Osteotomy path adjusting system in robot-assisted knee arthroplasty

Abstract

The invention provides an osteotomy path adjusting system in robot-assisted knee arthroplasty, and belongs to the technical field of knee joint detection. The system comprises a multi-modal data acquisition unit which comprises a knee joint pressure measuring gasket, an optical acquisition module and a kinematics data acquisition module, the knee joint pressure measuring gasket is used for acquiring knee joint pressure signals in an operation, and the optical acquisition module is used for acquiring relative poses of a femur and a tibia; the kinematics data acquisition module is used for acquiring kinematics data of the mechanical arm; the main control calculation unit comprises a data fusion module and a dynamic compensation module, and the data fusion module is used for constructing a synchronous spatio-temporal data set; and the dynamic compensation module is used for outputting a compensation instruction to the mechanical arm to adjust an osteotomy path based on the synchronous spatio-temporal data set, the preoperative predicted pressure prediction matrix and the soft tissue stiffness matrix. The pressure change in the operation can be responded in real time, the operation precision of the knee arthroplasty is improved, and the dependence on the experience of doctors can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of knee joint detection, and in particular to an osteotomy path adjustment system in robot-assisted knee replacement surgery. Background Art

[0002] In total knee arthroplasty, soft tissue balance is a key factor in determining postoperative joint stability, prosthesis life, and patient functional recovery. In traditional surgical procedures, surgeons rely on their experience to subjectively assess the tension of the medial and lateral soft tissues using manual retractors, gap gauges, or intraoperative trial molds, and visually determine the osteotomy angle and thickness. This "touch + visual inspection" model has been the clinical gold standard for decades, but it is extremely dependent on operator experience, and differences between individuals directly translate into significant differences in postoperative force alignment, pain, and revision risk.

[0003] To reduce subjective errors, robotic navigation and sensor-assisted technologies have emerged in recent years. Robotic systems use a robotic arm to guide osteotomy. After the osteotomy is complete, a single pressure sensor or simple force gauge is inserted into the joint space as a disposable trial spacer. A single pressure reading is taken in a fixed flexion position, providing the surgeon with empirical guidance during the procedure. These methods have significantly improved osteotomy accuracy and, for the first time, introduced the concept of "force" into the surgical decision-making process in a quantitative manner.

[0004] However, existing technologies still remain at the level of "relying on the doctor's empirical judgment", and there is an urgent need to propose a complete solution that can automatically adjust the osteotomy path. Summary of the Invention

[0005] The purpose of this application is to provide an osteotomy path adjustment system in robot-assisted knee replacement surgery to solve the above problems.

[0006] To achieve the above objectives, in a first aspect, the present application proposes an osteotomy path adjustment system in robot-assisted knee replacement surgery, the system comprising: A multimodal data acquisition unit, comprising a knee joint pressure measurement pad, an optical acquisition module, and a kinematic data acquisition module, wherein the knee joint pressure measurement pad is used to collect intraoperative knee joint pressure signals, the optical acquisition module is used to collect the relative position of the femur and tibia, and the kinematic data acquisition module is used to collect kinematic data of the robotic arm; A main control computing unit, the main control computing unit includes a data fusion module and a dynamic compensation module, wherein the data fusion module is used to construct a synchronized spatiotemporal data set based on the intraoperative knee joint pressure signal, the relative position of the femur and tibia, and the kinematic data of the robotic arm; the dynamic compensation module is used to output compensation instructions to the robotic arm based on the synchronized spatiotemporal data set, the preoperatively predicted pressure prediction matrix and the soft tissue stiffness matrix to adjust the osteotomy path.

[0007] In some embodiments, the dynamic compensation module is configured to: extracting a pressure distribution matrix based on the synchronized spatiotemporal dataset; Comparing the pressure distribution matrix with the pressure prediction matrix to calculate a pressure deviation metric; When the pressure deviation metric exceeds a preset threshold, a compensation instruction is output to the robotic arm based on the pressure deviation metric and the soft tissue stiffness matrix to adjust the osteotomy path.

[0008] In some embodiments, outputting a compensation instruction to the robotic arm based on the pressure deviation metric and the soft tissue stiffness matrix includes: Modeling the pressure deviation metric as a linearized model of a soft tissue stiffness matrix and a posture adjustment amount of the end effector of the robotic arm, wherein the soft tissue stiffness matrix is ​​used to describe the mapping relationship between the displacement and force of each region of the soft tissue after the soft tissue is subjected to force; Solving the linearized model using the least squares method to obtain a target value of the posture adjustment amount of the end effector; Based on the target value and the kinematic data of the robotic arm, compensation parameters of each joint of the robotic arm are calculated, and compensation instructions are output to the robotic arm based on the compensation parameters of each joint of the robotic arm. In some embodiments, modeling the pressure deviation metric as a linearized model of a soft tissue stiffness matrix and an end effector posture adjustment amount includes: Establishing a first functional model based on the pressure deviation metric and the soft tissue stiffness matrix to describe the functional relationship between the pressure deviation metric and the soft tissue displacement; and A second function model is constructed based on the posture adjustment of the end effector and the soft tissue displacement to describe the functional relationship between the bone displacement and the soft tissue displacement affected by the posture adjustment of the end effector; A linearization model is constructed based on the first function model and the second function model. In some embodiments, the intraoperative knee joint pressure includes intraoperative medial pressure and intraoperative lateral pressure, and the main control computing unit further includes: a quantitative assessment module, the quantitative assessment module being configured to calculate an intraoperative medial-lateral pressure ratio of the knee joint based on the intraoperative medial pressure and the intraoperative lateral pressure, so as to determine whether the soft tissue is unbalanced according to the intraoperative medial-lateral pressure ratio of the knee joint and a preset balance range; The dynamic compensation module is used to output compensation instructions to the robotic arm to adjust the osteotomy path based on the synchronized spatiotemporal data set, the preoperatively predicted pressure prediction matrix and the soft tissue stiffness matrix when determining soft tissue imbalance.

