Design method of proximal fibula intramedullary nail based on computer simulation

Abstract

The invention relates to a design method of a proximal fibula intramedullary nail based on computer simulation. Comprising the following steps: constructing a three-dimensional digital model of near-end and surrounding tissues of fibula based on CT scanning data of a patient, and accurately marking common peroneal nerve walking, skeleton morphological characteristics and fracture types; simulating stress distribution data of the fracture part by using a finite element analysis method; in combination with the three-dimensional model and the stress distribution data, a personalized intramedullary nail model matched with the proximal curvature, length and internal structure of the individual fibula is generated through parameterized design; and planning a minimally invasive implantation path of the intramedullary nail in a computer simulation environment, and generating matched implantation path data to guide an operation. Compared with the prior art, three-dimensional anatomical modeling, finite element analysis and parametric design technologies are creatively integrated, a complete personalized intramedullary nail design system is established, and the matching precision, fixing stability and operation safety of instruments and bones are effectively improved.

Description

Technical Field

[0001] This invention relates to a design method for a medical device, and more particularly to a design method for a proximal fibular intramedullary nail based on computer simulation. Background Technology

[0002] Intramedullary nailing is an important technique for treating long bone fractures and plays an irreplaceable role in orthopedic clinical applications. However, current intramedullary nail design methods generally suffer from insufficient personalized adaptation, resulting in an unsatisfactory match between the implant and the patient's bone structure, directly affecting surgical outcomes and patient recovery quality.

[0003] Specifically, the technical solution disclosed in publication number CN104820760A proposes an intramedullary nail design method based on optimized curvature. This method improves the design by grouping femoral samples according to sex and metaphyseal scintillation coefficient and establishing a curvature function for the femoral medullary cavity centerline. While this method considers population anatomical differences to some extent, it still has significant limitations. This technology relies on standardized classification and design of intramedullary nails based on population statistical characteristics, failing to achieve truly individualized customization. For example... Figure 3 The diagram showing the grouping of femoral samples illustrates that this method can only provide a limited selection of intramedullary nails and cannot adapt to subtle differences in individual bone morphology. In particular, for complex anatomical structures such as the proximal fibula, the accuracy of the fit is difficult to guarantee.

[0004] This population-based statistical design method has inherent flaws: First, it cannot fully adapt to the specific characteristics of individual bone morphology, resulting in a misfit gap between the intramedullary nail and the medullary cavity; second, it lacks precise analysis of individual bone biomechanical properties, making it impossible to optimize the stress distribution performance of the instrument; most importantly, this method does not integrate three-dimensional spatial information of key tissues such as nerves and blood vessels, posing a risk of intraoperative injury. These limitations directly restrict the stability and safety of intramedullary nail fixation, affecting the quality of fracture healing.

[0005] Therefore, the key technical problem that urgently needs to be solved in this field is: how to break through the limitations of existing standardized designs and realize personalized customization of intramedullary nails throughout the entire process from morphological adaptation and mechanical optimization to surgical planning. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a computer simulation-based design method for intramedullary nails of the proximal fibula. This method achieves precise morphological adaptation of the intramedullary nail by constructing an individualized anatomical model, optimizes the mechanical properties of the instrument based on finite element analysis, generates a personalized intramedullary nail model by combining parametric design, and determines a safe implantation path through virtual surgical planning. This systematically improves the matching degree between the instrument and the bone, the fixation stability, and the surgical safety.

[0007] The objective of this invention can be achieved through the following technical solutions: This invention provides a computer simulation-based design method for a proximal fibular intramedullary nail, comprising the following steps: S1. Three-dimensional anatomical model reconstruction: Based on the patient's CT scan data, a three-dimensional digital model of the proximal fibula and surrounding tissues is constructed, and the course of the common peroneal nerve, bone morphology features, and fracture type are marked in the model; S2. Mechanical Simulation Analysis: Based on the three-dimensional digital model obtained in S1, the stress distribution data of the fracture site is simulated using the finite element analysis method. S3. Parametric intramedullary nail design: Based on the three-dimensional digital model obtained in S1 and the stress distribution obtained in S2, a personalized intramedullary nail model is generated to adapt to the individual proximal fibular curvature, length and internal structure. S4. Design a matching implantation path: In a computer simulation environment, based on the three-dimensional digital model in S1 and the intramedullary nail model in step S3, plan the minimally invasive implantation path of the intramedullary nail and obtain implantation path data that matches the intramedullary nail model.

[0008] Furthermore, in S1, the specific process of constructing a three-dimensional digital model of the proximal fibula and surrounding tissues based on the patient's CT scan data, and annotating the course of the common peroneal nerve, skeletal morphological characteristics, and fracture type in the model includes: S1.1 Import the patient's CT scan data into medical 3D reconstruction software, and use threshold segmentation and region growing algorithms to identify and extract the image contours of the proximal fibular bone tissue and surrounding soft tissues respectively. S1.2 Based on the image contours segmented in S1.1, a three-dimensional geometric model containing the proximal fibula and its surrounding tissues is reconstructed using surface rendering or volume rendering techniques. S1.3 On the three-dimensional geometric model generated in S1.2, based on medical imaging anatomy knowledge, the expected trajectory of the common peroneal nerve, the skeletal morphological parameters of the proximal fibula, and the specific location and type of fracture line are manually or semi-automatically marked, thereby completing the construction of the three-dimensional digital model.

