A method and system for reconstructing tumor-type bone defects

Abstract

This invention provides a method and system for reconstructing tumor-type bone defects. The method includes the following steps: acquiring bone defect information; constructing a bone defect repair; constructing a three-dimensional model of the bone defect; and simulating a bone defect surgery. This invention acquires bone defect information by assessing the patient's condition, simultaneously evaluating the anatomical structure of the patient's bone tumor using modern medical imaging, and constructing a bone defect repair based on the corresponding bone defect information. Then, the repair design of the bone defect repair is performed according to preset repair design data, and the patient's three-dimensional bone data is acquired in real time. Next, a three-dimensional model of the bone defect is constructed based on the acquired three-dimensional bone data, and a simulated bone defect surgery is performed. Finally, the implantation position, direction, and angle of the bone defect repair are predicted and optimized, achieving the effect of improving the accuracy of bone defect repair construction and solving the problem of low accuracy in bone defect repair construction in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of bone defect reconstruction technology, and more particularly to methods and systems for reconstructing tumor-type bone defects. Background Technology

[0002] With the development of oncology and orthopedic surgery, some new reconstruction techniques and methods have been gradually applied in clinical practice. For example, tumor bone inactivation and reimplantation technology, which involves inactivating tumor bone through external radiation and then reimplanting it into the body for bone defect reconstruction, has shown good therapeutic effects. Furthermore, with the development of 3D printing technology, it has become possible to create personalized bone defect repairs using 3D printing, enabling more precise matching of the patient's bone defect location. In practical applications, the extent of resection and reconstruction methods vary depending on the type of bone tumor. For example, benign tumors typically require reconstruction using autologous iliac bone, abnormal bone, and artificial bone; malignant bone tumors require extensive resection, necessitating the removal of large segments of bone, with the remaining bone defect reconstructed through artificial joint replacement and large-segment allograft bone transplantation. Therefore, developing a method and system for reconstructing tumor-type bone defects is crucial. It is important to note that during bone defect reconstruction, it is also necessary to consider how to promote bone healing and functional recovery, including promoting bone regeneration and repair, and restoring the patient's muscle strength and joint function through rehabilitation training.

[0003] Existing technologies utilize the main components of tumor-type articular cartilage tissue as bio-ink, employing 3D model reconstruction technology, 3D printing technology, and bionic principles to construct a scaffold with the same composition and structure as tumor-type articular cartilage tissue and integrated with the host tissue. This scaffold serves as an alternative material for repairing tumor-type articular cartilage defects, thus achieving the reconstruction of tumor-type bone defects.

[0004] CN111281608B discloses a module for repairing cartilage defects and a method for forming the same, comprising: establishing a virtual sample model based on the mechanical parameters of normal cartilage; obtaining a sample of virtual cartilage using a 3D printing method and the virtual sample model; and establishing a virtual model of the cartilage repair module based on the mechanical parameters of the sample.

[0005] The method for preparing a bone defect prosthesis disclosed in CN111631842B includes: constructing a bone prosthesis model using CT scans of the bone defect site and modifying the area that fits the human bone defect site into a porous fusion region; importing the STL file of the prosthesis model with the fusion layer into a 3D printing device and slicing it to obtain the cross-sectional shape to be printed; mixing Ti6Al4V titanium alloy powder according to the particle ratio and loading it into the material storage area to generate a TC4 matrix; and depositing micron-sized Ta powder into the porous structure of the fusion region by plasma spraying to obtain the bone defect prosthesis.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, because residual bone at the metaphysis is removed during bone defect reconstruction, the natural articular surfaces that allow for normal patient function are easily damaged, leading to loss of the patient's original natural function and resulting in low accuracy in the construction of bone defect repairs. Summary of the Invention

[0008] This invention provides a method and system for reconstructing tumor-type bone defects, which solves the problem of low accuracy in the construction of bone defect repairs in the prior art and improves the accuracy of bone defect repair construction.

