A preoperative planning method and system for bone joint replacement

Abstract

This invention relates to the field of orthopedic medical technology and discloses a method and system for preoperative planning of bone and joint replacement surgery. The method includes acquiring medical images to construct a three-dimensional model of the skeleton and identifying joint movement trajectories; marking anatomical reference points to calculate the ideal orientation of the joint movement axis and establish a virtual reference system; simulating contact pressure distribution under different activity angles and calculating stress distribution maps of the implant at multiple locations; selecting an installation position that homogenizes contact pressure to generate a preliminary plan; combining the elastic modulus of soft tissue to calculate the ligament tension change curve and adjusting the implant orientation to generate a balanced optimization plan; and finally simulating the postoperative dynamic process to predict abnormal loads and reverse-correcting parameters to generate the final execution plan.

Description

Technical Field

[0001] This invention relates to the field of orthopedic medical technology, specifically to a method and system for preoperative planning of bone and joint replacement surgery. Background Technology

[0002] In preoperative planning for joint replacement surgery, current methods mainly rely on medical imaging to reconstruct a three-dimensional model of the skeleton. The joint movement axis and prosthesis placement are then determined on the model based on anatomical landmarks. Experience or statistical averages are often used to determine the implant size and placement. While this method provides a reference for morphological matching, it is insufficient in assessing the biomechanical state during joint movement and struggles to reflect the load differences between different implant positions during actual activity.

[0003] Current technologies lack systematic simulation of joint contact pressure at multiple angles of motion, and fail to generate and quantify stress distribution for different potential implant locations. This results in the inability to select prosthesis placement based on uniform contact pressure, relying instead on morphological alignment or clinical conventions. This can lead to localized high-pressure zones during postoperative use, affecting lifespan and functional stability. Furthermore, existing methods generally neglect the elastic properties of individual soft tissues and the changes in ligament tension throughout the joint's range of motion, adjusting prosthesis orientation solely based on bony alignment. This fails to detect movement limitations and discomfort caused by sudden changes or mismatches in ligament tension, limiting the ability of preoperative planning to control dynamic functional balance. Therefore, stress analysis of implant placement based on multi-posture contact pressure distribution during joint movement, and calculation of ligament tension changes throughout the joint's range of motion using soft tissue elastic modulus to adjust prosthesis orientation accordingly, represent unmet needs in current preoperative planning. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for preoperative planning of osteoarthritis, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a preoperative planning method for osteoarthritis, the method comprising:

[0006] Acquire the patient's medical imaging data, construct a three-dimensional digital model of the skeletal structure, and identify joint movement trajectories;

[0007] Anatomical reference points are marked on the three-dimensional digital model, the ideal spatial orientation of the joint motion axis is calculated, and a virtual reference system for mechanical alignment is established.

[0008] Based on the virtual reference system, the contact pressure distribution of the joint under different activity angles is simulated, and the stress distribution diagram of the implant is calculated at multiple potential installation positions.

[0009] Based on the stress distribution diagram, the implant installation position and angle that make the contact pressure uniform are selected, and a preliminary placement plan including the implant model, position and orientation is generated.

[0010] Based on the preliminary placement plan, and combined with the patient's soft tissue elastic modulus data, the tension change curve of the ligaments during the entire range of joint movement was calculated;

[0011] Based on the tension change curve, the spatial orientation of the implant is adjusted until the ligament tension remains smooth within the range of joint movement, generating a soft tissue balance optimization scheme.

[0012] Based on the aforementioned soft tissue balance optimization scheme, the dynamic process of the joint during walking and flexion-extension movements after surgery is simulated to predict the timing and location of abnormal loads.

[0013] Based on the predicted abnormal load information, the implant installation parameters are corrected in reverse to generate the final surgical execution plan.

[0014] Preferably, the calculation of the ideal spatial orientation of the joint motion axis specifically includes:

[0015] The bony anatomical landmarks of the joints are extracted from the three-dimensional digital model, including the center of the femoral head, the center of the tibial plateau, and the center of the ankle joint.

[0016] Connecting the center of the femoral head with the center of the ankle joint forms the overall biomechanical axis of the lower limb;

[0017] Establish an orthogonal coordinate system at the center of the tibial plateau and calculate the projection angle of the overall mechanical axis in the orthogonal coordinate system.

[0018] Based on the projection angle and combined with the ideal alignment parameters in the standard anatomical database, the spatial angles of the coronal, sagittal, and transverse planes of the individualized joint motion axis are calculated. These spatial angles constitute a virtual reference system for mechanical alignment.

[0019] Preferably, the calculation of the stress distribution map of the implant at multiple potential installation locations specifically includes:

[0020] On the three-dimensional digital model, with the virtual reference system as a reference, a parameter space for implant installation is defined, which includes inversion and eversion angles, forward tilt and backward tilt angles, and implantation depth.

[0021] Grid sampling is performed within the parameter space, and the implant installation parameter combination corresponding to each sampling point is used to generate a virtual implant model in the computer.

[0022] Boolean operations are performed between the virtual implant model and the three-dimensional digital model of the patient's bones to obtain the contact interface between the bone and the implant.

[0023] A standardized load based on the patient's body weight is applied to the contact interface, simulating the joint's movement from extension to maximum flexion;

[0024] At each step angle during the motion process, the pressure distribution on the contact interface is calculated, and the peak pressure and pressure distribution uniformity index are recorded.

[0025] After traversing all sampling points, the peak pressure sequence and pressure uniformity index sequence under each combination of installation parameters are integrated to generate a stress distribution map indexed by the installation parameters.

[0026] Preferably, generating a preliminary placement plan including the implant model, location, and orientation specifically includes:

[0027] In the stress distribution spectrum, an upper limit threshold for peak pressure and a lower limit threshold for pressure distribution uniformity index are set;

[0028] All installation parameter combinations that simultaneously satisfy the condition that the peak pressure is lower than the upper threshold and the pressure uniformity index is higher than the lower threshold are selected to form a candidate scheme set;

[0029] Based on the installation parameters of each scheme in the candidate scheme set, calculate the comprehensive deviation value between it and the standard anatomical position;

[0030] The installation parameter combination with the smallest overall deviation value is selected from the candidate scheme set as the optimal installation parameter;

[0031] The optimal installation parameters are matched with the implant model database to select the implant model that best matches the patient's bone size and can achieve the optimal installation parameters;

[0032] The optimal installation parameters are then bound to the selected implant model to generate a preliminary placement plan containing precise three-dimensional coordinates and angles.