[0009] Furthermore, in S1.1, the specific process of identifying and extracting the image contours of the proximal fibular bone tissue and surrounding soft tissue through threshold segmentation and region growing algorithms includes: S1.1.1 In medical 3D reconstruction software, an initial grayscale threshold is set for CT scan data to initially distinguish between high-density bone tissue and low-density soft tissue areas, thereby obtaining the initial contour of the proximal fibula. S1.1.2 Based on the initial contour obtained in S1.1.1, a region growing algorithm is used to grow a three-dimensional region with the proximal fibula as the seed point to identify and extract the complete image contour of the proximal fibula bone tissue. At the same time, the region of interest where key surrounding soft tissues such as the common peroneal nerve are located is manually outlined or secondary segmented based on a specific gray range to extract the approximate image contour. S1.1.3 Integrate and align the proximal fibular bone tissue image contour extracted in S1.1.2 with the extracted surrounding soft tissue image contour to form a complete image contour set for three-dimensional reconstruction.

[0010] Furthermore, in S1.3, based on medical imaging anatomy knowledge, the specific process of manually or semi-automatically marking the expected course of the common peroneal nerve, the skeletal morphological parameters of the proximal fibula, and the specific location and type of the fracture line includes: S1.3.1 On the three-dimensional geometric model generated in S1.2, based on the relative anatomical position of the common peroneal nerve and known bony landmarks, manually plot points or use the surface projection tool to outline the expected course of the common peroneal nerve and generate its three-dimensional spatial path. S1.3.2. Based on the three-dimensional geometric model processed in S1.3.1, measure and record the key skeletal morphological feature parameters of the proximal fibula; S1.3.3 Based on the three-dimensional geometric model processed in S1.3.2, three-dimensional tracing is performed along the fracture line displayed in the CT image to clearly mark the specific location and direction of the fracture line, and the fracture type is determined based on its morphological characteristics.

[0011] Furthermore, in S2, the specific process of simulating the stress distribution data of the fracture site using the finite element analysis method based on the three-dimensional digital model obtained in S1 includes: S2.1. Import the three-dimensional digital model constructed in S1 into the finite element analysis software, mesh the proximal fibular bone and fracture site, assign bone material properties according to CT values, set the contact conditions between fracture ends, and establish a finite element model for simulation. S2.2 Based on the stress characteristics of the proximal fibula under human physiological conditions, simulated physiological loads are applied to the finite element model established in S2.1, and the degrees of freedom of the distal end of the model are constrained to simulate fixed boundary conditions in the human body. S2.3. Run the finite element solver to calculate and simulate the stress distribution and deformation of the fracture site under different load conditions, and extract the stress peak, stress distribution cloud map and displacement data of the key area as the stress distribution data.

[0012] Furthermore, in S3, the simulated physiological load includes one or more combinations of pressure, torsional force, and tensile force.

[0013] Furthermore, in S3, the specific process of generating a personalized intramedullary nail model adapted to the individual's proximal fibular curvature, length, and internal structure based on the three-dimensional digital model obtained in S1 and the stress distribution obtained in S2 includes: S3.1 Based on the curvature, length and internal structural dimensions of the proximal fibular medullary canal reflected by the three-dimensional digital model obtained in S1, an initial three-dimensional model of the intramedullary nail that matches the anatomical morphology of the medullary canal is generated in computer-aided design software using parametric modeling methods. S3.2. Combine the initial three-dimensional model of the intramedullary nail generated in S3.1 with the stress distribution data obtained in S2, and simulate the mechanical state after implantation of the intramedullary nail in the finite element analysis software. Iteratively optimize the nail body contour, wall thickness and the position and number of locking holes of the intramedullary nail for the stress concentration area. S3.3 Based on the intramedullary nail model optimized in S3.2, according to the course of the common peroneal nerve and the skeletal morphology marked in S1, the shape and position of the proximal antirotation module structure and the layout of the distal locking screws are specifically designed to generate the final personalized intramedullary nail model.

[0014] Further, in S3.2, the initial three-dimensional model of the intramedullary nail generated in S3.1 is combined with the stress distribution data obtained in S2. The mechanical state after implantation of the intramedullary nail is simulated in finite element analysis software. The nail body contour, wall thickness, and the position and number of locking holes of the intramedullary nail are iteratively optimized for stress concentration areas. The specific process of the above steps includes: S3.2.1 Perform Boolean operations on the initial three-dimensional model of the intramedullary nail generated in S3.1 and the three-dimensional digital model obtained in S1, assemble them in the finite element analysis software to form a "bone-intramedullary nail" coupled model, and set the contact relationship between the two. S3.2.2 Based on the simulated physiological load conditions set in S2.2, apply the same load and boundary conditions to the "bone-intramedullary nail" coupling model established in S3.2.1, run the finite element solver, and calculate the stress distribution after intramedullary nail implantation; S3.2.3 Extract and analyze the stress distribution cloud map of the intramedullary nail calculated in S3.2.2, identify the stress concentration area where the stress exceeds the allowable safety threshold of the material, and based on this analysis result, return to the computer-aided design software to adjust the nail body outline of the intramedullary nail in the stress concentration area, increase the local wall thickness, or optimize the position and number of locking holes to generate a corrected three-dimensional model of the intramedullary nail. S3.2.4 Substitute the corrected three-dimensional model of the intramedullary nail obtained in S3.2.3 back into the steps of S3.2.1 to S3.2.3 for iterative simulation verification until the stress distribution of the intramedullary nail meets the preset mechanical safety requirements, thus completing the iterative optimization process.