[0009] In some embodiments, the knee joint pressure measurement pad is further used to collect postoperative knee joint pressure signals during knee flexion and extension, and the quantitative evaluation module is further used to: generating a pressure distribution change curve during knee joint flexion and extension based on the postoperative knee joint pressure signal; Based on a preset time segment, the pressure distribution change curve is divided into multiple pressure distribution vectors, and the relative change rate of knee joint pressure is obtained by calculating the Euclidean distance between each adjacent pressure distribution vector and performing normalization processing; The flexion-extension continuity coefficient is obtained by calculating the degree of discreteness of the relative change rate of the knee joint pressure. The flexion-extension continuity coefficient is used to reflect the smoothness of the motion trajectory during the flexion and extension of the knee joint to determine whether the motion trajectory is abnormal. In some embodiments, the main control computing unit further includes a preoperative prediction module, which is configured to: Acquiring three-dimensional image data of a target knee joint and segmenting key anatomical structures in the three-dimensional image data; Importing the segmented three-dimensional image data into finite element analysis software to generate a finite element model, wherein each key anatomical structure in the finite element model has different material properties; generating simulation results under various preset osteotomy plans based on the finite element model, and selecting a target osteotomy plan from the preset osteotomy plans based on the simulation results; The pressure prediction matrix, the soft tissue stiffness matrix and the initial osteotomy path corresponding to the target osteotomy scheme are output to guide the end effector to complete the first osteotomy according to the initial osteotomy path.

[0010] In some embodiments, the preoperative prediction module is further configured to: Acquire the synchronized spatiotemporal dataset collected by the data fusion module, and update the finite element model based on the synchronized spatiotemporal dataset; Based on the updated finite element model, the pressure adjustment matrix under the adjusted osteotomy path is output.

[0011] In some embodiments, before importing the segmented three-dimensional image data into the finite element analysis software to generate the finite element model, the method further includes: Acquiring user information of the target knee joint, the user information including race information, gender information, and age information; Obtaining a ligament stiffness reference value corresponding to the user information; The step of importing the segmented three-dimensional image data into finite element analysis software to generate a finite element model includes: The segmented three-dimensional image data and the ligament stiffness reference value are imported into finite element analysis software to generate a finite element model. In some embodiments, the optical acquisition module includes infrared marker balls provided on the femur and tibia, and a camera array, wherein the camera array acquires the relative posture of the femur and tibia by tracking the infrared marker balls.

[0012] Compared with the prior art, the advantages of this application include: This application uses a closed-loop coupling between a modular multimodal data acquisition unit and a main control computing unit to fuse three types of heterogeneous signals, namely, intraoperative knee joint pressure, bony posture, and kinematic data of the robotic arm, into a synchronized spatiotemporal data set in real time. This data set not only fully depicts the mechanical-geometric-motion coupling state of the knee joint during surgery, but also directly serves as the input of the dynamic compensation module to compare the deviations with the preoperatively predicted pressure prediction matrix and soft tissue stiffness matrix, thereby outputting compensation instructions to the robotic arm. As a result, the system can respond to changes in knee joint pressure during surgery in real time, automatically adjust the osteotomy path, significantly improve surgical accuracy, and reduce dependence on the doctor's experience, making the surgery more standardized and improving the repeatability and reliability of the surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope of the present application.

[0014] Figure 1 A schematic diagram of functional units of an osteotomy path adjustment system in robot-assisted knee replacement surgery according to one embodiment; Figure 2 A schematic diagram of an execution process of an osteotomy path adjustment system in robot-assisted knee replacement surgery according to an embodiment; Figure 3 A schematic diagram of the execution process of the osteotomy path adjustment system in robot-assisted knee replacement surgery according to another embodiment; Figure 4 Schematic diagram of the functional modules of the main control computing unit in one embodiment.

[0015] Explanation of the accompanying figures: 100. Osteotomy path adjustment system in robot-assisted knee arthroplasty; 1. Multimodal data acquisition unit; 11. Knee joint pressure measurement gasket; 12. Optical acquisition module; 13. Kinematic data acquisition module; 2. Main control computing unit; 21. Data fusion module; 22. Dynamic compensation module; 23. Quantitative evaluation module; 24. Preoperative prediction module. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0017] All terms (including technical and scientific terms) used in this application have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0018] For example, the terms "first," "second," etc. used in this application may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish a first element from another element.

[0019] For example, the terms "include", "comprising", etc. used in this application indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0020] As mentioned above, in recent years, robotic navigation and sensor-assisted total knee replacement (RAK) technologies have emerged. The robotic system guides osteotomy using a robotic arm. After the osteotomy is complete, a single pressure sensor or simple force gauge is inserted into the joint space as a disposable trial gasket. A single pressure value is read in a fixed flexion position for the surgeon's empirical judgment during the procedure. These methods have improved osteotomy accuracy to a certain extent and, for the first time, quantitatively introduced the concept of "force" into the surgical decision-making process. However, existing technologies remain at the level of "relying on the surgeon's empirical judgment," and a complete solution that can automatically adjust the osteotomy path is urgently needed. To this end, this application proposes a system for adjusting the osteotomy path during knee replacement surgery that can respond to intraoperative pressure changes in real time and automatically adjust the osteotomy path, thereby improving surgical accuracy. Furthermore, it can reduce reliance on the surgeon's experience, making the procedure more standardized and improving the surgeon's repeatability and reliability.