[0033] Preferably, the calculation of the ligament tension change curve throughout the entire joint movement specifically includes:

[0034] The major ligament structures, including the medial collateral ligament, lateral collateral ligament, and cruciate ligament, are segmented and reconstructed from the patient's medical imaging data to create three-dimensional models.

[0035] Each ligament's three-dimensional model is assigned a nonlinear elastic material property based on tissue characteristics, which is personalized according to the patient's age and gender;

[0036] In a computer simulation environment, the anatomical origin and insertion points of the ligament's three-dimensional model are fixed to the corresponding positions of the bone and the virtual implant model, respectively.

[0037] The joints are driven to move along a preset trajectory, and the length changes of each ligament are calculated in real time during the movement.

[0038] Based on the real-time length and initial resting length of each ligament, and combined with the properties of the nonlinear elastic material, the real-time tension value of each ligament during motion is calculated.

[0039] Plot the joint range of motion angle as the abscissa and the ligament tension value as the ordinate, and draw the tension change curve of each ligament as a function of angle throughout the entire range of motion of the joint.

[0040] Preferably, the soft tissue balance optimization scheme specifically includes:

[0041] Analyze the tension change curve to identify non-smooth transition points where tension suddenly increases or decreases at a specific angle of activity;

[0042] An optimization objective function is established with the minor adjustment parameters of the implant as variables. The objective function aims to minimize the variance of the rate of change of tension of all ligaments throughout the entire range of motion.

[0043] An iterative optimization algorithm is used to perform minute translations and rotations in three-dimensional space based on the implant position and orientation determined by the preliminary placement plan;

[0044] After each adjustment, a new ligament tension change curve is recalculated and generated, and the value of the optimization objective function is calculated.

[0045] When the value of the optimization objective function reaches the preset convergence criterion, the iteration stops, and the implant adjustment parameters at this time are the optimal parameters for soft tissue balance.

[0046] The optimal parameters for soft tissue balance are updated in the preliminary placement scheme, overriding the original position and orientation parameters, to form an optimized soft tissue balance scheme.

[0047] Preferably, the predicted timing and location of the abnormal load specifically include:

[0048] Establish a multibody dynamics model that includes the mechanics of bones, implants, ligaments, and simplified muscles;

[0049] Define the driving conditions for standard gait cycle motion and daily activity movements for the multibody dynamics model;

[0050] Run dynamic simulations to calculate the magnitude and direction of dynamic loads on the joint contact interface during the complete gait cycle and flexion-extension movements;

[0051] Monitor the dynamic load, detect events where the load vector direction suddenly deviates from the normal physiological range, and record the simulation time point of the event and its corresponding position on the contact interface;

[0052] Analyze all detected events and statistically determine the phases and contact interface regions where abnormal load events occur in clusters;

[0053] The phase distribution map and thermal map of the contact interface location of the abnormal load event are output as prediction information for the abnormal load.

[0054] Preferably, the installation parameters of the reverse correction implant specifically include:

[0055] The heat map of the contact interface location of the abnormal load event is mapped back to the implant model surface in the soft tissue balance optimization scheme.

[0056] Analyze the relative positional relationship between the high-load area and the edge of the implant design features in the heat map to determine whether the abnormal load is caused by the protrusion of the implant edge or improper placement.

[0057] If the problem is determined to be caused by improper implant position or orientation, the adjustment direction of the implant will be recalculated in the parameter space, guided by reducing the load value of the high-load area.

[0058] While maintaining a smooth change in ligament tension, the orientation of the implant is adjusted to a limited extent to generate multiple modification candidate schemes;

[0059] For each modified candidate scheme, re-perform the dynamic simulation and calculate the degree of reduction in its abnormal load events;

[0060] The candidate modification scheme that maximizes the reduction of abnormal load events is selected, and its installation parameters are determined as the final modification parameters.

[0061] Preferably, generating the final surgical execution plan further includes:

[0062] The final correction parameters are integrated into the soft tissue balance optimization scheme to form unified implant placement data;

[0063] Based on the unified implant placement data, calculate the digital design parameters of the bone cutting guide required during the surgery;

[0064] The digital design parameters of the bone cutting guide are registered with the three-dimensional digital model of the patient's bones to generate guide installation positioning instructions.

[0065] By integrating the unified implant placement data, the digital design parameters of the bone cutting guide, and the guide installation and positioning instructions, a final surgical execution plan containing executable instructions is generated.

[0066] Preferably, when the processor executes the computer program, it implements the steps of a preoperative planning method for osteoarthritis as described in any of the above-mentioned methods.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] Based on the established anatomical reference points and virtual reference system of joint motion axes, the contact pressure distribution of the joint at multiple different angles of motion is simulated, and the stress distribution of the implant at multiple potential installation positions is calculated. Contact pressure homogenization is used as the selection criterion to determine the implant type, position, and orientation, allowing for a systematic comparison of the mechanical responses of different postures and position combinations preoperatively. Quantitative analysis covering multi-angle and multi-position stress fields enables the planning to intuitively present the load distribution differences of each candidate scheme, thereby avoiding local high-pressure areas that are difficult to detect from a single anatomical alignment perspective. The contact pressure homogenization selection method ensures that the prosthesis placement directly corresponds to the balanced load on the articular surface in various movements, reducing interface wear and early loosening tendency caused by load concentration during postoperative joint flexion, extension, rotation, and other complex movements, thus creating a more stable mechanical environment.

[0069] By combining the patient's soft tissue elastic modulus data, the tension change curve of the ligaments throughout the entire range of joint movement is calculated. The spatial orientation of the implant is adjusted based on the smoothness of the curve, allowing for the preoperative capture of nonlinear changes and abnormal peaks in ligament stress during movement. Introducing individualized elastic parameters for full-range tension projection enables the planning to identify abrupt changes or sustained overload segments in ligament tension caused by improper prosthesis placement. Iterative orientation targeting a smooth tension curve ensures that the prosthesis layout conforms to the physiological bearing rhythm of the ligaments, maintaining a gradual transition in tension during movement and avoiding movement stagnation or interruption of force transmission due to uneven tension. Achieving dynamic coordination between the ligament and the prosthesis allows the joint to maintain more consistent biomechanical synergy during continuous movements such as walking and squatting postoperatively, reducing movement compensation and discomfort caused by soft tissue imbalance. Attached Figure Description

[0070] Figure 1 This is a schematic diagram illustrating the working principle of the preoperative planning method for osteoarthritis described in this invention.