[0015] Further, in S4, a matching implantation path is designed. In a computer simulation environment, based on the three-dimensional digital model in S1 and the intramedullary nail model in S3, a minimally invasive implantation path for the intramedullary nail is planned to obtain implantation path data matching the intramedullary nail model. The specific process of the above steps includes: S4.1 Determination of Path Entry Point: In a computer simulation environment, the intramedullary nail model generated in S3 is virtually registered with the three-dimensional digital model constructed in S1. Based on the anatomical morphology of the proximal fibula and the requirements of minimally invasive surgery, the optimal skin entry point and bone entry point for intramedullary nail implantation are determined on the three-dimensional digital model. S4.2 Implantation Channel Planning: Based on the bone entry point determined in S4.1 and according to the axial curvature of the personalized intramedullary nail model, plan an ideal implantation channel in three-dimensional space that extends from the bone entry point to the distal end of the medullary cavity, ensuring that the channel matches the shape of the medullary cavity and avoids impact with the cortical bone. S4.3 Nerve Avoidance Verification and Adjustment: Perform three-dimensional spatial distance calculation and collision detection between the implantation channel planned in S4.2 and the expected trajectory of the common peroneal nerve marked in S1. If the channel and the nerve trajectory are detected to be too close or there is interference, the angle or entry point of the implantation channel will be automatically or manually adjusted until the implantation path maintains a safe distance from the high-risk area of ​​the common peroneal nerve.

[0016] Furthermore, S4 also includes S4.4, guidance data generation, which specifically includes generating implantation path data for guiding the surgery based on the final implantation path verified in S4.3. The data includes the implantation angle and the needle insertion depth.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The method described in this invention, through the systematic integration of computer simulation technology, achieves personalized design of the proximal fibular intramedullary nail throughout the entire process, from anatomical adaptation and biomechanical optimization to surgical planning, resulting in significant beneficial effects. First, a three-dimensional digital model containing the detailed structures of the proximal fibula and surrounding areas is constructed based on the patient's CT data, with precise annotation of the common peroneal nerve course, bone morphology, and fracture details. This provides a realistic individual anatomical basis for all subsequent steps, ensuring a high degree of fit between the instrument design and the patient's physiological structure. Subsequently, finite element analysis is used to simulate the stress distribution at the fracture site, enabling a scientific assessment of the mechanical properties of internal fixation under different loads in advance. This overcomes the limitations of traditional reliance on surgeon experience and provides a quantitative basis for the structural optimization of the intramedullary nail. Based on this, a parametric intramedullary nail model generated by combining individual anatomical parameters and biomechanical simulation data can accurately match the curvature, length, and internal bone density characteristics of the proximal fibula. This not only significantly improves the fit between the instrument and the medullary cavity but also effectively enhances fixation stability through optimized anti-rotation modules and locking screw layout. Finally, by planning a minimally invasive implantation path in a virtual environment that precisely matches the personalized intramedullary nail, the high-risk area of ​​the common peroneal nerve can be actively avoided, generating a visualized and quantifiable surgical guidance plan, which significantly reduces the risk of nerve damage during surgery and improves the accuracy and safety of surgical procedures. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall process of the computer simulation-based design method for the proximal fibular intramedullary nail in this invention. Figure 2 This is a schematic diagram of the specific process of step S1 in this invention; Figure 3 This is a schematic diagram of the specific process of step S2 in this invention; Figure 4 This is a schematic diagram of the specific process of step S3 in this invention; Figure 5 This is a schematic diagram of the specific process of step S4 in this invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, circuit structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0020] Example 1 This embodiment presents a computer-simulated design method for a proximal fibular intramedullary nail; see [link to relevant documentation]. Figure 1 This includes the following steps: S1. Three-dimensional anatomical model reconstruction: Based on the patient's CT scan data, a three-dimensional digital model of the proximal fibula and surrounding tissues is constructed, and the course of the common peroneal nerve, bone morphology features, and fracture type are marked in the model; See Figure 2 In S1, the specific process of constructing a three-dimensional digital model of the proximal fibula and surrounding tissues based on the patient's CT scan data, and annotating the course of the common peroneal nerve, skeletal morphology, and fracture type in the model includes: S1.1 Import the patient's CT scan data into medical 3D reconstruction software, and use threshold segmentation and region growing algorithms to identify and extract the image contours of the proximal fibular bone tissue and surrounding soft tissues respectively. S1.2 Based on the image contours segmented in S1.1, a three-dimensional geometric model containing the proximal fibula and its surrounding tissues is reconstructed using surface rendering or volume rendering techniques. S1.3 On the three-dimensional geometric model generated in S1.2, based on medical imaging anatomy knowledge, the expected trajectory of the common peroneal nerve, the skeletal morphological parameters of the proximal fibula, and the specific location and type of fracture line are manually or semi-automatically marked, thereby completing the construction of the three-dimensional digital model.

