first path trajectory that closely matches the correlation characteristics between the target location and the first monitoring point is output, providing clear path guidance for the peeling operation.

[0045] The second preprocessing parameter, the initial dissection force value to be measured, and the preprocessing correction factor determine the second path trajectory to which the target location point belongs. By locating key areas, collecting multi-dimensional parameters, and integrating historical correction factors with mechanical data to derive the path, the dissection path of the key location of the tendon to be dissected is gradually clarified. This provides specific trajectory references for subsequent risk assessment and path optimization, ensuring that the dissection path planning not only conforms to current anatomical features but also incorporates historical experience and mechanical principles, thereby improving the scientific rigor and safety of the surgical approach.

[0046] The comprehensive path deviation factor is calculated based on the first path trajectory and the second path trajectory, specifically including the following steps: The pretreatment strain frequency was obtained by detecting the strain frequency of the tissue at the first monitoring point; The first damage risk factor is obtained by multiplying the path deviation value of the first path trajectory with the pre-treatment strain frequency. The second damage risk factor is obtained by multiplying the path deviation value of the second path trajectory with the pre-treatment strain frequency. The comprehensive path deviation factor is obtained by summing the first damage risk factor and the second damage risk factor.

[0047] This application detects the strain frequency of the tissue at the first monitoring point. The strain frequency reflects the number of strain fluctuations of the tissue at that monitoring point per unit time during the stripping preparation stage. It is an important indicator of tissue stability. The higher the frequency, the more sensitive the tissue is to external operations, and the greater the risk of damage caused by subsequent path deviations.

[0048] The first damage risk factor is obtained by multiplying the path deviation value of the first path trajectory by the pretreatment strain frequency. Path deviation causes mechanical disturbance to the tissue, while strain frequency reflects the tissue's sensitivity to disturbance. Multiplying the two quantifies the damage risk. The larger the deviation and the higher the strain frequency, the larger the value of the first damage risk factor, indicating that the path causes greater damage to the tissue. The second damage risk factor is obtained by multiplying the path deviation value of the second path trajectory by the pretreatment strain frequency.

[0049] The first and second damage risk factors are summed to integrate the damage risks of the two pathways. The comprehensive pathway deviation factor can fully reflect the overall damage risk level caused by pathway deviation in the two stripping pathways under tissue strain-sensitive environments, providing a quantitative basis for subsequent judgment of tissue damage risk values ​​and optimization of stripping pathways based on this factor.

[0050] The risk of tissue damage to the tendon to be dissected during the dissection process is determined based on the comprehensive path deviation factor, and the current damage risk value is obtained. This includes the following steps: Obtain a damage database under different path deviation values ​​and different tissue strain frequencies; The current injury risk value of the tendon to be stripped is obtained by matching the comprehensive path deviation factor with the injury database.

[0051] The injury database was built using a large number of anterior cruciate ligament (ACL) tendon detachment surgical cases and biomechanical experiments. The database stores data on tissue damage under different combinations of path deviation values ​​and tissue strain frequencies, such as the probability of injury and the severity level of the injury.

[0052] The comprehensive path deviation factor is compared with data in the damage database. The comprehensive path deviation factor contains the superimposed damage risk information of the two surgical paths, integrating the correlation between path deviation and tissue strain frequency. During the matching process, based on the path deviation value and key parameters of tissue strain frequency in the comprehensive path deviation factor, the best-fitting record is searched in the damage database. By combining historical experience with the actual situation of the current surgery through matching, the current damage risk value of the tendon to be dissected under the current path planning is derived. This allows the surgeon to clearly understand the degree of tissue damage caused by the dissection path, providing crucial quantitative risk basis for whether and how to adjust the path.

[0053] The optimal adjustment path is determined based on the current damage risk value, specifically including the following steps: If the current damage risk value is greater than or equal to the preset tissue damage warning threshold, a path adjustment notification will be output. Based on the path adjustment notification information, the optimized removal path is obtained by determining the optimized adjustment path of the tendon to be removed. The high-stress areas of the auxiliary paths are obtained by collecting data from the high-stress areas of each optimized stripping path. Stress characteristic data of the auxiliary path are obtained by detecting the stress intensity and area of ​​the high-stress region in the auxiliary path. The stress intensity and area of ​​the high-stress region in the main peeling area are detected to obtain the stress characteristic data of the main path. The optimal adjustment path is extracted from the optimized stripping path, where the stress characteristic data of the auxiliary path is less than that of the main path.

[0054] First, a damage risk threshold is set as the trigger condition for path adjustment. When the calculated current damage risk value is greater than or equal to the preset tissue damage warning threshold, it means that there is tissue damage in the currently planned stripping path. Therefore, a path adjustment notification is output, and the path optimization process is initiated.

[0055] Based on the path adjustment notification, and combined with multi-dimensional data on the anatomical structure of the tendon to be dissected, tissue stress distribution, and spatial parameters to be measured, multiple optimized dissection paths are derived. These paths will proactively avoid dangerous areas such as neurovascular bundles and high-stress zones along the original path.