[0071] Figure 2 A flowchart for calculating the ideal spatial orientation of joint motion axes;

[0072] Figure 3 A flowchart for calculating the stress distribution of an implant at multiple potential installation locations;

[0073] Figure 4 A comparison diagram of abnormal loads in different optimization schemes during preoperative planning for osteoarthritis.

[0074] Figure 5 Heatmap showing the distribution of abnormal load events on the surface of the acetabular cup prosthesis. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please see Figure 1 This invention provides a preoperative planning method for osteoarthritis, comprising: acquiring the patient's medical imaging data to construct a three-dimensional digital model of the skeletal structure and identifying the joint movement trajectory; marking anatomical reference points on the three-dimensional digital model and calculating the ideal spatial orientation of the joint movement axis to establish a virtual reference system for mechanical alignment; simulating the contact pressure distribution of the joint at different activity angles and calculating the stress distribution map of the implant at multiple potential installation positions; selecting the implant installation position and angle to homogenize the contact pressure based on the stress distribution map to generate a preliminary placement plan; calculating the ligament tension change curve throughout the joint movement by combining the patient's soft tissue elastic modulus data; adjusting the spatial orientation of the implant according to the tension change curve until the ligament tension remains smooth within the joint movement range to generate a soft tissue balance optimization plan; simulating the dynamic process of the joint during walking and flexion-extension movements after surgery to predict the timing and location of abnormal loads; and finally, reversing the implant installation parameters based on the predicted abnormal load information to generate the final surgical execution plan.

[0077] In one embodiment of the present invention, see [reference] Figure 2 The bony anatomical landmarks of the joints are extracted from the three-dimensional digital model. These landmarks include the center of the femoral head, the center of the tibial plateau, and the center of the ankle joint. The center of the femoral head and the center of the ankle joint are connected to form the overall mechanical axis of the lower limb. An orthogonal coordinate system is established at the center of the tibial plateau and the projection angle of the overall mechanical axis in the orthogonal coordinate system is calculated. Based on the projection angle and the ideal alignment parameters in the standard anatomical database, the spatial angles of the coronal, sagittal, and transverse planes of the individualized joint motion axis are calculated. These spatial angles constitute the virtual reference system for mechanical alignment.

[0078] In this specific implementation, we will take a patient who needs to undergo total knee replacement surgery as an example. In this implementation, the patient's preoperative computed tomography (CT) scan data was used to reconstruct a three-dimensional digital model of the lower limb skeleton, including the femur, tibia, and ankle joint regions. In the three-dimensional digital model, the position coordinates of the femoral head center, tibial plateau center, and ankle joint center were accurately identified and marked through semi-automatic segmentation and geometric fitting algorithms.

[0079] In some embodiments, the operation of connecting the center of the femoral head and the center of the ankle joint to form the overall mechanical axis of the lower limb is achieved by calculating a linear vector between the two points in three-dimensional space. This vector represents the force transmission path of the lower limb under ideal weight-bearing conditions. Subsequently, an orthogonal coordinate system is established at the center of the tibial plateau. The axial definition of this coordinate system usually follows anatomical standards. For example, the coronal axis is parallel to the line connecting the medial and lateral sides of the tibial plateau, the sagittal axis is perpendicular to the coronal axis and points in the anteroposterior direction, and the transverse axis is perpendicular to the first two axes. The projection angle of the overall mechanical axis in this orthogonal coordinate system is calculated through vector projection and coordinate transformation.

[0080] It is understandable that calculating the projection angle is to quantitatively correlate the overall biomechanical axis with the anatomical orientation of the individual tibial plateau. Based on the projection angle and ideal alignment parameters from a standard anatomical database, the spatial angles of the individualized joint motion axis in the coronal, sagittal, and transverse planes are calculated. The standard anatomical database stores a large number of statistical parameters of lower limb force lines and joint angles from healthy individuals. The calculation process involves comparing and interpolating the individual projection angle with the ideal parameter range in the database, thereby deriving the spatial angle values ​​of a virtual reference system applicable to that specific patient as a biomechanical alignment benchmark. In one specific calculation, the coronal angle is the angle between the projection of the overall biomechanical axis onto the coronal plane and the perpendicular line to the tibial anatomical axis; the sagittal angle is the angle between the projection of the overall biomechanical axis onto the sagittal plane and the perpendicular line to the tibial anatomical axis; and the transverse angle involves the rotational relationship between the line connecting the posterior femoral condyles and the transverse axis of the tibial plateau. These angles collectively define the orientation of the virtual reference system. Optionally, a weighted compensation algorithm based on a standard anatomical database can be used when calculating the spatial angles, the expression of which is:

[0081]

[0082] in: This represents the final calculated individualized spatial angle. This represents the original projection angle calculated from the projection of the 3D digital model. This represents the ideal median value of the corresponding angle parameter in the standard anatomical database. It is a weighting coefficient that is dynamically adjusted based on the matching degree between the patient's anatomical characteristics and the database. The value of the weighting coefficient ranges from 0 to 1.

[0083] In some embodiments, to verify the accuracy of the calculation, the calculated virtual reference frame spatial angle can be compared with the lower limb force line angle obtained by traditional two-dimensional X-ray measurement. For example, in a set of implementation cases, for the same patient, the coronal mechanical axis angle calculated based on the three-dimensional model and the above method is 4.2 degrees of eversion, while the angle obtained by manual measurement based on standard full-length standing X-ray is 4.5 degrees of eversion. The comparison of the two data shows a high degree of consistency, thereby confirming the reliability of the three-dimensional calculation method in terms of quantitative accuracy.