[0021] In specific implementation, S1.1, the process of identifying and extracting the image contours of the proximal fibular bone tissue and surrounding soft tissue through threshold segmentation and region growing algorithms includes: S1.1.1 In medical 3D reconstruction software, an initial grayscale threshold is set for CT scan data to initially distinguish between high-density bone tissue and low-density soft tissue areas, thereby obtaining the initial contour of the proximal fibula. S1.1.2 Based on the initial contour obtained in S1.1.1, a region growing algorithm is used to grow a three-dimensional region with the proximal fibula as the seed point to identify and extract the complete image contour of the proximal fibula bone tissue. At the same time, the region of interest where key surrounding soft tissues such as the common peroneal nerve are located is manually outlined or secondary segmented based on a specific gray range to extract the approximate image contour. S1.1.3 Integrate and align the proximal fibular bone tissue image contour extracted in S1.1.2 with the extracted surrounding soft tissue image contour to form a complete image contour set for three-dimensional reconstruction.

[0022] In specific implementation, S1.3, based on medical imaging anatomy knowledge, involves manually or semi-automatically marking the expected course of the common peroneal nerve, the skeletal morphological parameters of the proximal fibula, and the specific location and type of the fracture line, including the following process: S1.3.1 On the three-dimensional geometric model generated in S1.2, based on the relative anatomical position of the common peroneal nerve and known bony landmarks, manually plot points or use the surface projection tool to outline the expected course of the common peroneal nerve and generate its three-dimensional spatial path. S1.3.2. Based on the three-dimensional geometric model processed in S1.3.1, measure and record the key skeletal morphological feature parameters of the proximal fibula; S1.3.3 Based on the three-dimensional geometric model processed in S1.3.2, three-dimensional tracing is performed along the fracture line displayed in the CT image to clearly mark the specific location and direction of the fracture line, and the fracture type is determined based on its morphological characteristics.

[0023] The mechanism of 3D anatomical model reconstruction begins with the processing of CT scan data of the proximal fibula. This process first imports the CT sequence images into medical 3D reconstruction software, and by setting an initial grayscale threshold, preliminary distinction is made between bone and soft tissues, obtaining the initial contour of the proximal fibula. Then, based on this initial contour, a region growing algorithm is used, with the proximal fibula as the seed point, to grow a 3D region, accurately identifying and extracting the complete bone tissue contour. For key soft tissues such as the common fibular nerve, the approximate contour is extracted within its region of interest through manual outlining or secondary segmentation based on a specific grayscale range. Finally, the contours of the bones and soft tissues are integrated and aligned to form a complete image contour set for 3D reconstruction. Next, surface rendering or volume rendering techniques are used to reconstruct a 3D geometric model containing the proximal fibula and surrounding tissues based on this contour set. After obtaining the 3D geometric model, annotation is performed according to standard anatomical knowledge. Based on the relative positional relationship between the common fibular nerve and known bony landmarks, the expected course of the nerve is outlined manually or using surface projection tools, generating a 3D spatial path. Simultaneously, key morphological parameters of the proximal fibula are measured and recorded. Finally, a three-dimensional tracing was performed along the fracture line shown in the CT image to clearly mark the specific location and direction of the fracture line, and the fracture was classified according to its morphological characteristics. Through this series of continuous technical steps, a three-dimensional digital model was finally constructed, which was marked with the course of the common peroneal nerve, skeletal morphological characteristics, and fracture type.

[0024] S2. Mechanical Simulation Analysis: Based on the three-dimensional digital model obtained in S1, the stress distribution data of the fracture site is simulated using the finite element analysis method. See Figure 3 In specific implementation, S2, based on the three-dimensional digital model obtained in S1, involves the following process for simulating the stress distribution data at the fracture site using the finite element analysis method: S2.1. Import the three-dimensional digital model constructed in S1 into the finite element analysis software, mesh the proximal fibular bone and fracture site, assign bone material properties according to CT values, set the contact conditions between fracture ends, and establish a finite element model for simulation. S2.2 Based on the stress characteristics of the proximal fibula under human physiological conditions, simulated physiological loads are applied to the finite element model established in S2.1, and the degrees of freedom of the distal end of the model are constrained to simulate fixed boundary conditions in the human body. S2.3. Run the finite element solver to calculate and simulate the stress distribution and deformation of the fracture site under different load conditions, and extract the stress peak, stress distribution cloud map and displacement data of the key area as the stress distribution data.