[0021] like Figure 1 and Figure 2As shown, an embodiment of the present application provides an osteotomy path adjustment system 100 in robot-assisted knee replacement surgery. The system 100 includes: a multimodal data acquisition unit 1 and a main control computing unit 2.

[0022] The multimodal data acquisition unit 1 in this embodiment includes a knee joint pressure measurement pad 11, an optical acquisition module 12 and a kinematic data acquisition module 13. It is a hardware-software collaborative unit that synchronously acquires three types of heterogeneous data, namely "pressure-posture-motion", in real time during surgery. It can provide a high-precision, low-latency input source for dynamic compensation of the osteotomy path.

[0023] The knee joint pressure measurement pad 11 is a medical-grade pad inserted between the tibial osteotomy surface and the femoral condyle during surgery to collect real-time knee joint pressure signals. Specifically, the knee joint pressure measurement pad is embedded with a flexible pressure film sensor array that converts the "contact pressure" of the joint surface into a quantifiable electrical signal, namely the knee joint pressure signal. This flexible pressure film sensor array can be a two-dimensional matrix (e.g., a 100×100 dot matrix) composed of piezoresistive or capacitive microsensor units, with a sampling frequency of 1000Hz, and possesses high sensitivity and the ability to conform to curved surfaces.

[0024] The optical acquisition module 12 is a module for acquiring the relative position of the femur and tibia. In some embodiments, the optical acquisition module may include a camera array provided on the femur and tibia.

[0025] For example, three or more infrared cameras can be arranged in a ring. Infrared marker balls are fixed to the cortex of the distal femur and proximal tibia to collect six degrees of freedom (3D coordinates + 3D rotation), thereby digitizing the relative geometric relationship between bones in real time.

[0026] The kinematic data acquisition module 13 refers to an electronic-mechanical interface for acquiring kinematic data such as the joint angle, angular velocity, end torque, and position of the end effector of the robot arm.

[0027] For example, if a UR5 robot is used, the six joint angles can be read at 1kHz via the RTDE (Real-Time Data Exchange) interface. If a KUKA LBR Med is used, the joint angles and six-dimensional end-user forces / torques can be read via the FRI (Fast Robot Interface).

[0028] The main control computing unit 2 in this embodiment is used to process and fuse the three types of heterogeneous information (intraoperative knee joint pressure signal, relative posture of femur and tibia, and kinematic data of the robotic arm) sent by the multimodal data acquisition unit 1 in real time and generate compensation instructions executable by the robotic arm.

[0029] Specifically, the main control computing unit 2 includes a data fusion module 21 and a dynamic compensation module 22. The data fusion module 21 is used to construct a synchronized spatiotemporal dataset based on the intraoperative knee joint pressure signal, the relative position of the femur and tibia, and the kinematic data of the robotic arm. The dynamic compensation module 22 is used to output compensation instructions to the robotic arm based on the synchronized spatiotemporal dataset, the preoperatively predicted pressure prediction matrix, and the soft tissue stiffness matrix to adjust the osteotomy path.

[0030] It should be understood that the data fusion module 21 is capable of aligning three data of different frequencies, different coordinate systems, and different physical dimensions to the same spatiotemporal reference, and outputting a unified synchronized spatiotemporal data set. In some embodiments, the data fusion module performs time alignment based on the intraoperative knee joint pressure signal, the relative position of the femur and tibia, and the kinematic data of the robotic arm, and uses the optical tracking coordinate system where the relative position of the femur and tibia is located as a reference. Through the homogeneous transformation matrix obtained by preoperative calibration, the pressure distribution matrix corresponding to the intraoperative knee joint pressure signal and the position of the end effector of the robotic arm are mapped to the optical tracking coordinate system in real time, thereby achieving spatial registration of the intraoperative knee joint pressure signal, the relative position of the femur and tibia, and the kinematic data of the robotic arm to obtain an initial data set; the Kalman filter algorithm is used to filter the initial data set after completing the time alignment and spatial registration to obtain a synchronized spatiotemporal data set. By constructing a synchronized spatiotemporal data set, measurement noise, time drift, and coordinate system differences can be eliminated, providing a clean, synchronized, and directly operable input for the dynamic compensation module.

[0031] The dynamic compensation module 22 is used to calculate the deviation between the synchronized spatiotemporal dataset and the preoperatively predicted pressure prediction matrix P_pre and soft tissue stiffness matrix K, and to solve the optimal posture adjustment of the robotic arm end effector in real time. In some embodiments, a finite element model can be established preoperatively using CT (Computed Tomography) / MRI (Magnetic Resonance Imaging) to derive the pressure prediction matrix P_pre and soft tissue stiffness matrix K for the initial osteotomy path.

[0032] As an optional implementation method for triggering a compensation instruction, a pressure distribution matrix is ​​extracted based on the synchronized spatiotemporal data set; a pressure deviation metric is calculated by comparing the pressure distribution matrix with the pressure prediction matrix; when the pressure deviation metric exceeds a preset threshold, a compensation instruction is output to the robotic arm based on the pressure deviation metric and the soft tissue stiffness matrix to adjust the osteotomy path.