[0009] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0010] A method for reconstructing tumor-related bone defects includes:

[0011] S1, assess the patient's condition to obtain bone defect information. The condition assessment is used to quantify the severity of the patient's bone tumor and the patient's physical condition. The bone defect information includes the location information and the extent of infiltration of the bone defect.

[0012] S2, The anatomical structure of the patient's bone tumor is assessed using modern medical imaging, and a bone defect repair is constructed in combination with the corresponding bone defect information. The structural assessment is used to determine the structure and implantation location of the bone defect repair.

[0013] S3, Design a bone defect prosthesis based on preset prosthesis design data and acquire the patient's three-dimensional bone data in real time. The repair design is used to repair the difference between the bone defect prosthesis and the patient's own bone structure. The three-dimensional bone data includes bone morphology data, bone structure data and bone density data.

[0014] S4. Construct a three-dimensional model of bone defect based on the acquired three-dimensional bone data and perform a simulated surgery for bone defect. At the same time, predict and optimize the implantation position, direction and angle of the bone defect repair. The three-dimensional model of bone defect is used to visualize the position of the bone defect repair and the relationship between the tissues surrounding the bone tumor.

[0015] Optionally, the specific construction steps of the bone defect repair body are as follows:

[0016] Based on the results of the disease assessment and in conjunction with the preset prosthesis factors, a corresponding virtual prosthesis is designed. The preset prosthesis factors include the matching degree of the surrounding bone, mechanical stability and biocompatibility.

[0017] The implantation position, direction, and angle of the virtual prosthesis at the bone defect site are determined by using the obtained repair conformity coefficient and combining it with the results of structural assessment. The repair conformity coefficient is used to measure the degree of fit between the virtual prosthesis and the bone defect site.

[0018] A virtual simulation surgery is performed on the virtual prosthesis, and the implantation effect of the virtual prosthesis is pre-evaluated. The pre-evaluation is used to obtain a numerical description of the fusion between the virtual prosthesis and the bone defect site.

[0019] Based on the results of the pre-assessment, the virtual prosthesis is repaired and optimized to construct a bone defect prosthesis until the preset implantation effect is met.

[0020] Optionally, the specific steps for obtaining the repair compliance coefficient include:

[0021] The surface morphology of the virtual prosthesis is compared with that of the bone defect site to obtain morphological difference data. The morphological difference data includes shape data and curvature data. The curvature data is used to numerically describe the degree of curvature of the bone defect surface.

[0022] The matching degree between the volume of the virtual prosthesis and the volume of the bone defect site is analyzed and compared, and the virtual prosthesis is used in conjunction with the preset volume difference to determine whether the virtual prosthesis completely fills the bone defect area. The preset volume difference represents the maximum allowable volume between the virtual prosthesis and the bone defect site.

[0023] The stress distribution values ​​after the virtual prosthesis is implanted into the bone defect site are obtained and the stress condition of the virtual prosthesis is evaluated. At the same time, the mechanical support effect of the virtual prosthesis on the bone defect site is evaluated. The stress distribution values ​​include the maximum allowable stress value and the minimum allowable stress value.

[0024] The repair compliance coefficient is obtained by combining morphological difference data, analysis and comparison results, and evaluation results. The repair compliance coefficient is calculated using the following formula:

[0025]

[0026] In the formula, j is the number of the bone defect site of the patient, j = 1, 2, ..., X, X is the total number of bone defect sites of the patient, ji is the number of the virtual prosthesis corresponding to the j-th bone defect site of the patient, ji = j.1, j.2, ..., jY, jY is the total number of virtual prostheses corresponding to the j-th bone defect site of the patient, YP j.i This represents the repair compliance coefficient of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient, where e is a natural constant, a1 represents the weighting of the relative deviation of curvature data relative to the repair compliance coefficient, and A j.iThis represents the curvature data of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient, where ΔA represents the reference deviation of the curvature data. denoted as average curvature data, and a2 represents the weighting of the relative volume deviation relative to the repair compliance coefficient. This represents the volume of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. Let represent the volume corresponding to the j-th bone defect site, ΔB represent the preset volume difference, and a3 represent the weighting of the relative deviation of the stress distribution value relative to the repair conformity coefficient. This represents the maximum allowable stress value of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. ΔC represents the minimum allowable stress value of the i-th virtual prosthesis corresponding to the j-th bone defect site of the patient, and ΔC represents the reference deviation of the stress value corresponding to the bone defect site of the patient.