[0025] The core of mechanical simulation analysis lies in transforming the three-dimensional digital model obtained in S1 into a finite element model suitable for physical simulation and solving its mechanical response. This process begins by importing the completed three-dimensional digital model into professional finite element analysis software. Within the software, the geometry of the proximal fibula, especially the fracture site, is discretized, i.e., meshed to generate a large number of tiny elements. The material properties of each element are not arbitrarily set but are mapped and assigned based on its corresponding CT value, thus accurately reflecting the differences in material properties caused by different density regions of the bone, such as the elastic modulus and Poisson's ratio of cortical and cancellous bone. Simultaneously, to realistically simulate the mechanical interaction between the fracture ends, specific contact conditions between the fracture surfaces need to be set, such as frictional contact or bonded contact, to establish a finite element model usable for calculation. Next, based on the actual stress characteristics of the proximal fibula under physiological conditions such as standing or walking, simulated physiological loads are applied to this finite element model. The application point and direction of the loads must conform to the principles of anatomical force. Furthermore, to simulate the realistic state of bone constrained by surrounding tissues within the human body, freedom constraints are applied to the distal end of the model to simulate fixed boundary conditions within the body. Finally, a finite element method (FEM) solver was run to perform numerical calculations, iteratively solving a large set of linear equations to simulate the stress distribution and deformation of the fracture site under different load conditions. After the calculations were completed, key biomechanical indicators were extracted, including the peak stress in the fracture area, to assess the risk of internal fixation device failure; a comprehensive stress distribution cloud map was used to visually display the stress transmission path; and displacement data was used to analyze stability. These data together constitute the stress distribution data upon which the subsequent personalized intramedullary nail design depends.

[0026] S3. Parametric intramedullary nail design: Based on the three-dimensional digital model obtained in S1 and the stress distribution obtained in S2, a personalized intramedullary nail model is generated to adapt to the individual proximal fibular curvature, length and internal structure. In S3, the simulated physiological load includes one or more combinations of pressure, torsional force, and tensile force.

[0027] See Figure 4 In S3, the specific process of generating a personalized intramedullary nail model adapted to the individual's proximal fibular curvature, length, and internal structure based on the three-dimensional digital model obtained in S1 and the stress distribution obtained in S2 includes: S3.1 Based on the curvature, length and internal structural dimensions of the proximal fibular medullary canal reflected by the three-dimensional digital model obtained in S1, an initial three-dimensional model of the intramedullary nail that matches the anatomical morphology of the medullary canal is generated in computer-aided design software using parametric modeling methods. S3.2. Combine the initial three-dimensional model of the intramedullary nail generated in S3.1 with the stress distribution data obtained in S2, and simulate the mechanical state after implantation of the intramedullary nail in the finite element analysis software. Iteratively optimize the nail body contour, wall thickness and the position and number of locking holes of the intramedullary nail for the stress concentration area. S3.3 Based on the intramedullary nail model optimized in S3.2, according to the course of the common peroneal nerve and the skeletal morphology marked in S1, the shape and position of the proximal antirotation module structure and the layout of the distal locking screws are specifically designed to generate the final personalized intramedullary nail model.

[0028] In specific implementation, in S3.2, the initial three-dimensional model of the intramedullary nail generated in S3.1 is combined with the stress distribution data obtained in S2. The mechanical state after implantation of the intramedullary nail is simulated in finite element analysis software. The nail body contour, wall thickness, and the position and number of locking holes of the intramedullary nail are iteratively optimized for stress concentration areas. The specific process of the above steps includes: S3.2.1 Perform Boolean operations on the initial three-dimensional model of the intramedullary nail generated in S3.1 and the three-dimensional digital model obtained in S1, assemble them in the finite element analysis software to form a "bone-intramedullary nail" coupled model, and set the contact relationship between the two. S3.2.2 Based on the simulated physiological load conditions set in S2.2, apply the same load and boundary conditions to the "bone-intramedullary nail" coupling model established in S3.2.1, run the finite element solver, and calculate the stress distribution after intramedullary nail implantation; S3.2.3 Extract and analyze the stress distribution cloud map of the intramedullary nail calculated in S3.2.2, identify the stress concentration area where the stress exceeds the allowable safety threshold of the material, and based on this analysis result, return to the computer-aided design software to adjust the nail body outline of the intramedullary nail in the stress concentration area, increase the local wall thickness, or optimize the position and number of locking holes to generate a corrected three-dimensional model of the intramedullary nail. S3.2.4 Substitute the corrected three-dimensional model of the intramedullary nail obtained in S3.2.3 back into the steps of S3.2.1 to S3.2.3 for iterative simulation verification until the stress distribution of the intramedullary nail meets the preset mechanical safety requirements, thus completing the iterative optimization process.