[0084] In one embodiment of the present invention, see [reference] Figure 3 A parameter space for implant installation is defined on a 3D digital model using a virtual reference system. This parameter space includes inversion / valgus angles, anteversion / retroversion angles, and implantation depth. Mesh sampling is performed within this parameter space, and the implant installation parameter combination corresponding to each sampling point generates a virtual implant model in the computer. Boolean operations are performed between the virtual implant model and the 3D digital model of the patient's skeleton to obtain the contact interface between the bone and the implant. A standardized load based on the patient's body weight is applied to the contact interface, and the joint movement from extension to maximum flexion is simulated. At each step angle during the movement, the pressure distribution on the contact interface is calculated, and the peak pressure and pressure distribution uniformity index are recorded. After traversing all sampling points, the peak pressure sequence and pressure uniformity index sequence under each installation parameter combination are integrated. A stress distribution map indexed by installation parameters is generated. An upper threshold for peak pressure and a lower threshold for pressure distribution uniformity are set in the stress distribution map. All installation parameter combinations that simultaneously satisfy the condition of peak pressure below the upper threshold and pressure uniformity above the lower threshold are selected to form a candidate scheme set. The comprehensive deviation value between the installation parameters of each scheme and the standard anatomical position is calculated based on the installation parameters in the candidate scheme set. The installation parameter combination with the smallest comprehensive deviation value is selected as the optimal installation parameter. The optimal installation parameter is matched with an implant model database to select the implant model that best matches the patient's bone size and achieves the optimal installation parameter. The optimal installation parameter is then bound to the selected implant model to generate a preliminary placement plan containing precise three-dimensional coordinates and angles.

[0085] In practice, the femoral condyle prosthesis placement plan for total knee arthroplasty is used as an example scenario, and is carried out based on an established virtual reference system. In practice, a three-dimensional digital model is loaded into the planning software. Using the spatial angles of the virtual reference system as a benchmark, the parameter space for femoral condyle prosthesis placement is defined. The parameter space specifically includes the varus and valgus angles relative to the coronal plane of the virtual reference system, the anteversion and retroversion angles relative to the sagittal plane, and the implantation depth along the femoral anatomical axis. Each angle and depth is set with a range and step size based on clinical experience. For example, the varus and valgus angle range is -3 degrees to +3 degrees with a step size of 0.5 degrees; the anteversion and retroversion angle range is -2 degrees to +5 degrees with a step size of 0.5 degrees; and the implantation depth is defined as the distance from a specific anatomical plane at the distal end of the femur, ranging from 0 mm to 8 mm with a step size of 1 mm.

[0086] In some embodiments, mesh sampling in the parameter space means performing a full combination traversal of all angle and depth parameters to generate a virtual femoral condyle prosthesis model registered with the patient's bone model in a computer-aided design environment for each parameter combination. Boolean operations are then performed between the virtual femoral condyle prosthesis model and a three-dimensional digital model of the patient's distal femur. This step aims to accurately calculate and create a three-dimensional geometric model of the contact interface between the prosthesis and the bone cement or bone bed. The specific implementation method of performing Boolean operations between the virtual femoral condyle prosthesis model and the three-dimensional digital model of the patient's distal femur includes: In computer-aided design software, firstly, based on the installation parameters defined in the parameter space, the virtual femoral condyle prosthesis model and the three-dimensional digital model of the patient's distal femur are precisely spatially registered and aligned to ensure that the model position meets the individualized planning requirements; then, a Boolean difference operation is performed to subtract the virtual femoral condyle prosthesis model from the three-dimensional digital model of the patient's distal femur, thereby generating a three-dimensional geometric surface model representing the contact area between the prosthesis and the bone cement or bone bed; this three-dimensional geometric model of the contact interface accurately depicts the actual contact morphology between the implant and the bone, including the curvature, area, and boundary contour of the contact surface, providing a real geometric basis for subsequent calculations of standardized loads based on the patient's weight and pressure distribution during simulated joint movement. A standardized load based on the patient's weight is applied to the contact interface. The load value is determined by the product of the patient's weight and the activity coefficient and is evenly distributed on the contact surface. At the same time, the continuous movement process of the knee joint from 0 degrees of extension to 135 degrees of maximum flexion is simulated. The movement process is discretized into multiple step angles.

[0087] Understandably, at each buckling step angle, it is necessary to calculate the pressure distribution at the contact interface. This is accomplished through finite element analysis or contact mechanics algorithms, recording two key indicators: the peak contact pressure at that angle and a pressure distribution uniformity index reflecting the uniformity of the pressure distribution. The pressure distribution uniformity index can be quantified using a specific algorithm. One possible formula for calculating the pressure distribution uniformity index is as follows:

[0088]

[0089] in: This represents an index of pressure distribution uniformity. This represents the total number of units after discretization of the contact interface. Representing the Pressure value on each unit Representing all The average pressure value on each unit. After traversing all sampling points in the parameter space corresponding to the installation parameter combinations, the system integrates the peak pressure sequence and pressure distribution uniformity index sequence of each combination at all step angles during the entire buckling motion, forming a multidimensional stress distribution map indexed by the installation parameters. The map intuitively shows the differences in mechanical properties under different installation parameters.

[0090] In some embodiments, to generate a preliminary placement plan, screening thresholds need to be set in the stress distribution map. For example, the upper limit threshold for peak pressure is set to 25 MPa, and the lower limit threshold for pressure distribution uniformity index is set to 0.75. After screening, a candidate plan set containing dozens of installation parameter combinations that simultaneously meet the two threshold conditions is obtained. Then, the comprehensive deviation value between each plan and the standard anatomical position is calculated based on the installation parameters of each plan in the candidate plan set. The comprehensive deviation value is the weighted sum of squares of the deviations of each parameter from its ideal anatomical angle or depth. The installation parameter combination with the smallest comprehensive deviation value is selected as the optimal installation parameter from the candidate plan set. In a set of data comparisons, the comprehensive deviation value of the optimal installation parameter is 1.2 degrees, while the parameter combination with the largest comprehensive deviation value in the candidate plan set has a deviation of 5.7 degrees, which reflects the optimization of anatomical fit in the screening process. The optimal installation parameter is matched with an implant model database containing the three-dimensional dimensions and fitting parameters of various prostheses. The system selects a specific prosthesis model that best matches the patient's distal femoral dimensions and can achieve the varus / valgus, anteversion / posterior tilt, and depth required by the optimal installation parameters. Finally, the determined optimal installation parameters are linked to the selected implant model to generate a preliminary placement plan document containing the precise translational coordinates and rotational angles of the prosthesis in the three-dimensional digital model of the femur. Optionally, the preliminary placement plan can be presented in a three-dimensional visualization format for review by the planning physician.

[0091] In one embodiment of the present invention, a three-dimensional model of the main ligament structures, including the medial collateral ligament, lateral collateral ligament, and cruciate ligament, is segmented and reconstructed from the patient's medical imaging data. Each ligament's three-dimensional model is assigned a nonlinear elastic material property based on tissue characteristics. The material property is personalized according to the patient's age and gender. In a computer simulation environment, the anatomical origin and insertion points of the ligament's three-dimensional model are fixed to the corresponding positions of the bone and virtual implant models, respectively. The joint is driven to move along a preset movement trajectory, and the length change of each ligament is calculated in real time during the movement. Based on the real-time length and initial resting length of each ligament, combined with the nonlinear elastic material property, the real-time tension value of each ligament during the movement is calculated. A curve showing the tension of each ligament as a function of angle throughout the entire range of joint movement is plotted with the joint movement angle as the abscissa and the ligament tension value as the ordinate, i.e., the ligament tension change curve.