[0027] Optionally, the specific steps for evaluating the stress condition of the virtual restoration are as follows:

[0028] A stress distribution map is generated based on the stress condition of the virtual prosthesis. The stress distribution map is used to visualize the stress concentration area where the virtual prosthesis and the surrounding bone experience stress.

[0029] By analyzing the gradient changes at the intersection of the virtual prosthesis and the bone defect in the stress concentration region, the stress transmission and dispersion can be obtained.

[0030] By combining the stress transmission and dispersion, abnormal stress areas in the stress distribution map are identified and marked, and corresponding solutions are formulated.

[0031] Optionally, the specific steps for acquiring the skeletal 3D data include:

[0032] Two-dimensional bone information is obtained by positioning and calibrating the patient's bone surface using a bone fixation device. The two-dimensional bone information includes geometric shape information and texture information.

[0033] A three-dimensional image of the skeleton is generated by combining two-dimensional skeleton information. The three-dimensional image of the skeleton is used to visualize the morphological and structural features of the skeleton.

[0034] Key information data is extracted from the three-dimensional image of the bone and registered and aligned with the preset prosthesis design data. The key information data is used to describe the morphological information, structural information and density information of the bone.

[0035] Based on the registration and alignment results, the bone defect repair device is designed and the patient's three-dimensional bone data is obtained by combining the bone matching index. The bone matching index is used to measure the degree of matching between key information data and bone three-dimensional data.

[0036] Optionally, the specific steps for obtaining the bone matching index are as follows:

[0037] Analyze three-dimensional images of bones and extract internal structural data of bones from the three-dimensional images of bones. The internal structural data includes the shape data of the medullary cavity and the porosity data of the bones.

[0038] The extracted key information data is combined to spatially align the internal structural data of the skeleton with the three-dimensional data of the skeleton.

[0039] Within a preset time period, feature point matching is performed between the 3D skeletal data and key information data until the obtained skeletal matching index is equal to the preset skeletal matching index. Otherwise, feature point matching is performed again. Feature point matching means registering and aligning the corner points and edge points in the key information data and the 3D skeletal data.

[0040] The skeletal matching index is calculated using the following formula:

[0041]

[0042] In the formula, m represents the number of feature point matches, m = 1, 2, ..., M, and M is the total number of feature point matches. m Let represent the skeleton matching index of the m-th feature point matching, e be the natural constant, and μ1 be the correction factor for the relative deviation of the skeleton morphology data. This represents the skeletal morphology reference data corresponding to the m-th feature point matching. Let represent the skeletal morphology data in the 3D skeletal data corresponding to the m-th feature point matching, ΔD represent the skeletal morphology reference data, and μ2 represent the correction factor for the relative deviation of the skeletal structure data. This represents the skeletal structure reference data corresponding to the m-th feature point matching. This represents the skeletal structure data in the 3D bone data corresponding to the m-th feature point matching, ΔE represents the skeletal structure reference data, and μ3 represents the correction factor for the relative deviation of the skeletal density data. This represents the bone density reference data corresponding to the m-th feature point matching. ΔP represents the bone density data in the 3D bone data corresponding to the m-th feature point matching, and ΔP represents the bone density reference data.

[0043] Optionally, the specific steps for constructing the three-dimensional model of the bone defect include:

[0044] The acquired 3D bone data is standardized to obtain 3D data of the bone defect site, which is then imported into medical image processing software.

