A dynamic path planning method and system for anterior cruciate ligament tendon stripping

Abstract

The application discloses a kind of anterior cruciate ligament tendon stripping dynamic path planning method and system, it is related to ligament tendon stripping technical field, and its technical solution points include the following steps: obtaining the anatomical structure data of anterior cruciate ligament tendon and the tissue characteristic parameter of the region to be stripped, key monitoring point marking is carried out to the region to be stripped to obtain stripping monitoring point;The spatial distance between adjacent stripping monitoring points and the tissue density difference value are counted to obtain the space parameter to be measured;The historical tissue stress distribution characteristics of different anatomical structure types are extracted from historical anterior cruciate ligament tendon stripping surgery data, and the path correction correlation factor between the stripping path deviation value and the space parameter to be measured in historical surgery data is counted according to historical tissue stress distribution characteristics;According to the anatomical structure data and tissue characteristic parameter of the tendon to be stripped currently, the effect is to reduce the operation failure caused by path deviation, improve the accuracy of operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ligament tendon stripping, more particularly, it relates to a dynamic path planning method and system for anterior cruciate ligament tendon stripping. BACKGROUND

[0002] In the field of orthopedic surgery, anterior cruciate ligament tendon stripping operation has a significant impact on surgical results and patient recovery. Traditional methods rely heavily on the experience of the operator, so there are obvious limitations. The anatomical structure of the human anterior cruciate ligament tendon varies greatly from individual to individual, and the length, diameter and surrounding tissue of different patients are complex and diverse. It is difficult to accurately adapt to experience-based operations, and rough path planning can lead to incomplete stripping or excessive stripping, thereby affecting the subsequent ligament reconstruction effect. At the same time, there is a lack of assessment of tissue stress and damage risk, and path deviation during surgery can easily cause damage to tendons and surrounding nerves and blood vessels, thereby increasing the probability of postoperative complications and prolonging the patient's recovery period. SUMMARY

[0003] In view of the deficiencies of the prior art, the purpose of the present application is to provide a dynamic path planning method and system for anterior cruciate ligament tendon stripping.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0005] A dynamic path planning method for anterior cruciate ligament tendon stripping, the method comprising the following steps:

[0006] Obtain the anatomical structure data of the anterior cruciate ligament tendon and the tissue characteristic parameters of the region to be stripped, and mark the key monitoring points of the region to be stripped to obtain the stripping monitoring points;

[0007] Statistically obtain the spatial parameters of the adjacent stripping monitoring points and the tissue density difference values;

[0008] Extract the historical tissue stress distribution characteristics of different anatomical structure types from the historical anterior cruciate ligament tendon stripping surgery data, and statistically obtain the path correction correlation factor between the stripping path deviation value and the measured spatial parameters in the historical surgery data according to the historical tissue stress distribution characteristics;

[0009] According to the anatomical structure data and the tissue characteristic parameters of the current tendon to be stripped, a pre-processing correction factor is extracted from the path correction correlation factor, and the stripping path trajectory at the key position of the tendon to be stripped is judged according to the pre-processing correction factor, to obtain a first path trajectory and a second path trajectory;

[0010] Statistically obtain a comprehensive path deviation factor according to the first path trajectory and the second path trajectory, and judge the tissue damage risk value generated by the tendon to be stripped in the stripping process due to path deviation according to the comprehensive path deviation factor, to obtain a current damage risk value.

[0011] According to the current injury risk value, an optimal adjustment path is obtained.

[0012] Preferably, the key monitoring points in the region to be stripped are marked to obtain stripping monitoring points, specifically including the following steps:

[0013] The total length and cross-sectional diameter of the tendon to be stripped in the region to be stripped are obtained;

[0014] The key monitoring points of the tendon attachment point in the region to be stripped are marked to obtain the first monitoring point;

[0015] The key monitoring points near the free end of the tendon in the region to be stripped are marked to obtain the second monitoring point;

[0016] According to the total length, cross-sectional diameter, first monitoring point and second monitoring point of the tendon, the sub-monitoring points in the region to be stripped are marked to obtain the sub-monitoring points;

[0017] The first monitoring point, the second monitoring point and the sub-monitoring point are combined into the stripping monitoring point.

[0018] Preferably, according to the historical tissue stress distribution characteristics, the path correction correlation factor between the stripping path deviation value in the historical operation data and the to-be-measured space parameter is counted, specifically including the following steps:

[0019] According to the historical stress distribution characteristics, the historical stripping path deviation value to which the stripping monitoring point belongs is extracted from the historical operation data;

[0020] According to the to-be-measured space parameter, the path deviation of adjacent stripping monitoring points in the historical stripping path deviation value is processed to obtain the deviation value;

[0021] The deviation value and the to-be-measured space parameter are processed to obtain the to-be-measured deviation ratio;

[0022] All to-be-measured deviation ratios are processed to obtain the path correction correlation factor.

[0023] Preferably, according to the anatomical structure data and tissue characteristic parameters of the current tendon to be stripped, a pretreatment correction factor is extracted from the path correction correlation factor, specifically including the following steps:

[0024] According to the anatomical structure data and tissue characteristic parameters of the current tendon to be stripped, the current tissue stress distribution characteristics are extracted;

[0025] The current tissue stress distribution characteristics are matched with the historical stress distribution characteristics to extract the pretreatment correction factor from the path correction correlation factor.