[0092] In the specific implementation, the tension analysis of the anterior cruciate ligament (ACL) and medial collateral ligament (MCL) is used as an example scenario, based on a pre-generated three-dimensional digital model of the knee joint containing a virtual implant model. Specifically, from the patient's knee MRI medical image data, a deep learning-based image segmentation algorithm is used to identify and segment the two-dimensional contours of the MCL, lateral collateral ligament, ACL, and posterior cruciate ligament. Subsequently, the continuous two-dimensional contours are reconstructed into three dimensions to generate independent three-dimensional models of each major ligament structure. These three-dimensional models represent the geometry and spatial orientation of the ligaments in the form of triangular meshes.

[0093] In some embodiments, each ligament's 3D model is assigned nonlinear elastic material properties based on tissue characteristics. Specific parameters of these material properties are derived from the force-elongation relationship of ligament tissues in biomechanical literature, with the patient's age and gender used as personalized adjustment factors. For example, for a 65-year-old female patient, the stiffness parameter of the medial collateral ligament is adjusted downwards according to the tissue degeneration coefficient corresponding to her age group. In a computer simulation environment, the reconstructed ligament 3D model is fixed with its proximal attachment point to the corresponding anatomical region of the femoral bone model and its distal attachment point to the corresponding position in a virtual implant model or tibial bone model, based on anatomical origin and insertion points, thereby completing the biomechanical constraint of the ligament structure. It can be understood that driving the joint to move along a preset trajectory is a prerequisite for calculating length changes. The preset trajectory is derived from the joint motion trajectory or a standard flexion-extension kinematic curve. During movement, the instantaneous length of each ligament is calculated in real time by solving the coordinate changes of the ligament attachment points in 3D space. Based on the real-time length and initial resting length of each ligament, combined with the nonlinear elastic material properties, the real-time tension value of each ligament during movement is calculated. The initial resting length is defined as the length of the ligament when the knee joint is in a straight, neutral position.

[0094] The relationship between ligament tension and length variation can be described by a nonlinear constitutive model. The specific implementation of this model involves constructing a mathematical relationship based on the biomechanical properties of ligament tissue, mapping real-time ligament length changes to corresponding tension values. The model is in polynomial form, containing one linear term and one cubic term. The linear term reflects the approximate elastic behavior of the ligament within a small deformation range, while the cubic term captures the nonlinear hardening effect of the material during large deformations. Model parameters include initial stiffness parameters and higher-order nonlinear stiffness parameters; for example, stiffness parameters can be appropriately reduced for elderly patients to simulate tissue degeneration. In computer simulations, the model is programmed as a real-time calculation module, taking as input the length variation data of a 3D ligament model reconstructed from medical images during joint movement, and outputting continuous tension values. One possible formula for calculating real-time ligament tension is:

[0095]

[0096] in: This represents the calculated real-time tension value of the ligament; This represents the initial stiffness parameters of the ligaments, which are individually adjusted based on the patient's age and gender. High-order nonlinear stiffness parameters representing ligaments; Represents the real-time length of the ligament; Represents the initial resting length of the ligament; ratio To adapt to the change in stress, by plotting the knee flexion angle on the x-axis and the calculated ligament tension value on the y-axis, a curve can be plotted showing the continuous change in tension of each ligament with the angle within the flexion range of 0 to 135 degrees. This curve is the ligament tension change curve.

[0097] In some embodiments, the length change of the medial collateral ligament can be illustrated by comparing the calculated lengths of the medial collateral ligament at 30° and 90° flexion. For example, in one simulation, the calculated length of the medial collateral ligament at 30° flexion was 78.2 mm, and the calculated length at 90° flexion was 82.7 mm. Its initial resting length was 75.5 mm. By substituting the material property parameters into the formula, the tension value at the corresponding angle can be calculated. It can be understood that the completed ligament tension change curve, output in chart form, visually displays the tension state and interaction relationship of different ligaments within the joint range of motion.

[0098] In one embodiment of the present invention, the ligament tension change curve is analyzed to identify non-smooth transition points where tension suddenly increases or decreases at specific activity angles. An optimization objective function is established with the implant's micro-adjustment parameters as variables. The optimization objective function aims to minimize the variance of the tension change rate of all ligaments throughout the entire range of motion. An iterative optimization algorithm is used to perform micro-translations and rotations in three-dimensional space based on the implant position and orientation determined in the initial placement plan. After each adjustment, a new ligament tension change curve is recalculated and generated, and the value of the optimization objective function is calculated. Iteration stops when the value of the optimization objective function reaches a preset convergence criterion. The implant adjustment parameters at this point are the optimal parameters for soft tissue balance. The optimal parameters for soft tissue balance are then updated to the initial placement plan. The study incorporates existing position and orientation parameters to create a soft tissue balance optimization scheme. It establishes a multibody dynamics model that includes bones, implants, ligaments, and simplified muscle mechanics. The model defines standard driving conditions for gait cycle movements and daily activities. Dynamic simulations are run to calculate the magnitude and direction of dynamic loads at joint contact interfaces during complete gait cycles and flexion-extension movements. Dynamic loads are monitored to detect events where the load vector direction suddenly deviates from the normal physiological range, and the simulation time point and corresponding position on the contact interface are recorded. All detected events are analyzed to statistically determine the phase and contact interface region where abnormal load events occur. The phase distribution map of abnormal load events and the heat map of contact interface positions are output as predictive information for abnormal loads.

[0099] In practical implementation, taking gait analysis after total knee arthroplasty as an example scenario, soft tissue balance optimization and abnormal load prediction are carried out based on the obtained ligament tension change curves. Specifically, the tension change curves of the medial collateral ligament, lateral collateral ligament, and cruciate ligament are analyzed. By calculating the first derivative of tension with respect to the joint range of motion, points where the absolute value of the derivative exceeds a preset threshold are identified. These points are marked as non-smooth transition points where tension suddenly increases or decreases at specific ranges of motion. For example, in one set of analyses, a non-smooth transition point with a sudden increase in tension was identified near 65 degrees of flexion in the medial collateral ligament.