[0045] The patient's bone tissue and bone defect sites are encoded and edge detection is performed according to edge detection indicators until the edge detection results meet the preset edge detection indicators. The edge detection is used to detect the boundary of the patient's bone tissue and the boundary of the bone defect site. The edge detection indicators are used to measure the degree of matching between the boundary of the patient's bone tissue and the boundary of the bone defect site.

[0046] The three-dimensional data of the bone defect site is compared and verified with the preset medical imaging data, and a three-dimensional model of the bone defect is constructed using a three-dimensional reconstruction algorithm.

[0047] Optionally, the specific method for obtaining the edge detection index is as follows:

[0048] The pixels belonging to the edge points in the bone defect area are obtained from the edge detection results and marked to obtain the boundary fit coefficient and boundary similarity coefficient. The boundary fit coefficient represents the ratio of the boundary fit deviation to the corresponding reference deviation. The boundary fit is used to measure the degree of fit between the boundary of the patient's bone tissue and the boundary of the bone defect area. The boundary similarity coefficient represents the ratio of the boundary similarity deviation to the corresponding reference deviation. The boundary similarity is used to measure the degree of similarity between the boundary of the patient's bone tissue and the boundary of the bone defect area.

[0049] Edge detection metrics are obtained by combining the obtained boundary fit coefficient and boundary similarity coefficient with boundary accuracy and detection environment metrics. The boundary accuracy is used to measure the accuracy between the boundary of the patient's bone tissue and the boundary of the bone defect site. The detection environment metrics are used to quantify the influence of environmental conditions on edge detection.

[0050] Optionally, the specific procedure of the bone defect simulation surgery includes:

[0051] The constructed 3D model of the bone defect is imported into the medical simulation environment. Based on the location, size and shape of the bone defect, the simulated surgical path of the bone defect is planned to formulate the corresponding simulated surgical plan for the bone defect.

[0052] The simulated surgical procedure was performed according to the simulated surgical plan for bone defects, and a simulated evaluation was conducted to determine the feasibility of the simulated surgical procedure. The simulated evaluation included checking the stability of the implanted bone defect prosthesis, the repair status of the bone defect site, and the physiological response of the patient's bone tissue.

[0053] The tumor-type bone defect reconstruction system includes: a bone defect information acquisition module, a bone defect prosthesis construction module, a bone three-dimensional data acquisition module, and a bone defect surgery simulation module;

[0054] The bone defect information acquisition module is used to assess the patient's condition and acquire bone defect information. The condition assessment is used to quantify the severity of the patient's bone tumor and the patient's physical condition. The bone defect information includes the location information and infiltration range information of the bone defect.

[0055] The bone defect repair module is used to perform structural assessment of the anatomical structure of the patient's bone tumor through modern medical imaging and to construct a bone defect repair in combination with the corresponding bone defect information. The structural assessment is used to determine the structure and implantation location of the bone defect repair.

[0056] The bone 3D data acquisition module is used to design a bone defect prosthesis based on preset prosthesis design data and acquire the patient's bone 3D data in real time. The repair design is used to repair the difference between the bone defect prosthesis and the patient's own bone structure. The bone 3D data includes bone morphology data, bone structure data and bone density data.

[0057] The bone defect surgery simulation module is used to construct a three-dimensional model of bone defects based on the acquired three-dimensional bone data and perform simulated bone defect surgery. At the same time, it predicts and optimizes the implantation position, direction and angle of the bone defect repair. The three-dimensional model of bone defects is used to visualize the position of the bone defect repair and the relationship between the tissues surrounding the bone tumor.