[0026] Preferably, the dissection path trajectory at the key position of the tendon to be peeled is determined according to the pre-processing correction factor, to obtain a first path trajectory and a second path trajectory, specifically including the following steps:

[0027] Obtain the key functional area of the tendon to be peeled;

[0028] Position the position of the key functional area in the peeling area to obtain a target position point;

[0029] Calculate the spatial distance and tissue density difference value between the target position point and the first monitoring point to obtain a first pre-processing parameter;

[0030] Calculate the spatial distance and tissue density difference value between the target position point and the second monitoring point to obtain a second pre-processing parameter;

[0031] Obtain the main peeling area tissue hardness data of the current peeling area;

[0032] Detect the initial peeling force to which the first monitoring point belongs according to the main peeling area tissue hardness data to obtain a measured initial peeling force value, and the initial peeling force to which the second monitoring point belongs is the same as the initial peeling force to which the first monitoring point belongs;

[0033] Determine the first path trajectory to which the target position point belongs according to the first pre-processing parameter, the measured initial peeling force value and the pre-processing correction factor;

[0034] Determine the second path trajectory to which the target position point belongs according to the second pre-processing parameter, the measured initial peeling force value and the pre-processing correction factor.

[0035] Preferably, the comprehensive path deviation factor is calculated according to the first path trajectory and the second path trajectory, specifically including the following steps:

[0036] Detect the tissue strain frequency of the first monitoring point to obtain a pre-processing strain frequency;

[0037] Multiply the path deviation value of the first path trajectory by the pre-processing strain frequency to obtain a first damage risk factor;

[0038] Multiply the path deviation value of the second path trajectory by the pre-processing strain frequency to obtain a second damage risk factor;

[0039] Sum the first damage risk factor and the second damage risk factor to obtain a comprehensive path deviation factor.

[0040] Preferably, the tissue damage risk value of the tendon to be peeled in the peeling process affected by the path deviation is determined according to the comprehensive path deviation factor to obtain a current damage risk value, specifically including the following steps:

[0041] obtaining a damage database under different path deviation values and different tissue strain frequencies;

[0042] matching the comprehensive path deviation factor with the damage database to obtain a current damage risk value of the tendon to be stripped.

[0043] Preferably, the preferred adjustment path is obtained according to the current damage risk value, specifically including the following steps:

[0044] if the current damage risk value is greater than or equal to a preset tissue damage warning threshold, outputting path adjustment notification information;

[0045] obtaining an optimized stripping path according to the path adjustment notification information and the current tendon to be stripped;

[0046] collecting high stress areas of each optimized stripping path to obtain auxiliary path high stress areas;

[0047] detecting stress intensity and area of the auxiliary path high stress areas to obtain auxiliary path stress characteristic data;

[0048] detecting stress intensity and high stress area of the main stripping region to obtain main path stress characteristic data;

[0049] extracting the preferred adjustment path from the optimized stripping path, wherein the auxiliary path stress characteristic data is less than the main path stress characteristic data corresponding to the preferred adjustment path.

[0050] A dynamic path planning system for anterior cruciate ligament tendon stripping, comprising:

[0051] an acquisition module: acquiring anatomical structure data of the anterior cruciate ligament tendon and tissue characteristic parameters of the region to be stripped, and marking key monitoring points of the region to be stripped to obtain stripping monitoring points;

[0052] a statistical module: statistically analyzing spatial distance and tissue density difference values between adjacent stripping monitoring points to obtain a to-be-measured spatial parameter;

[0053] an extraction module: extracting historical tissue stress distribution characteristics of different anatomical structure types from historical anterior cruciate ligament tendon stripping surgery data, and statistically analyzing path correction correlation factors between the stripping path deviation values and the to-be-measured spatial parameter in the historical surgery data according to the historical tissue stress distribution characteristics;

[0054] a path judgment module: extracting a preprocessing correction factor from the path correction correlation factors according to the anatomical structure data and the tissue characteristic parameters of the tendon to be stripped, judging the stripping path trajectory at the key position of the tendon to be stripped according to the preprocessing correction factor, and obtaining a first path trajectory and a second path trajectory;

[0055] The statistical judgment module: according to the first path trajectory and the second path trajectory, a comprehensive path deviation factor is counted, according to the comprehensive path deviation factor, a tissue damage risk value generated by the path deviation in the stripping process of the to-be-stripped tendon is judged, and a current damage risk value is obtained;

[0056] The path determination module: according to the current damage risk value, an optimal adjustment path is obtained.

[0057] An electronic device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to realize the dynamic path planning method for anterior cruciate ligament tendon stripping.

[0058] Compared with the prior art, the present application has the following advantages:

[0059] The present application can master the space and tissue differences between adjacent monitoring points, and the basis for path planning is more in line with the actual anatomical features. With the help of historical data extraction path correction correlation factor, combined with the current operation data extraction pretreatment correction factor, the path trajectory judgment is more accurate. The stripping path can be maximally matched with the anatomical structure of the tendon, reducing the surgical errors caused by path deviation, and improving the accuracy of surgical operation. By detecting the tissue strain frequency and combining the path deviation value to calculate the damage risk factor, and matching with the damage database to obtain the current damage risk value, the risk of tissue damage during operation can be predicted in advance. According to the risk value, the path is optimized and adjusted to actively avoid high stress areas, nerve and blood vessel bundles and other dangerous areas, effectively reducing the damage probability of the tendon and surrounding tissues during operation. BRIEF DESCRIPTION OF DRAWINGS

[0060] Fig. 1 A dynamic path planning method for anterior cruciate ligament tendon stripping is provided.