[0033] Specifically, the pressure distribution matrix is ​​a two-dimensional matrix used to represent the pressure distribution of the knee joint at different positions and time points. Each element in the matrix corresponds to the pressure value of a specific area of ​​the knee joint. The dynamic compensation module filters out pressure-related data (intraoperative knee joint pressure signals) from the synchronized spatiotemporal dataset, and organizes the intraoperative knee joint pressure signals into a two-dimensional matrix, namely the pressure distribution matrix, according to the anatomical structure of the knee joint and the layout of the flexible pressure film sensor array. The pressure distribution matrix can intuitively display the spatial distribution characteristics of the knee joint pressure, providing a basis for subsequent comparison with the pressure prediction matrix. For example, if the flexible pressure film sensor array is a 10×10 grid, a 10×10 pressure distribution matrix can be formed, and each element corresponds to the pressure value of a flexible pressure film sensor.

[0034] The pressure prediction matrix refers to a matrix formed by predicting the pressure distribution of the knee joint under the ideal initial osteotomy path based on the preoperative finite element model. The pressure deviation metric is used to quantify the degree of difference between the actual pressure distribution and the predicted pressure distribution, and can be obtained by calculating the difference index (such as Euclidean distance, correlation coefficient, etc.) between the pressure distribution matrix and the pressure prediction matrix. Exemplarily, the dynamic compensation module 22 calculates the sum of the squares of the differences between the corresponding elements of the pressure distribution matrix and the pressure prediction matrix, and then takes the square root to obtain the Euclidean distance as the pressure deviation metric. The pressure deviation metric can intuitively reflect the degree of deviation of the knee joint pressure distribution under the current osteotomy path from the ideal situation, and provide a basis for determining whether the osteotomy path needs to be adjusted.

[0035] The preset threshold is an upper limit for the pressure deviation metric, determined based on clinical experience and experimental research, and is used to determine whether osteotomy path adjustment is necessary. When the calculated pressure deviation metric exceeds the preset threshold, the dynamic compensation module 22, combined with the soft tissue stiffness matrix, calculates the required compensation adjustment for the robotic arm, generates corresponding compensation instructions, and outputs them to the robotic arm, enabling automatic adjustment of the osteotomy path.

[0036] In some embodiments, the pressure deviation measurement can be modeled as a linearized model of the soft tissue stiffness matrix and the posture adjustment amount of the end effector of the robotic arm, wherein the soft tissue stiffness matrix is ​​used to describe the mapping relationship between the displacement of each region and the force after the soft tissue is subjected to force; the linearized model is solved using the least squares method to obtain the target value of the posture adjustment amount of the end effector; based on the target value and the kinematic parameters of the robotic arm, the compensation parameters of each joint of the robotic arm are calculated, and compensation instructions are output to the robotic arm based on the compensation parameters of each joint of the robotic arm.

[0037] Specifically, a linearized model is a mathematical model that approximates the nonlinear relationship between the pressure deviation metric and the posture adjustment amount as a linear relationship, facilitating solution and analysis. A first function model can be established based on the pressure deviation metric and the soft tissue stiffness matrix to describe the functional relationship between the pressure deviation metric and the soft tissue displacement; and a second function model can be constructed based on the end effector's posture adjustment amount and the soft tissue displacement to describe the functional relationship between the skeletal displacement and the soft tissue displacement affected by the end effector's posture adjustment amount. A linearized model is constructed based on the first and second function models.

[0038] It is important to understand that the soft tissue stiffness matrix describes the relationship between the displacement and force of each region of the soft tissue after it is subjected to stress, reflecting the mechanical properties of the soft tissue. Therefore, under the condition of small deformation of the soft tissue, it can be assumed that the displacement of the soft tissue after being subjected to stress is proportional to the pressure and satisfies Hooke's law. Based on the soft tissue stiffness matrix K, the pressure deviation measurement value ΔP and the soft tissue displacement , this relationship can be expressed in matrix form as: In this embodiment, the first function model constructed with the pressure deviation measurement as input and the soft tissue displacement as output can be expressed as: .in, is the inverse matrix of the soft tissue stiffness matrix K, which represents the displacement response of soft tissue under unit force. By constructing a first function model, we reveal how the pressure deviation metric leads to changes in soft tissue displacement.

[0039] The posture adjustment of the end effector refers to the adjustment of the position and posture of the end effector of the robotic arm, which is used to change the position and direction of the osteotomy. Bone displacement refers to the displacement of the bone caused by the posture adjustment of the end effector during the osteotomy process. Through kinematic analysis and finite element model, a second function model can be pre-constructed to describe the relationship between the posture adjustment of the end effector and the soft tissue displacement. For example, a rigid body kinematic model can be used to describe the relationship between bone displacement and the posture adjustment of the end effector, and a flexible body mechanics model can be used to describe the relationship between soft tissue displacement and bone displacement. Based on the rigid body kinematic model and the flexible body mechanics model, a second function model is constructed to predict the impact of the posture adjustment of the end effector on bones and soft tissues.