[0029] The mechanism of parametric intramedullary nail design is a closed-loop optimization process based on individual anatomical and mechanical properties. This process begins with extracting key geometric parameters of the proximal fibular medullary canal from the 3D digital model obtained in S1, including the curvature, length, and internal structural dimensions of the canal. In a computer-aided design software environment, these specific geometric parameters drive a parametric modeling program to automatically generate an initial 3D model of the intramedullary nail that morphologically closely matches the patient's medullary canal anatomy. This model forms the basic geometric framework for the personalized design. Next, an iterative optimization phase of mechanical performance is initiated. The initial intramedullary nail model is assembled with the bone model from S1 through Boolean operations to form a complete coupled bone-intramedullary nail model, and the contact relationship between the two is set in finite element analysis software. Subsequently, the same simulated physiological loads, such as pressure or torsional forces, and boundary conditions as in the S2 mechanical simulation are applied, and the finite element solver is run to calculate the stress distribution after intramedullary nail implantation. By analyzing the stress cloud map obtained through calculation, stress concentration areas where the stress value exceeds the allowable safety threshold of the material are identified. Based on the analysis results, the design stage is fed back to adjust the nail body contour in a targeted manner to improve stress flow, increase local wall thickness to enhance strength, or optimize the position and number of locking holes to avoid high-stress areas, thus generating a modified intramedullary nail model. This optimized model needs to be substituted into finite element simulation again for verification and iterative iteration until its stress distribution fully meets the preset mechanical safety requirements. Finally, based on the mechanically optimized model, the functional structure is refined according to the peroneal nerve trajectory and bone morphology characteristics marked in S1. This includes designing the specific structural shape and installation position of the proximal anti-rotation module to ensure its effective function and avoid high-risk areas of the nerve, as well as planning the layout of the distal locking screws to provide sufficient stability. Finally, a personalized three-dimensional model of the intramedullary nail that conforms to individual anatomical morphology and meets the requirements of mechanical performance and surgical safety is generated.

[0030] The proximal anti-rotation module is a specialized mechanical structure integrated into the proximal portion of the personalized intramedullary nail. Its core function is to interlock with bony landmarks or sections of the proximal fibula through a specific physical configuration to effectively resist rotational displacement of the fracture ends under stress. The specific structural shape and spatial position of this module are personalized based on the individual skeletal morphological characteristics (such as the unique contour of the fibular head) marked in the 3D model in S1 to ensure maximum matching with the bone.

[0031] S4. Design a matching implantation path: In a computer simulation environment, based on the three-dimensional digital model in S1 and the intramedullary nail model in step S3, plan the minimally invasive implantation path of the intramedullary nail and obtain implantation path data that matches the intramedullary nail model.

[0032] See Figure 5 In specific implementation, in step S4, a matching implantation path is designed. In a computer simulation environment, based on the three-dimensional digital model in step S1 and the intramedullary nail model in step S3, a minimally invasive implantation path for the intramedullary nail is planned to obtain implantation path data matching the intramedullary nail model. The specific process of the above steps includes: S4.1 Determination of Path Entry Point: In a computer simulation environment, the intramedullary nail model generated in S3 is virtually registered with the three-dimensional digital model constructed in S1. Based on the anatomical morphology of the proximal fibula and the requirements of minimally invasive surgery, the optimal skin entry point and bone entry point for intramedullary nail implantation are determined on the three-dimensional digital model. S4.2 Implantation Channel Planning: Based on the bone entry point determined in S4.1 and according to the axial curvature of the personalized intramedullary nail model, plan an ideal implantation channel in three-dimensional space that extends from the bone entry point to the distal end of the medullary cavity, ensuring that the channel matches the shape of the medullary cavity and avoids impact with the cortical bone. S4.3 Nerve Avoidance Verification and Adjustment: Perform three-dimensional spatial distance calculation and collision detection between the implantation channel planned in S4.2 and the expected trajectory of the common peroneal nerve marked in S1. If the channel and the nerve trajectory are detected to be too close or there is interference, the angle or entry point of the implantation channel will be automatically or manually adjusted until the implantation path maintains a safe distance from the high-risk area of ​​the common peroneal nerve.

[0033] In specific implementation, S4 also includes S4.4, guidance data generation, which specifically includes generating implantation path data for guiding the surgery based on the final implantation path verified in S4.3. The data includes the implantation angle and the needle insertion depth.

[0034] The process begins with virtual registration. The final intramedullary nail model generated by S3 is precisely spatially aligned with the 3D digital model constructed by S1, which includes annotations of bone, soft tissue, and nerves. Based on the aligned integrated model, and taking into full account the specific anatomical morphology of the proximal fibula and the requirements of minimally invasive surgery for minimizing incision size and tissue damage, an optimal skin entry point and its corresponding bone drilling entry point are calculated and determined on the surface of the 3D model through geometric analysis. The positioning of these two points must ensure that surgical instruments can access and enter the medullary canal without obstruction.

[0035] The next step is the geometric planning stage for the implantation channel. Using the bone entry point determined in the previous step as a starting reference, and based on the unique axial curvature of the personalized intramedullary nail model, an ideal implantation trajectory is reverse-engineered in three-dimensional space, starting from this entry point and extending along the natural anatomical channel of the medullary cavity to its distal end. This trajectory must closely match the internal morphology of the medullary cavity, and a collision detection algorithm is used to ensure that the intramedullary nail does not unexpectedly collide with the cortical bone wall during virtual implantation.