[0100] In some embodiments, an optimization objective function is established with implant micro-adjustment parameters as variables, including the minute translations and rotations of the femoral prosthesis in the coronal, sagittal, and transverse planes. The optimization objective function aims to minimize the variance of the rate of change of tension in all four major ligaments throughout the entire 0 to 135 degree flexion range. A specific mathematical expression for the optimization objective function is as follows:

[0101]

[0102] in: This represents the value of the objective function that needs to be minimized. Index representing ligaments, Represents the joint flexion angle. Representing the The ligament at the angle The tension value below, Representing the The tension of the ligament at the angle rate of change at that point Representing the The mean rate of change of ligament tension throughout its range of motion. A sequential quadratic programming iterative optimization algorithm is employed. Based on the implant position and orientation determined in the initial placement plan, minute translations and rotations are performed in three-dimensional space. After each adjustment, the ligament tension calculation model is rerun to generate a new ligament tension change curve, and then a new optimization objective function value is calculated.

[0103] It is understandable that the iterative process continues until the decrease in the objective function value is less than the preset convergence criterion, for example, the function value changes by less than 0.1% after three consecutive iterations. At this point, the corresponding implant adjustment parameters are determined to be the optimal parameters for soft tissue balance. The optimal parameters for soft tissue balance are updated in the preliminary placement plan, overriding the original position and orientation parameters, forming a document containing the optimized prosthesis placement coordinates and angles. Based on this optimization plan, a multibody dynamics model is established, including the bone, implant, ligaments, and simplified quadriceps and hamstring muscle strength models. The model is constructed in dynamics software, and all rigid and flexible bodies are assigned corresponding mass, inertia, and material properties. Standard gait cycle motion driving conditions are defined for the multibody dynamics model. The driving conditions are derived from gait database data of healthy individuals, including the angle change curves of the hip, knee, and ankle joints during a complete gait cycle. Driving conditions for daily activities such as standing up from a seated position are also defined.

[0104] In some embodiments, dynamic simulations are performed to calculate the magnitude and direction of the dynamic load at the tibia-femoral prosthesis contact interface during a complete gait cycle and flexion-extension movements. The angle between the contact force vector and the contact surface normal is monitored. When this angle exceeds a threshold set based on physiological data, an event is identified where the load vector direction deviates from the normal physiological range. The system records the simulation time point of the event and its corresponding specific location coordinates on the contact interface mesh. Events detected in all gait cycle simulations are analyzed, and the gait phases and contact interface regions where abnormal load events occur are statistically analyzed. A phase distribution map and a contact interface location heatmap of the abnormal load events are output as predictive information. The phase distribution map is presented as a bar chart, and the contact interface location heatmap is superimposed on the prosthesis 3D model as colored contour lines. Optionally, the statistical results can be summarized in tabular form, as shown in Table 1, which displays the number of abnormal load events detected in different gait phase intervals during a single simulation.

[0105] Table 1: Distribution Statistics of Abnormal Loading Events in Gait Cycle

[0106] Gait phase interval (%) Number of abnormal load events 0-15 (Initial Landing) 2 15-30 (Load-bearing response) 5 30-45 (Mid-Standing) 3 45-60 (Late Standing Stage) 8 60-75 (pre-oscillation) 1 75-100 (Oscillating Period) 0

[0107] It is understandable that the data in Table 1 and the phase distribution map corroborate each other, jointly indicating that abnormal load events are concentrated in the late standing stage. The thermal map of the contact interface location visually shows the spatial distribution of high-load areas on the prosthesis surface.

[0108] See Figure 4 This is a comparison chart of abnormal loads from different optimized plans in the preoperative planning of osteoarthritis surgery. Both indicators show a synchronous downward trend with each optimization iteration, reflecting the effectiveness of the optimized plans. It visually demonstrates the significant role of "soft tissue balance optimization" and the "final revised plan" in reducing abnormal loads, providing data support for surgical plan selection. The abstract "optimization effect" is transformed into quantifiable event counts and load values, aiding in assessing the clinical safety of the plan. The improvement margin at each optimization stage is clearly defined, providing a reference for subsequent preoperative planning process optimization. The reduction in peak load can decrease the risk of postoperative complications such as prosthesis wear and osteolysis, providing data evidence for surgical safety.

[0109] In one embodiment of the present invention, the heat map of the contact interface location of the abnormal load event is mapped back to the implant model surface in the soft tissue balance optimization scheme. The relative positional relationship between the high-load area and the edge of the implant design features in the heat map is analyzed to determine whether the abnormal load is caused by the protrusion of the implant edge or improper position. If it is determined that it is caused by improper implant position or orientation, the adjustment direction of the implant is recalculated in the parameter space with the load value of the high-load area as the guide. Under the premise of maintaining the smooth change of ligament tension, the orientation of the implant is adjusted to a limited extent to generate multiple correction candidate schemes. For each correction candidate scheme, the dynamic simulation is re-executed and the calculation is performed. The degree of reduction of abnormal load events is determined by selecting the candidate modification scheme that maximizes the reduction of abnormal load events and determining its installation parameters as the final modification parameters. The final modification parameters are integrated into the soft tissue balance optimization scheme to form unified implant placement data. Based on the unified implant placement data, the digital design parameters of the bone cutting guide required in the operation are calculated. The digital design parameters of the bone cutting guide are registered with the three-dimensional digital model of the patient's bones to generate guide installation and positioning instructions. The unified implant placement data, the digital design parameters of the bone cutting guide, and the guide installation and positioning instructions are integrated to generate the final surgical execution plan containing executable instructions.

[0110] In practical implementation, taking the final modification of the acetabular cup prosthesis in total hip arthroplasty as an example scenario, a reverse modification is carried out based on the obtained abnormal load prediction information, namely the heat map and phase distribution map of the contact interface position, to generate the final solution. In the specific implementation, the heat map of the contact interface position of the abnormal load event is mapped back to the inner surface of the acetabular cup prosthesis model in the soft tissue balance optimization scheme through three-dimensional coordinate transformation. The heat map uses color depth to represent the load magnitude and is overlaid on the mesh model of the prosthesis surface. The relative positional relationship between the high load area in the heat map and the edge of the acetabular cup prosthesis design features is analyzed. For example, it is identified that the high load area is concentrated in the anterior superior edge area of ​​the acetabular cup prosthesis and the distance from the prosthesis edge is less than 2 mm. Based on biomechanical knowledge, it is determined that this kind of abnormal load is caused by insufficient anteversion angle or improper abduction angle of the acetabular cup prosthesis, resulting in non-physiological impact between the femoral neck and the cup rim within a certain range of motion.