[0058] The above technical solution has at least the following advantages compared with the existing technology:

[0059] 1. The above-mentioned scheme obtains bone defect information by assessing the patient's condition and constructing a bone defect prosthesis. Then, based on the preset prosthesis design data, it designs the bone defect prosthesis and acquires the patient's three-dimensional bone data in real time. Finally, it constructs a three-dimensional model of the bone defect based on the acquired three-dimensional bone data and performs a simulated bone defect surgery. At the same time, it predicts and optimizes the implantation position, direction, and angle of the bone defect prosthesis, thereby improving the accuracy and reliability of the simulated bone defect surgery and thus improving the accuracy of bone defect prosthesis construction. This effectively solves the problem of low accuracy in bone defect prosthesis construction in existing technologies.

[0060] 2. The above scheme designs a corresponding virtual prosthesis based on the results of the condition assessment and in combination with the pre-set prosthesis factors. At the same time, it determines the implantation position, direction and angle of the virtual prosthesis at the bone defect site based on the results of the structural assessment. Then, a virtual simulation surgery is performed on the virtual prosthesis and the implantation effect of the virtual prosthesis is pre-assessed. Finally, based on the results of the pre-assessment, the virtual prosthesis is repaired and optimized to construct a bone defect prosthesis until the pre-set implantation effect is met. This achieves a more accurate pre-assessment of the virtual prosthesis, and thus a more accurate construction of the bone defect prosthesis.

[0061] 3. The above scheme obtains the three-dimensional data of the bone defect by standardizing the acquired three-dimensional bone data and importing it into medical image processing software. Then, the patient's bone tissue and bone defect are encoded, and edge detection is performed according to edge detection indicators until the edge detection results meet the preset edge detection indicators. Finally, the three-dimensional data of the bone defect are compared and verified with preset medical image data, and a three-dimensional reconstruction algorithm is used to construct a three-dimensional model of the bone defect. This improves the accuracy of edge detection and thus improves the accuracy and reliability of the three-dimensional model of the bone defect. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 A flowchart of a tumor-type bone defect reconstruction method provided in the embodiments of this application;

[0064] Figure 2 A flowchart illustrating the construction process of the bone defect repair body provided in the embodiments of this application;

[0065] Figure 3 A flowchart illustrating the construction process of a three-dimensional model of bone defects provided in this application embodiment;

[0066] Figure 4 This is a schematic diagram of the structure of the tumor-type bone defect reconstruction system provided in the embodiments of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0068] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0069] It should be noted that the terms "up", "down", "left", "right", "front", and "back" used in this invention are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0070] This invention addresses the problem of low accuracy in the construction of bone defect repair devices in existing technologies by providing a method and system for reconstructing tumor-type bone defects. The method involves assessing the patient's condition to obtain bone defect information, simultaneously evaluating the anatomical structure of the bone tumor using modern medical imaging, and constructing a bone defect repair device based on the corresponding bone defect information. Then, the repair device is designed according to pre-set repair design data, and the patient's three-dimensional bone data is acquired in real time. Next, a three-dimensional model of the bone defect is constructed based on the acquired bone three-dimensional data, and a simulated bone defect surgery is performed. Finally, the implantation position, direction, and angle of the bone defect repair device are predicted and optimized, thereby improving the accuracy of bone defect repair device construction.

[0071] like Figure 1 The diagram shows a flowchart of a tumor-type bone defect reconstruction method provided in an embodiment of this application. The method includes the following steps:

[0072] S1. The patient's condition is assessed to obtain information on bone defects. The condition assessment is used to quantify the severity of the patient's bone tumor and the patient's physical condition. The information on bone defects includes the location and extent of infiltration of the bone defects.

[0073] S2, through modern medical imaging, the anatomical structure of the patient's bone tumor is assessed and the corresponding bone defect information is combined to construct a bone defect repair body. The structural assessment is used to determine the structure and implantation location of the bone defect repair body.

[0074] S3, based on the preset prosthesis design data, designs the prosthesis for bone defects and acquires the patient's three-dimensional bone data in real time. The prosthesis design is used to repair the difference between the prosthesis for bone defects and the patient's own bone structure. The three-dimensional bone data includes bone morphology data, bone structure data and bone density data.