[0056] The high-stress areas of each optimized stripping path are located and collected to obtain the high-stress areas of the auxiliary path. The stress intensity and area of ​​the region are detected and integrated into the stress characteristic data of the auxiliary path to quantify the mechanical risk of the optimized path. The stress intensity and area of ​​the high-stress area of ​​the original main stripping area are detected to obtain the stress characteristic data of the main path, so that the mechanical risks of the optimized path and the original path have comparable dimensions.

[0057] By comparing the stress characteristic data of the auxiliary path for each optimized peeling path with the stress characteristic data of the main path, the optimized paths with lower stress intensity and smaller high-stress areas are extracted. Because these paths can meet the peeling requirements and more effectively reduce the mechanical load on the tissue, thereby reducing the risk of damage, they are ultimately determined as the preferred adjustment paths.

[0058] A dynamic path planning system for anterior cruciate ligament tendon dissection includes: Acquisition module: Acquires anatomical structure data of the anterior cruciate ligament tendon and tissue characteristic parameters of the area to be dissected, and marks key monitoring points in the area to be dissected to obtain dissection monitoring points; Statistical module: Calculates the spatial parameters to be measured by analyzing the differences in spatial distance and tissue density between adjacent stripping monitoring points; Extraction module: Extracts historical tissue stress distribution characteristics of different anatomical structure types from historical anterior cruciate ligament tendon stripping surgery data, and calculates the path correction correlation factor between the stripping path deviation value and the spatial parameter to be measured in the historical surgical data based on the historical tissue stress distribution characteristics. Path determination module: Based on the anatomical structure data and tissue feature parameters of the tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor. Based on the preprocessing correction factor, the stripping path trajectory at the key position of the tendon to be stripped is determined, and the first path trajectory and the second path trajectory are obtained. Statistical judgment module: Based on the first path trajectory and the second path trajectory, a comprehensive path deviation factor is calculated. Based on the comprehensive path deviation factor, the risk value of tissue damage caused by path deviation during the dissection of the tendon to be dissected is judged to obtain the current damage risk value. Path determination module: Optimizes and adjusts the path based on the current damage risk value.

[0059] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a dynamic path planning method for anterior cruciate ligament tendon stripping.

[0060] like Figure 3 As shown, the electronic device may include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a dynamic path planning method for anterior cruciate ligament tendon dissection.

[0061] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0062] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program that can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer is able to execute a dynamic path planning method for anterior cruciate ligament tendon stripping.

[0063] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform a dynamic path planning method for anterior cruciate ligament tendon stripping.

[0064] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic path planning method for anterior cruciate ligament tendon stripping, characterized in that, The method includes the following steps: Obtain anatomical data of the anterior cruciate ligament tendon and tissue characteristic parameters of the area to be dissected, and mark key monitoring points in the area to be dissected to obtain dissection monitoring points; The spatial parameters to be measured are obtained by statistically analyzing the spatial distance and tissue density differences between adjacent stripping monitoring points; From historical anterior cruciate ligament tendon stripping surgery data, historical tissue stress distribution characteristics of different anatomical structure types were extracted, and the path correction correlation factor between the stripping path deviation value and the spatial parameter to be measured in the historical surgical data was statistically determined based on the historical tissue stress distribution characteristics. Based on the anatomical structure data and tissue characteristic parameters of the tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor. The stripping path trajectory at the key location of the tendon to be stripped is determined based on the preprocessing correction factor, and the first path trajectory and the second path trajectory are obtained. Based on the first and second path trajectories, a comprehensive path deviation factor is calculated. Based on the comprehensive path deviation factor, the risk value of tissue damage caused by path deviation during the dissection of the tendon to be dissected is judged, and the current damage risk value is obtained. The optimal adjustment path can be obtained based on the current damage risk value.

2. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 1, characterized in that, The process of marking key monitoring points in the area to be stripped to obtain stripping monitoring points includes the following steps: Obtain the total length and cross-sectional diameter of the tendon to be stripped in the area to be stripped; The first monitoring point is obtained by marking the key monitoring points at the tendon attachment points in the area to be stripped. The key monitoring points near the free end of the tendon in the area to be dissected were marked to obtain the second monitoring point; Sub-monitoring points are obtained by marking sub-monitoring points in the area to be dissected based on the total length of the tendon, the cross-sectional diameter, the first monitoring point, and the second monitoring point. The first monitoring point, the second monitoring point, and the sub-monitoring point are combined to form the stripping monitoring point.

3. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 2, characterized in that, Based on the historical tissue stress distribution characteristics, the path correction correlation factor between the dissection path deviation value and the spatial parameter to be measured in historical surgical data is statistically determined, specifically including the following steps: Based on the historical stress distribution characteristics, the historical peeling path deviation value of the peeling monitoring point is extracted from the historical surgical data. The deviation value is obtained by subtracting the path deviations of adjacent stripping monitoring points in the historical stripping path deviation values ​​based on the spatial parameters to be measured. The ratio of the deviation value to the measured spatial parameter is used to obtain the measured deviation ratio. The path correction correlation factor is obtained by averaging all the deviation ratios to be measured.

4. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 3, characterized in that, Based on the anatomical structure data and tissue characteristic parameters of the tendon to be dissected, preprocessing correction factors are extracted from the path correction correlation factors, specifically including the following steps: Extract the current tissue stress distribution characteristics based on the anatomical structure data and tissue feature parameters of the tendon to be stripped; The current stress distribution characteristics of the tissue are matched with the historical stress distribution characteristics to extract the preprocessing correction factor from the path correction correlation factor.

5. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 4, characterized in that, Based on the preprocessing correction factor, the dissection path trajectory at key locations of the tendon to be dissected is determined, resulting in a first path trajectory and a second path trajectory. This process includes the following steps: Obtain the key functional areas of the tendon to be stripped; The location of the key functional areas within the area to be stripped is processed to obtain the target location points; The spatial distance and tissue density difference between the target location point and the first monitoring point are statistically analyzed to obtain the first preprocessing parameters; The spatial distance and tissue density difference between the target location point and the second monitoring point are statistically analyzed to obtain the second preprocessing parameter; Obtain the tissue hardness data of the main peeling area of ​​the current area to be peeled; The initial peeling force value to be measured is obtained by detecting the initial peeling force of the first monitoring point based on the tissue hardness data of the main peeling area, and the initial peeling force of the second monitoring point is the same as that of the first monitoring point. The first path trajectory to which the target location point belongs is determined based on the first preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor. The second path trajectory to which the target location point belongs is determined based on the second preprocessing parameter, the initial peeling force value to be measured, and the preprocessing correction factor.

6. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 5, characterized in that, The comprehensive path deviation factor is calculated based on the first path trajectory and the second path trajectory, specifically including the following steps: The pretreatment strain frequency was obtained by detecting the strain frequency of the tissue at the first monitoring point; The first damage risk factor is obtained by multiplying the path deviation value of the first path trajectory with the pre-treatment strain frequency. The second damage risk factor is obtained by multiplying the path deviation value of the second path trajectory with the pre-treatment strain frequency. The comprehensive path deviation factor is obtained by summing the first damage risk factor and the second damage risk factor.

7. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 6, characterized in that, The risk of tissue damage to the tendon to be dissected during the dissection process is determined based on the comprehensive path deviation factor, and the current damage risk value is obtained. This includes the following steps: Obtain a damage database under different path deviation values ​​and different tissue strain frequencies; The current injury risk value of the tendon to be stripped is obtained by matching the comprehensive path deviation factor with the injury database.

8. The dynamic path planning method for anterior cruciate ligament tendon stripping according to claim 7, characterized in that, The optimal adjustment path is determined based on the current damage risk value, specifically including the following steps: If the current damage risk value is greater than or equal to the preset tissue damage warning threshold, a path adjustment notification will be output. Based on the path adjustment notification information, the optimized removal path is obtained by determining the optimized adjustment path of the tendon to be removed. The high-stress areas of the auxiliary paths are obtained by collecting data from the high-stress areas of each optimized stripping path. Stress characteristic data of the auxiliary path are obtained by detecting the stress intensity and area of ​​the high-stress region in the auxiliary path. The stress intensity and area of ​​the high-stress region in the main peeling area are detected to obtain the stress characteristic data of the main path. The optimal adjustment path is extracted from the optimized stripping path, where the stress characteristic data of the auxiliary path is less than that of the main path.

9. A dynamic path planning system for anterior cruciate ligament tendon stripping, applied to the dynamic path planning method for anterior cruciate ligament tendon stripping as described in any one of claims 1 to 8, characterized in that, include: Acquisition module: Acquires anatomical structure data of the anterior cruciate ligament tendon and tissue characteristic parameters of the area to be dissected, and marks key monitoring points in the area to be dissected to obtain dissection monitoring points; Statistical module: Calculates the spatial parameters to be measured by analyzing the differences in spatial distance and tissue density between adjacent stripping monitoring points; Extraction module: Extracts historical tissue stress distribution characteristics of different anatomical structure types from historical anterior cruciate ligament tendon stripping surgery data, and calculates the path correction correlation factor between the stripping path deviation value and the spatial parameter to be measured in the historical surgical data based on the historical tissue stress distribution characteristics. Path determination module: Based on the anatomical structure data and tissue feature parameters of the tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor. Based on the preprocessing correction factor, the stripping path trajectory at the key position of the tendon to be stripped is determined, and the first path trajectory and the second path trajectory are obtained. Statistical judgment module: Based on the first path trajectory and the second path trajectory, a comprehensive path deviation factor is calculated. Based on the comprehensive path deviation factor, the risk value of tissue damage caused by path deviation during the dissection of the tendon to be dissected is judged to obtain the current damage risk value. Path determination module: Optimizes and adjusts the path based on the current damage risk value.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a dynamic path planning method for anterior cruciate ligament tendon stripping as described in any one of claims 1 to 8.

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