[0111] In some embodiments, if the abnormal load is determined to be caused by improper implant position or orientation, the adjustment direction of the implant is recalculated in the parameter space defining the installation direction of the acetabular cup prosthesis, guided by reducing the load value in the high-load area. The parameter space includes the anteversion and abduction angles of the acetabular cup. An optimization objective function for quantifying the load value in the high-load area and guiding the adjustment is expressed as follows:

[0112]

[0113] in: This represents the load assessment value for the high-load area that needs to be minimized. This represents the number of surface mesh cells identified as high-load regions in the thermal map. Representing the Normalized load values ​​mapped onto high-load grid cells. This represents the weighting coefficient assigned based on the distance between the unit and the edge of the prosthesis; the closer the distance, the higher the weighting coefficient. The larger the angle, the better. While maintaining the smoothness of the optimized ligament tension change curve without significant deterioration, the anteversion and abduction angles of the acetabular cup prosthesis are adjusted to a limited extent. For example, the anteversion angle is combined within ±3 degrees of the optimized value, and the abduction angle within ±2 degrees, in steps of 0.5 degrees, to generate multiple candidate schemes for acetabular cup prosthesis orientation correction.

[0114] Understandably, for each generated correction candidate scheme, a multibody dynamics simulation involving skeleton, implants, ligaments, and simplified muscle models needs to be re-executed to simulate daily activities such as gait cycles, sit-ups, and squats, and to recalculate the number and distribution of abnormal load events detected. In a set of data comparisons, the original soft tissue balance optimization scheme detected a total of 12 abnormal load events in the simulated gait and squat movements, while the three different correction candidate schemes detected 5, 3, and 7 events respectively. The degree of reduction was quantified by comparing the percentage reduction in the total number of abnormal load events of each scheme relative to the original scheme. The correction candidate scheme that maximizes the reduction of abnormal load events, such as the scheme mentioned above that reduces the number of events from 12 to 3, was selected, and its corresponding acetabular cup anteversion angle and abduction angle installation parameters were determined as the final correction parameters.

[0115] In some embodiments, the final correction parameters are integrated into the soft tissue balance optimization scheme to form a unified implant placement data document containing the model, size, three-dimensional coordinates, and spatial angles of the acetabular cup prosthesis and femoral stem prosthesis. Based on the unified implant placement data, the digital design parameters of the acetabular cup bony acetabular reaming guide and femoral osteotomy guide required during surgery are calculated. These parameters include the shape of the contact surface between the guide and the patient's bone anatomical surface, the three-dimensional coordinates and orientation of the positioning pin holes, and the geometry of the guide channel for osteotomy or reaming. The digital design parameters of the bone cutting guide are registered with the three-dimensional digital model of the patient's pelvis and femur. The installation position of the guide is visualized and simulated in the software, and guide installation positioning instructions containing positioning marker lines and reference points are generated. By integrating the unified implant placement data, the digital design parameters of the bone cutting guide, and the guide installation positioning instructions, a final surgical execution plan containing executable instructions is generated. This plan can be directly output for 3D printing of customized surgical guides or input into the surgical robot navigation system. Optionally, the final surgical plan also includes a summary report for the surgeon to review, which uses charts to compare and contrast the changes in key parameters before and after optimization.

[0116] See Figure 5 This is a heatmap showing the distribution of abnormal load events on the surface of the acetabular cup prosthesis. Abnormal load events are highly concentrated in the "polar angle 40-60 degrees, azimuth angle 20-100 degrees" region, which is bright yellow (frequency nearly 80%); the remaining regions have a frequency of less than 20% and are dark in color. Identifying high-risk angle ranges: The "polar angle 40-60 degrees + azimuth angle 20-100 degrees" region is clearly identified as a high-incidence area for abnormal loads, providing a direct target for prosthesis angle adjustment during arthroplasty. For high-frequency areas, the contact surface morphology of the prosthesis at the corresponding angles can be optimized to reduce the probability of abnormal loads. This concentrated distribution characteristic can verify the accuracy of the preoperative biomechanical simulation model. Combined with the joint posture corresponding to the angles, patients can be guided to avoid specific movements, reducing the risk of complications.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of preoperative planning for a bone joint replacement, characterized in that, include: Acquire the patient's medical imaging data, construct a three-dimensional digital model of the skeletal structure, and identify joint movement trajectories; Anatomical reference points are marked on the three-dimensional digital model, the ideal spatial orientation of the joint motion axis is calculated, and a virtual reference system for mechanical alignment is established. Based on the virtual reference system, the contact pressure distribution of the joint under different activity angles is simulated, and the stress distribution diagram of the implant is calculated at multiple potential installation positions. Based on the stress distribution diagram, the implant installation position and angle that make the contact pressure uniform are selected, and a preliminary placement plan including the implant model, position and orientation is generated. Based on the preliminary placement plan, and combined with the patient's soft tissue elastic modulus data, the tension change curve of the ligaments during the entire range of joint movement was calculated; Based on the tension change curve, the spatial orientation of the implant is adjusted until the ligament tension remains smooth within the range of joint movement, generating a soft tissue balance optimization scheme. Based on the aforementioned soft tissue balance optimization scheme, the dynamic process of the joint during walking and flexion-extension movements after surgery is simulated to predict the timing and location of abnormal loads. Based on the predicted abnormal load information, the implant installation parameters are corrected in reverse to generate the final surgical execution plan.

2. A preoperative planning method for bone joint arthroplasty according to claim 1, characterized in that, The calculation of the ideal spatial orientation of the joint motion axis specifically includes: The bony anatomical landmarks of the joints are extracted from the three-dimensional digital model, including the center of the femoral head, the center of the tibial plateau, and the center of the ankle joint. Connecting the center of the femoral head with the center of the ankle joint forms the overall biomechanical axis of the lower limb; Establish an orthogonal coordinate system at the center of the tibial plateau and calculate the projection angle of the overall mechanical axis in the orthogonal coordinate system. Based on the projection angle and combined with the ideal alignment parameters in the standard anatomical database, the spatial angles of the coronal, sagittal, and transverse planes of the individualized joint motion axis are calculated. These spatial angles constitute a virtual reference system for mechanical alignment.