[0061] Fig. 2 A dynamic path planning system for anterior cruciate ligament tendon stripping is provided.

[0062] Fig. 3 The structure diagram of the electronic device provided by the embodiment of the present application.

[0063] 610, processor; 620, communication interface; 630, memory; 640, communication bus. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0065] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0066] It is also noted that, as used herein, "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the application. The appearances of the phrase "in one embodiment" or "in an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.

[0067] Referring to Figs. 1-3 as shown.

[0068] Embodiments further illustrate a method and system for dynamic path planning of anterior cruciate ligament tendon stripping.

[0069] A method for dynamic path planning of anterior cruciate ligament tendon stripping, the method comprising the following steps:

[0070] Obtaining anatomical structure data of the anterior cruciate ligament tendon and tissue characteristic parameters of the region to be stripped, and marking key monitoring points of the region to be stripped to obtain stripping monitoring points;

[0071] Statistically obtaining the spatial parameters to be measured from the spatial distance and tissue density difference value between adjacent stripping monitoring points;

[0072] Extracting historical tissue stress distribution characteristics of different anatomical structure types from historical anterior cruciate ligament tendon stripping surgery data, and statistically obtaining the path correction correlation factor between the stripping path deviation value and the spatial parameters to be measured in the historical surgery data according to the historical tissue stress distribution characteristics;

[0073] According to the anatomical structure data and tissue characteristic parameters of the tendon to be stripped, a pre-processing correction factor is extracted from the path correction correlation factor, and the stripping path trajectory at the key position of the tendon to be stripped is judged according to the pre-processing correction factor, to obtain a first path trajectory and a second path trajectory;

[0074] According to the first path trajectory and the second path trajectory, a comprehensive path deviation factor is statistically obtained, and according to the comprehensive path deviation factor, a tissue damage risk value generated by the tendon to be stripped in the stripping process due to path deviation is judged, to obtain a current damage risk value;

[0075] According to the current damage risk value, an optimal adjustment path is obtained.

[0076] The application firstly carries out the marking of the stripping monitoring points, obtains the total length and cross-sectional diameter of the tendon in the region to be stripped through the high-frequency ultrasonic imaging equipment, and clearly defines the basic size parameters of the tendon; marks the tendon attachment point as the first monitoring point and marks the free end of the tendon as the second monitoring point, and the two endpoints correspond to the starting and ending key positions of the stripping operation respectively, and according to the total length, cross-sectional diameter, first monitoring point and second monitoring point of the tendon, the sub-monitoring points are marked in the region to be stripped.

[0077] The three-dimensional coordinates of the adjacent stripping monitoring points are obtained through the electromagnetic positioning system, and then the spatial distance between the two points is calculated, and the tissue density data of the positions of the adjacent stripping monitoring points are extracted by using the gray value of the high-frequency ultrasonic image, and the density difference value is calculated, and the two parameters are associated and integrated to obtain the to-be-measured spatial parameter, which reflects the difference between the adjacent regions in the spatial position and the tissue composition.

[0078] Firstly, a large number of historical surgery databases of clinical cases are classified according to the types of tendon anatomical structures, and the corresponding historical tissue stress distribution characteristics of different types are extracted, including stress peak position and key information of stress conduction path; according to these stress distribution characteristics, the positions corresponding to the current monitoring points in each historical surgery are located, and the historical stripping path deviation values of these positions, i.e. the spatial deviation of the actual stripping path and the ideal path, are extracted; for each group of adjacent monitoring points, the historical path deviation values are processed by difference in combination with the corresponding to-be-measured spatial parameters to obtain the deviation of the local region; then the deviation value and the corresponding to-be-measured spatial parameter are calculated by ratio to obtain the to-be-measured deviation ratio, so as to quantify the path deviation degree corresponding to the unit spatial parameter; the historical case deviation ratio of the same anatomical type and similar to-be-measured spatial parameter is averaged to obtain the path correction correlation factor, which clearly reflects the influence law of the spatial parameter on the path deviation under the specific anatomy and tissue characteristics.

[0079] The stress data is collected by an intraoperative tension sensor, and the current tissue stress distribution characteristics of the tendon to be peeled are obtained by analyzing the tissue deformation of the ultrasound image. The current stress distribution characteristics are matched with the stress characteristics of different anatomical types in the historical database, and the path correction correlation factors corresponding to the high-matching historical cases are selected as the preprocessing correction factors to ensure that the factors are highly adapted to the anatomical characteristics of the current operation. Then, the key functional areas of the tendon, such as the fiber bundle dense area and the area adjacent to the nerve and blood vessel bundle, are extracted from the ultrasound image by using an image recognition algorithm. These areas are prone to serious injury due to path deviation, and therefore need to be focused on. The target position points are located by three-dimensional coordinate positioning of the key functional areas, and the spatial distances between the target position points and the first and second monitoring points and the tissue density difference values are calculated to obtain the first and second preprocessing parameters. At the same time, the tissue hardness of the main peeling area is detected by the tension sensor to back-calculate the initial peeling force value of the first monitoring point, thereby ensuring the stability of the initial peeling force. Finally, the optimal peeling path of the target position point is obtained by combining the first preprocessing parameter, the initial peeling force value and the preprocessing correction factor with the aid of a mechanical simulation model, so as to obtain the first path trajectory. The second path trajectory is obtained in the same way by combining the second preprocessing parameter.