[0036] Next, crucial safety verification is performed. The planned 3D spatial data of the implantation channel is precisely calculated to match the expected trajectory of the common peroneal nerve pre-marked in S1, and a collision detection algorithm is executed. If the shortest distance between the planned channel and the nerve trajectory is detected to be lower than a preset safety threshold or if spatial interference exists, the system will automatically or the designer will manually fine-tune the spatial angle of the implantation channel or re-optimize the entry point position. After several iterative adjustments, the final confirmed implantation path maintains a sufficient safe distance from high-risk areas such as the common peroneal nerve.

[0037] Finally, based on the validated final implantation path, the system extracts and generates key guidance data to directly guide the surgical procedure. This data includes the precise implantation angle relative to the standard anatomical plane to ensure the intramedullary nail is screwed in along the planned path; and the needle depth from the skin entry point to the target location in the medullary cavity.

[0038] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A design method for a proximal fibular intramedullary nail based on computer simulation, characterized in that, Includes the following steps: S1. Three-dimensional anatomical model reconstruction: Based on the patient's CT scan data, a three-dimensional digital model of the proximal fibula and surrounding tissues is constructed, and the course of the common peroneal nerve, bone morphology features, and fracture type are marked in the model; S2. Mechanical Simulation Analysis: Based on the three-dimensional digital model obtained in S1, the stress distribution data of the fracture site is simulated using the finite element analysis method. S3. Parametric intramedullary nail design: Based on the three-dimensional digital model obtained in S1 and the stress distribution obtained in S2, a personalized intramedullary nail model is generated to adapt to the individual proximal fibular curvature, length and internal structure. S4. Design a matching implantation path: In a computer simulation environment, based on the three-dimensional digital model in S1 and the intramedullary nail model in step S3, plan the minimally invasive implantation path of the intramedullary nail and obtain implantation path data that matches the intramedullary nail model.

2. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 1, characterized in that, In S1, the specific process of constructing a three-dimensional digital model of the proximal fibula and surrounding tissues based on the patient's CT scan data, and annotating the course of the common peroneal nerve, skeletal morphology features, and fracture type in the model includes: S1.1 Import the patient's CT scan data into medical 3D reconstruction software, and use threshold segmentation and region growing algorithms to identify and extract the image contours of the proximal fibular bone tissue and surrounding soft tissues respectively. S1.2 Based on the image contours segmented in S1.1, a three-dimensional geometric model containing the proximal fibula and its surrounding tissues is reconstructed using surface rendering or volume rendering techniques. S1.3 On the three-dimensional geometric model generated in S1.2, based on medical imaging anatomy knowledge, the expected trajectory of the common peroneal nerve, the skeletal morphological parameters of the proximal fibula, and the specific location and type of fracture line are manually or semi-automatically marked, thereby completing the construction of the three-dimensional digital model.

3. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 2, characterized in that, In S1.1, the specific process of identifying and extracting the image contours of the proximal fibular bone tissue and surrounding soft tissue through threshold segmentation and region growing algorithms includes: S1.1.1 In medical 3D reconstruction software, an initial grayscale threshold is set for CT scan data to initially distinguish between high-density bone tissue and low-density soft tissue areas, thereby obtaining the initial contour of the proximal fibula. S1.1.2 Based on the initial contour obtained in S1.1.1, a region growing algorithm is used to grow a three-dimensional region with the proximal fibula as the seed point to identify and extract the complete image contour of the proximal fibula bone tissue. At the same time, the region of interest where key surrounding soft tissues such as the common peroneal nerve are located is manually outlined or secondary segmented based on a specific gray range to extract the approximate image contour. S1.1.3 Integrate and align the proximal fibular bone tissue image contour extracted in S1.1.2 with the extracted surrounding soft tissue image contour to form a complete image contour set for three-dimensional reconstruction.

4. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 2, characterized in that, In S1.3, based on medical imaging anatomy knowledge, the specific process of manually or semi-automatically marking the expected course of the common peroneal nerve, the skeletal morphological parameters of the proximal fibula, and the specific location and type of the fracture line includes: S1.3.1 On the three-dimensional geometric model generated in S1.2, based on the relative anatomical position of the common peroneal nerve and known bony landmarks, manually plot points or use the surface projection tool to outline the expected course of the common peroneal nerve and generate its three-dimensional spatial path. S1.3.

2. Based on the three-dimensional geometric model processed in S1.3.1, measure and record the key skeletal morphological feature parameters of the proximal fibula; S1.3.3 Based on the three-dimensional geometric model processed in S1.3.2, three-dimensional tracing is performed along the fracture line displayed in the CT image to clearly mark the specific location and direction of the fracture line, and the fracture type is determined based on its morphological characteristics.

5. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 1, characterized in that, In S2, the specific process of simulating the stress distribution data of the fracture site using the finite element analysis method based on the three-dimensional digital model obtained in S1 includes: S2.