3. A preoperative planning method for bone joint arthroplasty according to claim 2, characterized in that, The calculation of stress distribution maps of the implant at multiple potential installation locations specifically includes: On the three-dimensional digital model, with the virtual reference system as a reference, a parameter space for implant installation is defined, which includes inversion and eversion angles, forward tilt and backward tilt angles, and implantation depth. Grid sampling is performed within the parameter space, and the implant installation parameter combination corresponding to each sampling point is used to generate a virtual implant model in the computer. Boolean operations are performed between the virtual implant model and the three-dimensional digital model of the patient's bones to obtain the contact interface between the bone and the implant. A standardized load based on the patient's body weight is applied to the contact interface, simulating the joint's movement from extension to maximum flexion; At each step angle during the motion process, the pressure distribution on the contact interface is calculated, and the peak pressure and pressure distribution uniformity index are recorded. After traversing all sampling points, the peak pressure sequence and pressure uniformity index sequence under each combination of installation parameters are integrated to generate a stress distribution map indexed by the installation parameters.

4. A preoperative planning method for bone joint arthroplasty according to claim 3, characterized in that, The generation of a preliminary placement plan, including the implant type, location, and orientation, specifically includes: In the stress distribution spectrum, an upper limit threshold for peak pressure and a lower limit threshold for pressure distribution uniformity index are set; All installation parameter combinations that simultaneously satisfy the condition that the peak pressure is lower than the upper threshold and the pressure uniformity index is higher than the lower threshold are selected to form a candidate scheme set; Based on the installation parameters of each scheme in the candidate scheme set, calculate the comprehensive deviation value between it and the standard anatomical position; The installation parameter combination with the smallest overall deviation value is selected from the candidate scheme set as the optimal installation parameter; The optimal installation parameters are matched with the implant model database to select the implant model that best matches the patient's bone size and can achieve the optimal installation parameters; The optimal installation parameters are then bound to the selected implant model to generate a preliminary placement plan containing precise three-dimensional coordinates and angles.

5. A preoperative orthopaedic surgery planning method according to claim 4, characterized in that, The calculation of the ligament tension change curve throughout the entire range of joint movement specifically includes: The major ligament structures, including the medial collateral ligament, lateral collateral ligament, and cruciate ligament, are segmented and reconstructed from the patient's medical imaging data to create three-dimensional models. Each ligament's three-dimensional model is assigned a nonlinear elastic material property based on tissue characteristics, which is personalized according to the patient's age and gender; In a computer simulation environment, the anatomical origin and insertion points of the ligament's three-dimensional model are fixed to the corresponding positions of the bone and the virtual implant model, respectively. The joints are driven to move along a preset trajectory, and the length changes of each ligament are calculated in real time during the movement. Based on the real-time length and initial resting length of each ligament, and combined with the properties of the nonlinear elastic material, the real-time tension value of each ligament during motion is calculated. Plot the joint range of motion angle as the abscissa and the ligament tension value as the ordinate, and draw the tension change curve of each ligament as a function of angle throughout the entire range of motion of the joint.

6. A preoperative orthopaedic surgery planning method according to claim 5, characterized in that, The specific soft tissue balance optimization scheme includes: Analyze the tension change curve to identify non-smooth transition points where tension suddenly increases or decreases at a specific angle of activity; An optimization objective function is established with the minor adjustment parameters of the implant as variables. The objective function aims to minimize the variance of the rate of change of tension of all ligaments throughout the entire range of motion. An iterative optimization algorithm is used to perform minute translations and rotations in three-dimensional space based on the implant position and orientation determined by the preliminary placement plan; After each adjustment, a new ligament tension change curve is recalculated and generated, and the value of the optimization objective function is calculated. When the value of the optimization objective function reaches the preset convergence criterion, the iteration stops, and the implant adjustment parameters at this time are the optimal parameters for soft tissue balance. The optimal parameters for soft tissue balance are updated in the preliminary placement scheme, overriding the original position and orientation parameters, to form an optimized soft tissue balance scheme.

7. A preoperative planning method for bone joint arthroplasty according to claim 6, characterized in that, The timing and location of the predicted abnormal loads specifically include: Establish a multibody dynamics model that includes the mechanics of bones, implants, ligaments, and simplified muscles; Define the driving conditions for standard gait cycle motion and daily activity movements for the multibody dynamics model; Run dynamic simulations to calculate the magnitude and direction of dynamic loads on the joint contact interface during the complete gait cycle and flexion-extension movements; Monitor the dynamic load, detect events where the load vector direction suddenly deviates from the normal physiological range, and record the simulation time point of the event and its corresponding position on the contact interface; Analyze all detected events and statistically determine the phases and contact interface regions where abnormal load events occur in clusters; The phase distribution map and thermal map of the contact interface location of the abnormal load event are output as prediction information for the abnormal load.

8. A preoperative planning method for bone joint arthroplasty according to claim 7, characterized in that, The specific installation parameters of the reverse correction implant include: The heat map of the contact interface location of the abnormal load event is mapped back to the implant model surface in the soft tissue balance optimization scheme. Analyze the relative positional relationship between the high-load area and the edge of the implant design features in the heat map to determine whether the abnormal load is caused by the protrusion of the implant edge or improper placement. If the problem is determined to be caused by improper implant position or orientation, the adjustment direction of the implant will be recalculated in the parameter space, guided by reducing the load value of the high-load area. While maintaining a smooth change in ligament tension, the orientation of the implant is adjusted to a limited extent to generate multiple modification candidate schemes; For each modified candidate scheme, re-perform the dynamic simulation and calculate the degree of reduction in its abnormal load events; The candidate modification scheme that maximizes the reduction of abnormal load events is selected, and its installation parameters are determined as the final modification parameters.

9. A preoperative planning method for bone joint arthroplasty according to claim 8, characterized in that, The process of generating the final surgical execution plan also includes: The final correction parameters are integrated into the soft tissue balance optimization scheme to form unified implant placement data; Based on the unified implant placement data, calculate the digital design parameters of the bone cutting guide required during the surgery; The digital design parameters of the bone cutting guide are registered with the three-dimensional digital model of the patient's bones to generate guide installation positioning instructions. By integrating the unified implant placement data, the digital design parameters of the bone cutting guide, and the guide installation and positioning instructions, a final surgical execution plan containing executable instructions is generated.

10. A preoperative orthopaedic joint replacement planning system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterised in that, When the processor executes the computer program, it implements the steps of the preoperative planning method for osteoarthritis as described in any one of claims 1 to 9.

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