[0075] S4. Based on the acquired 3D bone data, a 3D model of the bone defect is constructed and a simulated surgery for the bone defect is performed. At the same time, the implantation position, direction and angle of the bone defect repair are predicted and optimized. The 3D model of the bone defect is used to visualize the position of the bone defect repair and the relationship between the tissues surrounding the bone tumor.

[0076] In this embodiment, the disease assessment aims to quantitatively evaluate the severity of the patient's bone tumor and their overall physical condition. The physician will comprehensively assess the patient's condition through medical history taking, physical examination, laboratory tests, and modern medical imaging. This assessment involves not only the size, shape, and location of the tumor, but also whether the tumor has infiltrated surrounding tissues and affected blood vessels and nerves. Simultaneously, the patient's overall physical condition is also a crucial aspect of the assessment (including age, nutritional status, and cardiopulmonary function), as these factors influence surgical risks and the accurate construction of the 3D bone model. When determining the shape and size of the prosthesis, the physician needs to comprehensively consider multiple factors (such as bone volume and density at the defect site, and the condition of surrounding tissues), utilizing 3D reconstruction technology on a computer. By simulating the three-dimensional structure of the defect, a prosthesis that perfectly matches the defect can be designed. This not only improves the stability of the prosthesis but also reduces trauma and risks during surgery. In addition, the choice of implantation site also needs careful consideration. Doctors need to ensure that the prosthesis can be accurately implanted into the defect site and form a good bond with the surrounding bone tissue. The tissues surrounding bone tumors mainly include bone tissue, cartilage tissue, fibrous tissue, adipose tissue, hematopoietic tissue, nerve tissue, and undifferentiated reticuloendothelial structures adjacent to the bone tumor. These tissues may have direct contact with or be adjacent to the tumor, and thus be affected by the bone tumor or produce corresponding reactions. This helps to formulate targeted treatment plans and improves the accuracy of bone defect prosthesis construction.

[0077] Furthermore, such as Figure 2 The diagram shown is a flowchart illustrating the construction process of the bone defect repair provided in this embodiment. The specific construction steps of the bone defect repair are as follows:

[0078] Based on the results of the disease assessment and in conjunction with the pre-set prosthesis factors, a corresponding virtual prosthesis is designed. The pre-set prosthesis factors include the matching degree of the surrounding bone, mechanical stability and biocompatibility.

[0079] The implantation position, orientation, and angle of the virtual prosthesis at the bone defect site are determined by using the obtained repair conformity coefficient and combining it with the results of structural assessment. The repair conformity coefficient is used to measure the degree of fit between the virtual prosthesis and the bone defect site.

[0080] A virtual simulation surgery was performed on the virtual prosthesis, and the implantation effect of the virtual prosthesis was pre-evaluated. The pre-evaluation was used to obtain a numerical description of the fusion between the virtual prosthesis and the bone defect site.

[0081] Based on the results of the pre-assessment, the virtual prosthesis is repaired and optimized to construct a bone defect prosthesis until the preset implantation effect is met.