[0040] For example, assuming that the position adjustment of the end effector is Δx, according to the rigid body kinematics model, the bone displacement , where J is the Jacobian matrix. In the rigid body kinematics model, the Jacobian matrix is ​​used to convert the pose adjustment of the end effector into bone displacement. It reflects how the movement of the end effector of the manipulator in different degrees of freedom affects the displacement of the bones. For example, for a 6-degree-of-freedom manipulator, the Jacobian matrix is ​​a 6×n matrix (n is the number of joints), and each column corresponds to the effect of the movement of a joint on the velocity of the end effector. At the same time, according to the flexible body mechanics model, the relationship between the soft tissue displacement Δd and the bone displacement Δs can be expressed as , where H is the flexibility matrix that describes the effect of bone displacement on soft tissue displacement. In the flexible body mechanics model, the flexibility matrix H describes the effect of bone displacement on soft tissue displacement. It reflects the deformation characteristics of soft tissue under the action of bone displacement. The flexibility matrix can be obtained through experimental measurement or finite element analysis, reflecting the mechanical properties and geometric structure of the soft tissue. For example, for the soft tissue around the knee joint, the flexibility matrix can be obtained by simulating and analyzing the finite element model of the knee joint, which takes into account the material properties of the soft tissue such as elastic modulus and Poisson's ratio, as well as the geometric shape and boundary conditions of the soft tissue. Therefore, the second function model can be expressed as: .

[0041] Based on the first function model obtained , which can be deduced to be , where ΔP is the pressure deviation measure, K is the soft tissue stiffness matrix, and Δd is the soft tissue displacement. The second function model is , where Δx is the position adjustment of the end effector, J is the Jacobian matrix, and H is the flexibility matrix that describes the effect of bone displacement on soft tissue displacement. Replacing Δd in the first function model with the expression in the second function model, we can get The final linearized model can be expressed as: , where A = K × H × J is a comprehensive coefficient matrix that combines information from the soft tissue stiffness matrix K, the flexibility matrix H, and the Jacobian matrix J. Because the linearized model integrates the mechanical properties of the soft tissue and the kinematic characteristics of the robotic arm, it reflects how the end-effector posture adjustment affects the pressure deviation measurement value and directly links the pressure deviation measurement value ΔP with the end-effector posture adjustment value Δx, greatly simplifying the real-time adjustment process and improving the efficiency and accuracy of the surgery.

[0042] Through matrix operations, we can get ,in, represents the transposed matrix of A. Due to the existence of measurement noise and model error, Δx usually has no exact solution. Therefore, the optimal estimate (target value) of Δx can be obtained by the least squares method, so that the error Minimum. Furthermore, based on the target value and the kinematic data of the robotic arm, the kinematic model of the robotic arm is reversely solved to obtain the compensation parameters of each joint of the robotic arm, generate corresponding control instructions, and send them to the control module of the robotic arm to drive the robotic arm to make corresponding adjustments. Among them, the kinematic model of the robotic arm describes the relationship between the parameters of each joint and the posture of the end effector. By accurately calculating the compensation parameters of each joint and outputting the corresponding compensation instructions, the robotic arm can be controlled to accurately adjust the posture of the end effector, thereby achieving optimized adjustment of the osteotomy path and improving the accuracy and effect of the operation.

[0043] As another optional implementation method of triggering the compensation instruction, Figure 3 As shown, the intraoperative knee joint pressure includes the intraoperative medial pressure and intraoperative lateral pressure The main control computing unit 2 further includes a quantitative evaluation module 23, which is used to evaluate the inner pressure of the patient based on the inner pressure of the patient. and the intraoperative lateral pressure The intraoperative knee joint pressure ratio R was calculated. , to determine soft tissue imbalance based on the intraoperative medial-lateral knee pressure ratio and a preset balance range. A normal knee maintains dynamic balance between medial and lateral pressure during flexion and extension (R ranges from approximately 0.8 to 1.2 in healthy individuals). When R < 0.8, it indicates excessive lateral pressure, possibly due to lateral soft tissue tension or medial collateral ligament laxity. When R > 1.2, it indicates medial pressure overload, associated with conditions such as osteoarthritis and meniscus injury.

[0044] For example, suppose at a certain moment, the knee joint pressure measurement pad measures an intraoperative medial pressure of 280 kPa and an intraoperative lateral pressure of 180 kPa. The quantitative assessment module calculates an intraoperative medial-to-lateral knee joint pressure ratio of 1.56. If the preset balance range is 0.8-1.2, this ratio exceeds the balance range, and soft tissue imbalance is determined.

[0045] When soft tissue imbalance is determined, the dynamic compensation module 22 is configured to output compensation instructions to the robotic arm based on the synchronized spatiotemporal dataset, the preoperatively predicted pressure prediction matrix, and the soft tissue stiffness matrix to adjust the osteotomy path. The specific calculation process can be found in the above description of the specific implementation of the dynamic compensation module 22 and will not be further described in this embodiment.

[0046] In some embodiments, the knee joint pressure measurement pad 11 is further used to collect a postoperative knee joint pressure signal during knee flexion and extension, and the quantitative evaluation module 23 is further used to generate a pressure distribution change curve during knee flexion and extension based on the postoperative knee joint pressure signal. The curve with time as the horizontal axis and pressure as the vertical axis is used to show the dynamic changes of pressure during knee flexion and extension. Based on the preset time segment (such as every 100ms as a segment), the pressure distribution change curve is divided into multiple pressure distribution vectors. , where m is the number of unit points in the flexible pressure film sensor array (e.g., a 100×100 array corresponds to m = 10,000 pressure values). By calculating the Euclidean distance between each adjacent pressure distribution vector and performing normalization, the relative change rate of knee joint pressure is obtained. For example, assuming the pressure vector at time t is , time t+1 is , then the Euclidean distance between the two reflects the dynamic change amplitude of the pressure distribution. By normalizing the Euclidean distance between the two, the relative change rate is obtained. .