[0080] The strain frequency of the tissue at the first monitoring point in the peeling preparation stage is detected by using a strain sensor, and the frequency reflects the stability of the tissue. The higher the frequency, the stronger the sensitivity of the tissue 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 injury risk factors, which quantize the risk degree 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. The pre-established injury database is called, which contains the correlation data of different path deviation values, different strain frequencies and corresponding tissue injury risk values. The current comprehensive path deviation factor is matched with the database data to obtain the current injury risk value, which intuitively reflects the safety degree of the path implementation.

[0081] If the current injury risk value is greater than or equal to the preset tissue injury warning threshold, a path adjustment notification is output. A plurality of optimized peeling paths are generated in combination with the current tendon anatomical structure, stress distribution and to-be-measured spatial parameters, and all the paths avoid high-risk areas. The high-stress areas of each optimized path are located, and the stress intensity and area of the areas are collected to obtain auxiliary path stress characteristic data. The stress data of the original main peeling area is collected as main path stress characteristic data. The auxiliary path and main path stress characteristics 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, which ensures that the path meets the peeling requirements while minimizing the risk of tissue injury, thereby providing dynamic guidance for surgical operation.

[0082] The key monitoring points of the region to be stripped are marked to obtain stripping monitoring points, specifically including the following steps:

[0083] The total length and cross-sectional diameter of the tendon to be stripped in the region to be stripped are obtained;

[0084] The key monitoring points of the tendon attachment point of the region to be stripped are marked to obtain the first monitoring point;

[0085] The key monitoring points of the proximal tendon free end of the region to be stripped are marked to obtain the second monitoring point;

[0086] The first monitoring point, the second monitoring point and the sub-monitoring point are combined to form a complete stripping monitoring point system. When calculating the spatial distance, tissue density difference and other parameters of adjacent monitoring points in the subsequent, these monitoring points are taken as the reference, and the positioning and anatomical feature reference are provided for the subsequent derivation of the stripping path and the assessment of the damage risk.

[0087] The first monitoring point, the second monitoring point and the sub-monitoring point are combined to form a complete stripping monitoring point system. When calculating the spatial distance, tissue density difference and other parameters of adjacent monitoring points in the subsequent, these monitoring points are taken as the reference, and the positioning and anatomical feature reference are provided for the subsequent derivation of the stripping path and the assessment of the damage risk.

[0088] First, the total length and cross-sectional diameter of the tendon to be stripped in the region to be stripped are obtained. The length and diameter of the tendon are measured in order to reasonably distribute the monitoring points later, so that the entire monitoring point system can fit the actual anatomical morphology of the tendon.

[0089] The tendon attachment point of the region to be stripped is marked as the first monitoring point. The tendon attachment point is a key position where the tendon is connected with the skeletal structure, and is the core reference in the initial stage of the stripping operation. The free end of the tendon is marked as the second monitoring point. The free end is a key transition position for the stripping operation, and the marking clearly defines the boundaries of the stripping path, providing a spatial range reference for the distribution of subsequent sub-monitoring points.

[0090] According to the total length and cross-sectional diameter of the tendon, and in combination with the spatial positions of the first monitoring point and the second monitoring point, sub-monitoring points are supplemented between them. If the total length of the tendon is 50mm, a sub-monitoring point is marked every 5-10mm. In combination with the differences in tissue density, sub-monitoring points can be added in areas with large density changes, so that the sub-monitoring points can cover all key anatomical areas in the course of the tendon running, fill the monitoring gaps between the first monitoring point and the second monitoring point, and make the entire monitoring system more detailed and comprehensive.

[0091] The first monitoring point, the second monitoring point and the sub-monitoring point are combined to form a complete stripping monitoring point system. When calculating the spatial distance, tissue density difference and other parameters of adjacent monitoring points in the subsequent, these monitoring points are taken as the reference, and the positioning and anatomical feature reference are provided for the subsequent derivation of the stripping path and the assessment of the damage risk.

[0092] According to the historical tissue stress distribution characteristics, the path correction correlation factor between the stripping path deviation value and the to-be-measured spatial parameter in the historical surgery data is statistically obtained, specifically including the following steps:

[0093] According to the historical stress distribution characteristics, the historical peeling path deviation value to which the peeling monitoring point belongs is extracted from the historical operation data;

[0094] According to the to-be-measured spatial parameter, the path deviation of adjacent peeling monitoring points in the historical peeling path deviation value is processed by difference to obtain a deviation value;

[0095] The deviation value and the to-be-measured spatial parameter are processed by ratio to obtain a to-be-measured deviation ratio;

[0096] All to-be-measured deviation ratios are processed by average to obtain a path correction correlation factor.

[0097] Path deviation information related to the current monitoring point is mined from historical operation data. According to the historical stress distribution characteristics, cases matching the current to-be-peeled tendon anatomical structure and tissue characteristics are screened, the positions of the peeling monitoring points in these cases are located, and the historical peeling path deviation values to which these monitoring points belong are extracted, that is, the spatial deviation of the actual peeling path and the standard path at the monitoring point position in the historical operation.

[0098] After obtaining the historical peeling path deviation value, the path deviation of adjacent peeling monitoring points is processed by difference in combination with the to-be-measured spatial parameter. This is to eliminate the interference of overall path deviation, such as the overall path deviation to the left in a certain historical operation, but the local deviation between adjacent monitoring points has different rules. After difference processing, the real difference of the local area path deviation can be highlighted, so as to obtain a deviation value that is more consistent with the anatomical feature difference between adjacent monitoring points.