1. Import the three-dimensional digital model constructed in S1 into the finite element analysis software, mesh the proximal fibular bone and fracture site, assign bone material properties according to CT values, set the contact conditions between fracture ends, and establish a finite element model for simulation. S2.2 Based on the stress characteristics of the proximal fibula under human physiological conditions, simulated physiological loads are applied to the finite element model established in S2.1, and the degrees of freedom at the distal end of the model are constrained to simulate fixed boundary conditions within the human body. S2.

3. Run the finite element solver to calculate and simulate the stress distribution and deformation of the fracture site under different load conditions, and extract the stress peak value, stress distribution cloud map and displacement data of the key area as the stress distribution data.

6. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 5, characterized in that, In S3, the simulated physiological load includes one or more combinations of pressure, torsional force, and tensile force.

7. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 1, characterized in that, In S3, the specific process of generating a personalized intramedullary nail model that adapts to the individual's proximal fibular curvature, length, and internal structure, based on the three-dimensional digital model obtained in S1 and the stress distribution obtained in S2, includes: S3.1 Based on the curvature, length and internal structural dimensions of the proximal fibular medullary canal of an individual as reflected in the three-dimensional digital model obtained in S1, an initial three-dimensional model of the intramedullary nail that matches the anatomical morphology of the medullary canal is generated in computer-aided design software through parametric modeling methods. S3.

2. Combine the initial three-dimensional model of the intramedullary nail generated in S3.1 with the stress distribution data obtained in S2, and simulate the mechanical state after implantation of the intramedullary nail in the finite element analysis software. Iteratively optimize the nail body contour, wall thickness and the position and number of locking holes of the intramedullary nail for the stress concentration area. S3.

3. Based on the intramedullary nail model optimized in S3.2, according to the course of the common peroneal nerve and the skeletal morphology marked in S1, the shape and position of the proximal antirotation module structure and the layout of the distal locking screws are specifically designed to generate the final personalized intramedullary nail model.

8. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 7, characterized in that, In S3.2, the initial three-dimensional model of the intramedullary nail generated in S3.1 is combined with the stress distribution data obtained in S2. The mechanical state of the intramedullary nail after implantation is simulated in finite element analysis software. The nail body contour, wall thickness, and the position and number of locking holes of the intramedullary nail are iteratively optimized for stress concentration areas. The specific process of the above steps includes: S3.2.1 Perform Boolean operations on the initial three-dimensional model of the intramedullary nail generated in S3.1 and the three-dimensional digital model obtained in S1, assemble them in the finite element analysis software to form a "bone-intramedullary nail" coupled model, and set the contact relationship between the two. S3.2.

2. Based on the simulated physiological load conditions set in S2.2, apply the same load and boundary conditions to the "bone-intramedullary nail" coupling model established in S3.2.1, run the finite element solver, and calculate the stress distribution after intramedullary nail implantation. S3.2.3 Extract and analyze the stress distribution cloud map of the intramedullary nail calculated in S3.2.2, identify the stress concentration area where the stress exceeds the allowable safety threshold of the material, and based on this analysis result, return to the computer-aided design software to adjust the nail body outline of the intramedullary nail in the stress concentration area, increase the local wall thickness, or optimize the position and number of locking holes to generate a corrected three-dimensional model of the intramedullary nail. S3.2.4 Substitute the corrected three-dimensional model of the intramedullary nail obtained in S3.2.3 back into the steps of S3.2.1 to S3.2.3 for iterative simulation verification until the stress distribution of the intramedullary nail meets the preset mechanical safety requirements, thus completing the iterative optimization process.

9. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 1, characterized in that, In step S4, a matching implantation path is designed. In a computer simulation environment, based on the three-dimensional digital model in step S1 and the intramedullary nail model in step S3, a minimally invasive implantation path for the intramedullary nail is planned to obtain implantation path data that matches the intramedullary nail model. The specific process of the above steps includes: S4.1 Determination of Path Entry Point: In a computer simulation environment, the intramedullary nail model generated in S3 is virtually registered with the three-dimensional digital model constructed in S1. Based on the anatomical morphology of the proximal fibula and the requirements of minimally invasive surgery, the optimal skin entry point and bone entry point for intramedullary nail implantation are determined on the three-dimensional digital model. S4.2 Implantation Channel Planning: Based on the bone entry point determined in S4.1 and according to the axial curvature of the personalized intramedullary nail model, plan an ideal implantation channel in three-dimensional space that extends from the bone entry point to the distal end of the medullary cavity, ensuring that the channel matches the shape of the medullary cavity and avoids impact with the cortical bone. S4.3 Nerve Avoidance Verification and Adjustment: Perform three-dimensional spatial distance calculation and collision detection between the implantation channel planned in S4.2 and the expected trajectory of the common peroneal nerve marked in S1. If the channel and the nerve trajectory are detected to be too close or there is interference, the angle or entry point of the implantation channel will be automatically or manually adjusted until the implantation path maintains a safe distance from the high-risk area of ​​the common peroneal nerve.

10. The design method for a proximal fibular intramedullary nail based on computer simulation according to claim 9, characterized in that, S4 also includes S4.4, guidance data generation, which specifically includes generating implantation path data for guiding the surgery based on the final implantation path verified in S4.

3. The data includes the implantation angle and the needle insertion depth.

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