[0047] Then the standard deviation of the relative change rate of knee joint pressure over a period of time can be calculated separately and average By calculating the discrete degree of the relative change rate of the knee joint pressure, the flexion-extension continuity coefficient C can be obtained. The flexion-extension continuity coefficient C is used to reflect the smoothness of the motion trajectory during knee flexion and extension to determine whether the motion trajectory is abnormal. The pressure distribution of a healthy knee joint during flexion and extension should be smooth and continuous (C is close to 1). If C < 0.7, it indicates that the motion trajectory is abnormal, and there may be problems such as joint jamming and synovial hyperplasia. For example, if the patient's R drops from 1.6 to 1.1 and C increases from 0.6 to 0.9 one month after surgery, it indicates that the pressure distribution has returned to normal and the recovery is progressing well.

[0048] In the knee arthroplasty osteotomy path adjustment system proposed in the present embodiment, through the closed-loop coupling of the modular multimodal data acquisition unit 1 and the main control computing unit 2, three heterogeneous signals—intraoperative knee joint pressure, bony posture, and robotic arm kinematic data—are fused in real time into a synchronized spatiotemporal dataset. This dataset not only fully depicts the mechanical-geometric-motion coupling state of the knee joint during surgery, but also directly serves as the input of the dynamic compensation module 22, comparing the deviations with the preoperatively predicted pressure prediction matrix and soft tissue stiffness matrix, thereby outputting compensation instructions to the robotic arm. As a result, the system can respond to changes in knee joint pressure in real time and automatically adjust the osteotomy path, significantly improving surgical accuracy. It can also reduce reliance on the doctor's experience, making the surgery more standardized and improving the repeatability and reliability of the surgery. In addition, through the quantitative evaluation module 23, the system can comprehensively evaluate the pressure distribution and smoothness of the knee joint motion trajectory after surgery, providing doctors with quantitative indicators to assist in postoperative rehabilitation assessment and diagnosis.

[0049] In one embodiment, Figure 4 As shown, the main control computing unit 2 also includes a preoperative prediction module 24, which is used to: obtain three-dimensional image data of the target knee joint and segment the key anatomical structures in the three-dimensional image data; import the segmented three-dimensional image data into finite element analysis software to generate a finite element model, wherein each key anatomical structure in the finite element model has different material properties; generate simulation results under each preset osteotomy scheme based on the finite element model, and select a target osteotomy scheme from the preset osteotomy schemes based on the simulation results; output the pressure prediction matrix, soft tissue stiffness matrix and initial osteotomy path corresponding to the target osteotomy scheme to guide the end effector to complete the first osteotomy according to the initial osteotomy path.

[0050] Specifically, 3D imaging data refers to 3D image data of the knee joint acquired using medical imaging technologies such as CT and MRI, and includes information on structures such as bones and soft tissue. Key anatomical structures include the femur, tibia, ligaments, and meniscus. In some embodiments, medical imaging software can be used to import CT or MRI data, extract key anatomical structures of the knee joint using algorithms such as threshold segmentation and region growing, and generate a 3D model.

[0051] Finite element analysis software, such as ANSYS and Abaqus, is used to build and solve finite element models. Finite element models discretize complex geometric structures into a finite number of elements and nodes for numerical simulation and analysis. Material properties describe the mechanical properties of materials, such as elastic modulus, Poisson's ratio, and density. Different anatomical structures have different material properties. For example, bone has a higher elastic modulus, while soft tissue has a lower one. For example, segmented 3D image data is imported into finite element analysis software. The software's modeling tools are used to discretize key anatomical structures into a finite element mesh (element size ≤ 2 mm) to ensure accurate simulation of mechanical transmission at the soft tissue-bone interface. Material properties are assigned based on the type of key anatomical structure. For example, the femur and tibia are assigned material properties of 18 GPa and a Poisson's ratio of 0.3, while the soft tissue model uses a hyperelastic Neo-Hookean model (material parameters can be adjusted based on patient age and weight).

[0052] The preset osteotomy plan refers to a variety of osteotomy plans that are pre-set based on clinical experience and surgical specifications, including parameters such as osteotomy angle, depth, and position. The simulation results refer to the simulation of the mechanical behavior of the knee joint under different osteotomy plans through finite element analysis, and the results such as pressure distribution and displacement are obtained. The target osteotomy plan refers to the optimal osteotomy plan selected from multiple preset osteotomy plans based on the simulation results. It usually refers to the plan that can make the mechanical environment of the knee joint after surgery closest to the normal state. For example, in the finite element analysis software, each preset osteotomy plan is simulated and analyzed, and the pressure distribution, displacement and other results of the knee joint under different osteotomy plans are calculated and recorded. By comparing the simulation results, the osteotomy plan with the most uniform pressure distribution and the best mechanical stability is selected as the target osteotomy plan.

[0053] The pressure prediction matrix refers to a matrix that is generated based on the finite element simulation results of the target osteotomy scheme and describes the pressure distribution of the knee joint after osteotomy. The soft tissue stiffness matrix refers to a matrix obtained through finite element analysis that describes the mechanical properties of the soft tissue under the target osteotomy scheme and is used for subsequent dynamic compensation calculations. The initial osteotomy path refers to a path generated based on the target osteotomy scheme and used to guide the robotic arm to perform the first osteotomy operation. Exemplarily, the pressure prediction matrix and the soft tissue stiffness matrix are generated by organizing and converting the finite element simulation results. According to the geometric parameters of the target osteotomy scheme, the initial osteotomy path of the end effector of the robotic arm is planned, including the starting point, end point and path shape of the osteotomy, to guide the robotic arm to complete the first osteotomy operation.