[0099] The deviation value and the corresponding to-be-measured spatial parameter are processed by ratio to obtain a to-be-measured deviation ratio. In order to quantify the path deviation degree corresponding to the unit spatial parameter, the anatomical feature difference and the path deviation are related in the form of numerical ratio, which is convenient for subsequent induction of rules.

[0100] All to-be-measured deviation ratios are processed by average to induce the average influence rule of path deviation under a certain anatomical structure and tissue characteristics from a large amount of historical data. This average value is the path correction correlation factor. It can reflect the average situation of path deviation in historical operations when the spatial distance between monitoring points and the tissue density difference are at a certain level. The path correction correlation factor is used to correct the initially planned path during the planning of the current operation path.

[0101] According to the anatomical structure data and tissue characteristic parameters of the current to-be-peeled tendon, a pre-processing correction factor is extracted from the path correction correlation factor, including the following steps:

[0102] According to the anatomical structure data and tissue characteristic parameters of the current to-be-peeled tendon, the current tissue stress distribution characteristics are extracted;

[0103] The current tissue stress distribution characteristics are matched with historical stress distribution characteristics to extract a pre-processing correction factor from the path correction correlation factors.

[0104] For example, the force data of different parts of the tendon is collected by using a tension sensor, and the deformation of the tissue is analyzed in combination with the ultrasound image to sort out the current tissue stress distribution characteristics of the tendon to be stripped. The current tissue stress distribution characteristics are compared and matched with various historical stress distribution characteristics accumulated in the historical operation data. Because different stress distribution characteristics in the historical data correspond to different path correction correlation factors, the most suitable part of the path correction correlation factors is selected by matching the current surgical tendon stress characteristics, which is extracted as a pre-processing correction factor. In subsequent planning of the stripping path of the current operation, the path is adjusted according to the pre-processing correction factor, so that the path is more suitable for the mechanical environment of the current tendon, and the risk of tissue damage caused by stress mismatch is reduced.

[0105] According to the pre-processing correction factor, the stripping path trajectory of the key position of the tendon to be stripped is judged to obtain a first path trajectory and a second path trajectory, which specifically includes the following steps:

[0106] Obtain the key functional area of the tendon to be stripped;

[0107] Position the position of the key functional area in the stripping area to obtain a target position point;

[0108] Statistical space distance and tissue density difference value between the target position point and the first monitoring point to obtain a first pre-processing parameter;

[0109] Statistical space distance and tissue density difference value between the target position point and the second monitoring point to obtain a second pre-processing parameter;

[0110] Obtain the main stripping area tissue hardness data of the current stripping area;

[0111] Detect the initial stripping force to which the first monitoring point belongs according to the main stripping area tissue hardness data to obtain a measured initial stripping force value, and the initial stripping force to which the second monitoring point belongs is the same as the initial stripping force to which the first monitoring point belongs;

[0112] According to the first pre-processing parameter, the measured initial stripping force value and the pre-processing correction factor, the first path trajectory to which the target position point belongs is judged;

[0113] According to the second pre-processing parameter, the measured initial stripping force value and the pre-processing correction factor, the second path trajectory to which the target position point belongs is judged.

[0114] Firstly, the key functional area and the target position point are determined. The key functional area of the tendon to be stripped is obtained, which is the core part of the tendon to realize physiological function, such as the area where the tendon fiber bundle is dense and plays a key role in mechanical transmission. The accuracy of the stripping path directly affects the surgical effect. After determining the key functional area, the specific position of the key functional area in the stripping area is located, thereby obtaining the target position point. This step locks the core reference point for subsequent path derivation, allowing the path planning to focus on the key anatomical site.

[0115] The spatial distance and tissue density difference value between the target position point and the first monitoring point are counted to obtain the first preprocessing parameter; the spatial distance and tissue density difference value between the target position point and the second monitoring point are counted to obtain the second preprocessing parameter. The spatial distance reflects the spatial correlation between the target position and the end point of the stripping path, and the tissue density difference value reflects the change of the tissue characteristics of different regions. The combination of the two forms the first preprocessing parameter and the second preprocessing parameter, which comprehensively depicts the anatomical feature difference between the target position and the key monitoring point. The tissue hardness data of the main stripping area of the current stripping area is obtained. The tissue hardness is closely related to the initial stripping force required during stripping. According to the hardness data, the initial stripping force of the first monitoring point is detected to obtain the measured initial stripping force value, and the initial stripping force of the second monitoring point is consistent with that of the first monitoring point.

[0116] According to the first preprocessing parameter, the measured initial stripping force value and the preprocessing correction factor, the first path trajectory to which the target position point belongs is determined. The first preprocessing parameter includes the spatial distance and the tissue density difference value between the target position point and the first monitoring point, reflecting the anatomical feature difference of the two key positions. The spatial distance determines the extension scale of the path in space, and the tissue density difference value reflects the response difference of different regions of the tissue to the stripping operation. The measured initial stripping force value represents the size of the force required at the initial stage of stripping under the tissue hardness of the main stripping area, which is the basic reference in the mechanical aspect of path planning, because the size of the stripping force will directly affect the way of action on the tissue during path advancement. The preprocessing correction factor is a correction basis adapted to the current tissue stress distribution characteristics refined from historical experience, which can adjust the path preliminarily planned based on the anatomical parameters and the initial force value. A mechanics-anatomy coupling model is constructed in advance, the first preprocessing parameter is input into the mechanics-anatomy coupling model, and the initial path direction is simulated in combination with the measured initial stripping force value; the initial path is corrected according to the preprocessing correction factor, which adjusts the direction and curvature parameters of the path according to the path deviation law under similar anatomical and mechanical characteristics in history, so that the path not only fits the current anatomical structure and initial force requirement, but also avoids potential risks by learning from historical experience. Through such comprehensive operation and correction of the mechanics-anatomy coupling model, the first path trajectory that fits the correlation characteristics of the target position point and the first monitoring point can be output, providing clear path guidance for the stripping operation.

[0117] According to the second pretreatment parameter, the initial peeling force value to be measured and the pretreatment correction factor, the second path trajectory to which the target position point belongs is judged. From the key area positioning, multi-dimensional parameter acquisition and fusion of historical correction factors and mechanical data, the path is derived, and the peeling path of the key position of the tendon to be peeled is gradually determined, which provides specific trajectory reference for subsequent risk assessment and path optimization. The peeling path planning not only fits the current anatomical characteristics, but also integrates historical experience and mechanical laws, thereby improving the scientificity and safety of the surgical path.

[0118] According to the first path trajectory and the second path trajectory, a comprehensive path deviation factor is counted, specifically including the following steps:

[0119] The tissue strain frequency of the first monitoring point is detected to obtain a pretreatment strain frequency;

[0120] The path deviation value of the first path trajectory is multiplied by the pretreatment strain frequency to obtain a first damage risk factor;

[0121] The path deviation value of the second path trajectory is multiplied by the pretreatment strain frequency to obtain a second damage risk factor;

[0122] The first damage risk factor and the second damage risk factor are summed to obtain a comprehensive path deviation factor.

[0123] The tissue strain frequency of the first monitoring point is detected, and the tissue strain frequency reflects the number of strain fluctuations per unit time of the tissue at the monitoring point in the peeling 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 damage risk caused by subsequent path deviation.

[0124] The path deviation value of the first path trajectory is multiplied by the pretreatment strain frequency to obtain a first damage risk factor. Path deviation will cause mechanical disturbance to the tissue, and strain frequency reflects the sensitivity of the tissue to disturbance. Multiplying the two can quantify the damage risk. The larger the deviation and the higher the strain frequency, the larger the first damage risk factor value, thereby representing the higher damage of the path to the tissue. The path deviation value of the second path trajectory is multiplied by the pretreatment strain frequency to obtain a second damage risk factor.

[0125] The first damage risk factor and the second damage risk factor are summed to superimpose and integrate the damage risks of the two paths. The comprehensive path deviation factor can comprehensively reflect the overall damage risk level caused by path deviation of the two peeling paths in the tissue strain sensitive environment, and provide a quantitative basis for subsequent judgment of tissue damage risk value and optimization of peeling path.

[0126] According to the comprehensive path deviation factor, the tissue damage risk value of the tendon to be stripped caused by the path deviation in the stripping process is judged, and the current damage risk value is obtained, which specifically includes the following steps:

[0127] An injury database under different path deviation values and different tissue strain frequencies is obtained.

[0128] The comprehensive path deviation factor is matched with the injury database to obtain the current damage risk value of the tendon to be stripped.

[0129] The injury database is constructed by a large number of anterior cruciate ligament tendon stripping surgery cases and biomechanical experiments. The injury database stores tissue injury data corresponding to different path deviation values and different tissue strain frequency combinations, such as the probability of injury and the severity level of injury.

[0130] The comprehensive path deviation factor is compared with the data in the injury database. The comprehensive path deviation factor contains the damage risk superposition information of the two path trajectories in the current surgery, which integrates the correlation influence of path deviation and tissue strain frequency. In the matching process, the key parameters of path deviation value and tissue strain frequency in the comprehensive path deviation factor are used to find the most suitable record in the injury database. Through matching, the historical experience and the actual situation of the current surgery are combined to deduce the current damage risk value of the tendon to be stripped under the current path planning, so that the surgeon can clearly know the degree of tissue damage caused by the stripping path, and provide key risk quantification basis for subsequent path adjustment and how to adjust the path.

[0131] According to the current damage risk value, an optimal adjustment path is obtained, which specifically includes the following steps:

[0132] If the current damage risk value is greater than or equal to a preset tissue damage warning threshold, output path adjustment notification information;

[0133] According to the path adjustment notification information, the optimal adjustment path of the tendon to be stripped is obtained to obtain an optimal stripping path;

[0134] The high stress area of each optimal stripping path is collected to obtain an auxiliary path high stress area;

[0135] The stress intensity and area of the auxiliary path high stress area are detected to obtain auxiliary path stress characteristic data;

[0136] The stress intensity and high stress area of the main stripping region are detected to obtain main path stress characteristic data;

[0137] The optimal adjustment path corresponding to the auxiliary path stress characteristic data smaller than the main path stress characteristic data is extracted from the optimal stripping path.

[0138] Firstly, 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 the current planned stripping path exists tissue damage, so the path adjustment notification information is output, and the path optimization process is started.