[0054] Through the preoperative prediction module 24, the system can accurately model and analyze the three-dimensional structure of the knee joint before surgery, predict the effects of different osteotomy plans, provide scientific guidance for surgery, and improve the success rate and effect of the surgery.

[0055] In some embodiments, the preoperative prediction module 24 is further used to: obtain the synchronized spatiotemporal dataset collected by the data fusion module 21, and update the finite element model based on the synchronized spatiotemporal dataset; and output the pressure adjustment matrix under the adjusted osteotomy path based on the updated finite element model.

[0056] Specifically, the preoperative prediction module 24 compares the data from the synchronized spatiotemporal dataset actually measured during surgery with the predicted values ​​from the finite element model. Based on the discrepancies, data assimilation techniques or inverse solution algorithms are used to adjust parameters in the finite element model (such as material properties and boundary conditions) to better align the model with reality. By updating the finite element model in real time, it can more accurately reflect the mechanical state of the knee joint during surgery, improving the accuracy and reliability of subsequent dynamic compensation.

[0057] The pressure adjustment matrix is ​​a matrix that predicts the knee joint pressure distribution under the adjusted osteotomy path based on the updated finite element model. It reflects the changes in pressure distribution after the osteotomy path adjustment and can be used to guide subsequent surgical procedures, improving surgical accuracy and effectiveness.

[0058] In some embodiments, before importing the segmented three-dimensional image data into the finite element analysis software to generate the finite element model, it also includes: obtaining user information of the target knee joint, the user information including race information, gender information and age information; obtaining a ligament stiffness reference value corresponding to the user information; importing the segmented three-dimensional image data into the finite element analysis software to generate the finite element model includes: importing the segmented three-dimensional image data and the ligament stiffness reference value into the finite element analysis software to generate the finite element model.

[0059] Specifically, the target knee joint refers to the knee joint of the patient undergoing surgery. User information refers to personal information related to the patient, including race, gender, age, weight, height, activity level, etc. This information is important for understanding the patient's physiological characteristics and disease conditions. Among them, racial information refers to the patient's racial background. Different races have differences in bone structure, muscle distribution, etc. Gender information refers to the patient's gender. Men and women differ in bone size, shape, and muscle strength. Age information refers to the patient's age. Age affects physiological characteristics such as bone density and ligament elasticity. Before surgery, the patient's user information can be obtained through the hospital's information system (such as the HIS system), or directly extracted through the electronic medical record system, or manually entered by medical staff. By providing the patient's basic physiological characteristic information for the subsequent establishment of a personalized finite element model, it can help to more accurately simulate the patient's knee joint mechanical behavior.

[0060] Ligament stiffness benchmark values ​​refer to the average values ​​or reference ranges for ligament stiffness across different populations (categorized by race, gender, age, etc.), derived from extensive experimental data and clinical studies. Ligament stiffness reflects the ligament's ability to resist deformation under load. Based on the acquired user information, the corresponding ligament stiffness benchmark value can be searched from a pre-established database. This database contains experimental data on ligament stiffness for populations of different races, genders, and age groups. For example, based on a patient's race (Asian), gender (male), and age (65 years), the corresponding knee ligament stiffness benchmark value of 15 N / mm is found in the database. This value will be used to set the mechanical properties of the ligaments in the subsequent finite element model.

[0061] The osteotomy path adjustment system for robot-assisted knee arthroplasty proposed in the embodiments of this application, firstly, by acquiring three-dimensional imaging data of the target knee joint and accurately segmenting key anatomical structures, can provide highly accurate geometric structure information for the establishment of a finite element model, making the preoperative osteotomy plan more consistent with the individual patient's anatomical characteristics, thereby improving the accuracy of the surgery. At the same time, based on the finite element model, simulation analysis of multiple preset osteotomy plans can be performed to screen out the best target osteotomy plan from multiple plans, which helps to optimize the osteotomy path, make the mechanical environment of the knee joint closer to the normal state after surgery, and improve the surgical effect.

[0062] Secondly, by acquiring synchronized spatiotemporal data sets and updating the finite element model during surgery, the finite element model can more accurately reflect the actual mechanical state of the knee joint during surgery, improve the real-time and adaptability of the model, and provide a more reliable basis for subsequent dynamic compensation.

[0063] Third, by obtaining user information (race, gender, age, etc.) of the target knee joint and combining it with the corresponding ligament stiffness baseline value, it is possible to set more reasonable mechanical properties for the ligaments in the finite element model, establish a finite element model that is more in line with the patient's individual characteristics, improve the accuracy and personalization of the model, and provide a more reliable basis for preoperative prediction and surgical plan planning.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

[0065] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is intended to be within the scope of this application and to form different embodiments. For example, any of the above-described claimed embodiments or implementations may be used in any combination. The information disclosed in this background section is intended solely to enhance understanding of the overall background technology of this application and should not be construed as an admission or any form of implication that such information constitutes prior art known to those skilled in the art.