[0139] According to the path adjustment notification information, combined with the anatomical structure of the current tendon to be stripped, the tissue stress distribution, and the multi-dimensional data of the to-be-measured space parameters, a plurality of optimized stripping paths are derived. These paths will actively avoid dangerous areas such as nerve and blood vessel bundles and high stress areas of the original path.

[0140] The high stress area of each optimized stripping path is located and collected to obtain the auxiliary path high stress area, the stress intensity and the area of the region are detected to integrate the auxiliary path stress characteristic data, and the mechanical risk of the optimized path is quantified; the stress intensity and the area of the high stress area of the original main stripping region are detected to obtain the main path stress characteristic data, so that the mechanical risks of the optimized path and the original path have comparable dimensions.

[0141] The auxiliary path stress characteristic data of each optimized stripping path is compared with the main path stress characteristic data, so as to extract the optimized path with lower stress intensity and smaller high stress area. Because this kind of path can meet the stripping demand, and can more effectively reduce the mechanical load borne by the tissue, thereby reducing the damage risk, finally it is determined as the preferred adjustment path.

[0142] A dynamic path planning system for anterior cruciate ligament tendon stripping, comprising:

[0143] The acquisition module acquires the anatomical structure data of the anterior cruciate ligament tendon and the tissue characteristic parameters of the stripping region, and marks the stripping monitoring points in the key monitoring points of the stripping region;

[0144] The statistical module: statistics the space distance between adjacent stripping monitoring points and the tissue density difference value to obtain the to-be-measured space parameter;

[0145] The extraction module: extracts the historical tissue stress distribution characteristics of different anatomical structure types from the historical anterior cruciate ligament tendon stripping surgery data, and according to the historical tissue stress distribution characteristics, the path correction correlation factor between the stripping path deviation value and the to-be-measured space parameter in the historical surgery data is counted;

[0146] The path judgment module: according to the anatomical structure data and the tissue characteristic parameters of the current tendon to be stripped, the preprocessing correction factor is extracted from the path correction correlation factor, the stripping path trajectory at the key position of the tendon to be stripped is judged according to the preprocessing correction factor, and the first path trajectory and the second path trajectory are obtained;

[0147] The statistical judgment module: according to the first path trajectory and the second path trajectory, a comprehensive path deviation factor is counted, and according to the comprehensive path deviation factor, a tissue damage risk value of the tendon to be stripped in the stripping process is judged, and a current damage risk value is obtained;

[0148] The path determination module: according to the current damage risk value, an optimal adjustment path is obtained.

[0149] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements a dynamic path planning method for anterior cruciate ligament tendon stripping when executing the program.

[0150] As shown in Fig. 3 The electronic device can 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 the logical instructions in the memory 630 to execute a dynamic path planning method for anterior cruciate ligament tendon stripping.

[0151] In addition, the logical instructions in the memory 630 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0152] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, and the computer can execute a dynamic path planning method for anterior cruciate ligament tendon stripping.

[0153] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement a dynamic path planning method for anterior cruciate ligament tendon stripping.

[0154] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0155] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0156] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of dynamic path planning for ACL tendon dissection, characterized by, The method comprises the following steps: Obtain the anatomical structure data of the anterior cruciate ligament tendon and the tissue characteristic parameters of the region to be stripped, and mark the key monitoring points of the region to be stripped to obtain the stripping monitoring points; Statistical adjacent stripping monitoring points between the spatial distance and the tissue density difference value to obtain the to-be-measured spatial parameter; Extract the historical tissue stress distribution characteristics of different anatomical structure types from the historical anterior cruciate ligament tendon stripping surgery data, and statistically obtain the path correction correlation factor between the stripping path deviation value and the to-be-measured spatial parameter in the historical surgery data; According to the anatomical structure data and the tissue characteristic parameters of the current anterior cruciate ligament tendon, a pre-processing correction factor is extracted from the path correction correlation factor, and the stripping path trajectory at the key position of the anterior cruciate ligament tendon is judged according to the pre-processing correction factor, to obtain a first path trajectory and a second path trajectory; According to the first path trajectory and the second path trajectory, a comprehensive path deviation factor is calculated, and the tissue damage risk value of the anterior cruciate ligament tendon caused by the path deviation in the stripping process is judged according to the comprehensive path deviation factor, to obtain a current damage risk value; According to the current damage risk value, an optimal adjustment path is obtained.

2. The method of claim 1, wherein, Mark the key monitoring points of the region to be stripped to obtain the stripping monitoring points, which specifically comprises the following steps: Obtain the total length and cross-sectional diameter of the anterior cruciate ligament tendon in the region to be stripped; Mark the key monitoring points of the tendon attachment point of the region to be stripped to obtain a first monitoring point; Mark the key monitoring points of the proximal tendon free end of the region to be stripped to obtain a second monitoring point; According to the total length, cross-sectional diameter, first monitoring point and second monitoring point, mark the sub-monitoring points of the region to be stripped to obtain the sub-monitoring points; The first monitoring point, the second monitoring point and the sub-monitoring point are combined into the stripping monitoring point.

3. The method of claim 2, wherein the ACL tendon dissection dynamic path planning is characterized by, According to the historical tissue stress distribution characteristics, the path correction correlation factor between the stripping path deviation value and the to-be-measured spatial parameter in the historical surgery data is statistically obtained, which specifically comprises the following steps: According to the historical stress distribution characteristics, the historical stripping path deviation value to which the stripping monitoring point belongs is extracted from the historical surgery data; According to the to-be-measured spatial parameter, the path deviation of the adjacent stripping monitoring points in the historical stripping path deviation value is processed to obtain a deviation value; The deviation value and the to-be-measured spatial parameter are processed to obtain a to-be-measured deviation ratio; All to-be-measured deviation ratios are processed to obtain an average value to obtain the path correction correlation factor.