Claims

1. A system for adjusting osteotomy path in robot-assisted knee replacement surgery, characterized in that: The system comprises: A multimodal data acquisition unit, comprising a knee joint pressure measurement pad, an optical acquisition module, and a kinematic data acquisition module, wherein the knee joint pressure measurement pad is used to collect intraoperative knee joint pressure signals, the optical acquisition module is used to collect the relative position of the femur and tibia, and the kinematic data acquisition module is used to collect kinematic data of the robotic arm; A main control computing unit, the main control computing unit includes a data fusion module and a dynamic compensation module, wherein the data fusion module is used to construct a synchronized spatiotemporal data set based on the intraoperative knee joint pressure signal, the relative position of the femur and tibia, and the kinematic data of the robotic arm; the dynamic compensation module is used to output compensation instructions to the robotic arm based on the synchronized spatiotemporal data set, the preoperatively predicted pressure prediction matrix and the soft tissue stiffness matrix to adjust the osteotomy path.

2. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 1, characterized in that: The dynamic compensation module is used for: extracting a pressure distribution matrix based on the synchronized spatiotemporal dataset; Comparing the pressure distribution matrix with the pressure prediction matrix to calculate a pressure deviation metric; When the pressure deviation metric exceeds a preset threshold, a compensation instruction is output to the robotic arm based on the pressure deviation metric and the soft tissue stiffness matrix to adjust the osteotomy path.

3. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 2, characterized in that: Outputting a compensation instruction to the robotic arm based on the pressure deviation metric and the soft tissue stiffness matrix includes: Modeling the pressure deviation metric as a linearized model of a soft tissue stiffness matrix and a posture adjustment amount of the end effector of the robotic arm, wherein the soft tissue stiffness matrix is ​​used to describe the mapping relationship between the displacement and force of each region of the soft tissue after the soft tissue is subjected to force; Solving the linearized model using the least squares method to obtain a target value of the posture adjustment amount of the end effector; Based on the target value and the kinematic data of the robotic arm, compensation parameters of each joint of the robotic arm are calculated, and compensation instructions are output to the robotic arm based on the compensation parameters of each joint of the robotic arm.

4. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 3, characterized in that: Modeling the pressure deviation metric as a linearized model of a soft tissue stiffness matrix and a posture adjustment amount of an end effector includes: Establishing a first functional model based on the pressure deviation metric and the soft tissue stiffness matrix to describe the functional relationship between the pressure deviation metric and the soft tissue displacement; and A second function model is constructed based on the posture adjustment of the end effector and the soft tissue displacement to describe the functional relationship between the bone displacement and the soft tissue displacement affected by the posture adjustment of the end effector; A linearization model is constructed based on the first function model and the second function model.

5. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 1, characterized in that: The intraoperative knee joint pressure includes the intraoperative medial pressure and the intraoperative lateral pressure, and the main control calculation unit further includes: a quantitative assessment module, the quantitative assessment module being configured to calculate an intraoperative medial-lateral pressure ratio of the knee joint based on the intraoperative medial pressure and the intraoperative lateral pressure, so as to determine whether the soft tissue is unbalanced according to the intraoperative medial-lateral pressure ratio of the knee joint and a preset balance range; The dynamic compensation module is used to output compensation instructions to the robotic arm to adjust the osteotomy path based on the synchronized spatiotemporal data set, the preoperatively predicted pressure prediction matrix and the soft tissue stiffness matrix when determining soft tissue imbalance.

6. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 5, characterized in that: The knee joint pressure measurement pad is further used to collect postoperative knee joint pressure signals during knee flexion and extension, and the quantitative evaluation module is further used to: generating a pressure distribution change curve during knee joint flexion and extension based on the postoperative knee joint pressure signal; Based on a preset time segment, the pressure distribution change curve is divided into multiple pressure distribution vectors, and the relative change rate of knee joint pressure is obtained by calculating the Euclidean distance between each adjacent pressure distribution vector and performing normalization processing; The flexion-extension continuity coefficient is obtained by calculating the degree of discreteness of the relative change rate of the knee joint pressure. The flexion-extension continuity coefficient is used to reflect the smoothness of the motion trajectory during the flexion and extension of the knee joint to determine whether the motion trajectory is abnormal.

7. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 1, characterized in that: The main control computing unit further includes a preoperative prediction module, which is used to: Acquiring three-dimensional image data of a target knee joint and segmenting key anatomical structures in the three-dimensional image data; Importing the segmented three-dimensional image data into finite element analysis software to generate a finite element model, wherein each key anatomical structure in the finite element model has different material properties; generating simulation results under various preset osteotomy plans based on the finite element model, and selecting a target osteotomy plan from the preset osteotomy plans based on the simulation results; The pressure prediction matrix, the soft tissue stiffness matrix and the initial osteotomy path corresponding to the target osteotomy scheme are output to guide the end effector to complete the first osteotomy according to the initial osteotomy path.

8. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 7, characterized in that: The preoperative prediction module is further used for: Acquire the synchronized spatiotemporal dataset collected by the data fusion module, and update the finite element model based on the synchronized spatiotemporal dataset; Based on the updated finite element model, the pressure adjustment matrix under the adjusted osteotomy path is output.

9. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 7, characterized in that: Before importing the segmented three-dimensional image data into the finite element analysis software to generate the finite element model, the method further includes: Acquiring user information of the target knee joint, the user information including race information, gender information, and age information; Obtaining a ligament stiffness reference value corresponding to the user information; The step of importing the segmented three-dimensional image data into finite element analysis software to generate a finite element model includes: The segmented three-dimensional image data and the ligament stiffness reference value are imported into finite element analysis software to generate a finite element model.

10. The osteotomy path adjustment system in robot-assisted knee replacement surgery according to claim 1, characterized in that: The optical acquisition module includes infrared marker balls arranged on the femur and tibia, and a camera array, wherein the camera array acquires the relative posture of the femur and tibia by tracking the infrared marker balls.

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