4. The method of claim 3, wherein the ACL tendon dissection dynamic path planning is characterized by, According to the anatomical structure data and the tissue characteristic parameters of the current anterior cruciate ligament tendon, a pre-processing correction factor is extracted from the path correction correlation factor, which specifically comprises the following steps: According to the anatomical structure data and the tissue characteristic parameters of the current anterior cruciate ligament tendon, the current tissue stress distribution characteristics are extracted; The current tissue stress distribution characteristics are matched with the historical stress distribution characteristics to extract the pre-processing correction factor from the path correction correlation factor.

5. The method of claim 4, wherein the ACL tendon dissection dynamic path planning is characterized by, According to the pre-processing correction factor, the stripping path trajectory at the key position of the anterior cruciate ligament tendon is judged to obtain a first path trajectory and a second path trajectory, which specifically comprises the following steps: Obtain the key functional area of the anterior cruciate ligament tendon; Position the position of the key functional area in the region to be stripped to obtain a target position point; Statistical target position point and the first monitoring point between the spatial distance and the difference value of tissue density, get the first pretreatment parameter; Statistical target position point and the second monitoring point between the spatial distance and the difference value of tissue density, get the second pretreatment parameter; Obtain the main stripping region tissue hardness data of the current region to be stripped; According to the main stripping region tissue hardness data, the initial stripping force to which the first monitoring point belongs is detected to obtain the measured initial stripping force value, and the initial stripping force to which the second monitoring point belongs is the same as the initial stripping force to which the first monitoring point belongs; According to the first pretreatment parameter, the measured initial stripping force value and the pretreatment correction factor, the first path trajectory to which the target position point belongs is judged; According to the second pretreatment parameter, the measured initial stripping force value and the pretreatment correction factor, the second path trajectory to which the target position point belongs is judged.

6. The method of claim 5, wherein the ACL tendon dissection dynamic path planning is characterized by, According to the first path trajectory and the second path trajectory, the comprehensive path deviation factor is counted, which specifically includes the following steps: The tissue strain frequency of the first monitoring point is detected to obtain the pretreatment strain frequency; The path deviation value of the first path trajectory is multiplied by the pretreatment strain frequency to obtain the first damage risk factor; The path deviation value of the second path trajectory is multiplied by the pretreatment strain frequency to obtain the second damage risk factor; The first damage risk factor and the second damage risk factor are summed to obtain the comprehensive path deviation factor.

7. The method of claim 6, wherein the ACL tendon dissection dynamic path planning is characterized by, According to the comprehensive path deviation factor, the tissue damage risk value generated by the path deviation during the stripping process of the tendon to be stripped is judged to obtain the current damage risk value, which specifically includes the following steps: Obtain the damage database under the condition of different path deviation values and different tissue strain frequencies; The comprehensive path deviation factor is matched with the damage database to obtain the current damage risk value of the tendon to be stripped.

8. The method of claim 7, wherein the ACL tendon dissection dynamic path planning is characterized by, According to the current damage risk value, the preferred adjustment path is obtained, which specifically includes the following steps: If the current damage risk value is greater than or equal to the preset tissue damage warning threshold, output the path adjustment notification information; According to the path adjustment notification information, the optimization adjustment path of the current tendon to be stripped is judged to obtain the optimization stripping path; The high stress area of each optimization stripping path is collected to obtain the auxiliary path high stress area; The stress intensity and area of the auxiliary path high stress area are detected to obtain the auxiliary path stress characteristic data; The stress intensity and high stress area of the main stripping region belong to the main path stress characteristic data; The preferred adjustment path corresponding to the auxiliary path stress characteristic data smaller than the main path stress characteristic data is extracted from the optimization stripping path.

9. A dynamic path planning system for ACL tendon stripping, applied to the dynamic path planning method for ACL tendon stripping according to any one of claims 1 to 8, characterized in that, It includes: The acquisition module: the anatomical structure data of the anterior cruciate ligament tendon and the tissue characteristic parameters of the region to be stripped are obtained, and the key monitoring points of the region to be stripped are marked to obtain the stripping monitoring points; The statistical module: the spatial distance and the difference value of tissue density between adjacent stripping monitoring points are counted to obtain the measured spatial parameter; The extraction module: the historical tissue stress distribution characteristics of different anatomical structure types are extracted from the historical anterior cruciate ligament tendon stripping surgery data, and the path correction correlation factor between the stripping path deviation value and the measured spatial parameter in the historical surgery data is counted according to the historical tissue stress distribution characteristics; The path judgment module extracts a pre-processing correction factor from the path correction correlation factor according to the anatomical structure data and the tissue characteristic parameter of the tendon to be stripped, judges the stripping path trajectory at the key position of the tendon to be stripped according to the pre-processing correction factor, and obtains a first path trajectory and a second path trajectory. The statistical judgment module statistically obtains a comprehensive path deviation factor according to the first path trajectory and the second path trajectory, judges the tissue damage risk value of the tendon to be stripped in the stripping process affected by the path deviation according to the comprehensive path deviation factor, and obtains a current damage risk value. The path determination module obtains an optimal adjustment path according to the current damage risk value.

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

Images