[0082] In this embodiment, mechanical stability indicates that the prosthesis needs to withstand various mechanical loads in daily life (such as the pressure generated by walking and running). Therefore, during the prosthesis design process, doctors need to optimize the structure of the prosthesis using engineering principles and methods. Furthermore, the materials used in the prosthesis need to have good biocompatibility, meaning they do not cause rejection reactions with human tissue and can be gradually accepted and integrated by the body. When selecting prosthesis materials, biocompatibility, degradation rate, and toxicity are fully considered to ensure the safety and effectiveness of the prosthesis. Then, using computer-aided design software, based on the results of the condition assessment and preset prosthesis factors, a corresponding virtual prosthesis is designed. This virtual prosthesis can be displayed and simulated on a computer in three dimensions. If the performance of the virtual prosthesis meets the expected requirements, the doctor can proceed with the actual prosthesis manufacturing and surgical operation based on this design. Structural assessment typically includes a comprehensive analysis of the anatomical structure of the bone defect site, the matching degree of the surrounding bones, mechanical stability, and biocompatibility. The surrounding bones mainly include bone tissue adjacent to the defect site, specifically involving… The surrounding bones depend on the location of the defect. For example, if the defect is in the tibia, the surrounding bones may include the fibula, the distal femur of the knee, and the tarsal bones of the ankle. If the defect is in the skull, the surrounding bones may include adjacent skull plates and other skull structures. These surrounding bones not only provide support and protection for the defect site but may also participate in the regeneration and repair process. Therefore, in practical applications, doctors need to comprehensively consider the condition of the surrounding bones to ensure the effectiveness and safety of the treatment. Pre-assessment refers to a comprehensive and detailed analysis of the fusion between the virtual prosthesis and the bone defect site. This includes, but is not limited to, assessing the degree of matching between the shape, size, and surface texture characteristics of the prosthesis and the bone defect site, as well as predicting the mechanical stability and biocompatibility of the prosthesis after implantation. Secondly, through numerical description, pre-assessment can transform these complex fusion situations into specific and quantifiable data. These numerical descriptions typically include the fit index between the prosthesis and the bone defect site, the mechanical stability coefficient, and the biocompatibility score, achieving precise fusion between the prosthesis and the bone defect site.

[0083] Furthermore, by using finite element analysis to simulate the performance of the 3D reconstructed skeletal structure under preset stress conditions, and predicting the corresponding stability and degree of functional recovery, the repair compliance coefficient is obtained by comparing the simulation results with the expected results. Besides obtaining the repair compliance coefficient through the aforementioned finite element analysis method, it can also be obtained and calculated using the following methods and formulas: The specific steps for obtaining the repair compliance coefficient include:

[0084] The surface morphology of the virtual prosthesis is compared with that of the bone defect site to obtain morphological difference data. The morphological difference data includes shape data and curvature data. The curvature data is used to numerically describe the degree of curvature of the bone defect surface.

[0085] The matching degree between the volume of the virtual prosthesis and the volume of the bone defect site is analyzed and compared, and the virtual prosthesis is used to determine whether it completely fills the bone defect area by combining the preset volume difference. The preset volume difference represents the maximum allowable volume between the virtual prosthesis and the bone defect site.

[0086] The stress distribution values ​​after the virtual prosthesis is implanted into the bone defect site are obtained and the stress condition of the virtual prosthesis is evaluated. At the same time, the mechanical support effect of the virtual prosthesis on the bone defect site is evaluated. The stress distribution values ​​include the maximum allowable stress value and the minimum allowable stress value.

[0087] The repair compliance coefficient is obtained by combining morphological difference data, analysis and comparison results, and evaluation results. The repair compliance coefficient is calculated using the following formula:

[0088]

[0089] In the formula, j is the number of the bone defect site of the patient, j = 1, 2, ..., X, X is the total number of bone defect sites of the patient, ji is the number of the virtual prosthesis corresponding to the j-th bone defect site of the patient, ji = j.1, j.2, ..., jY, jY is the total number of virtual prostheses corresponding to the j-th bone defect site of the patient, YP j.i This represents the repair compliance coefficient of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient, where e is a natural constant, a1 represents the weighting of the relative deviation of curvature data relative to the repair compliance coefficient, and A j.i This represents the curvature data of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient, where ΔA represents the reference deviation of the curvature data. denoted as average curvature data, and a2 represents the weighting of the relative volume deviation relative to the repair compliance coefficient. This represents the volume of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. Let represent the volume corresponding to the j-th bone defect site, ΔB represent the preset volume difference, and a3 represent the weighting of the relative deviation of the stress distribution value relative to the repair conformity coefficient. This represents the maximum allowable stress value of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. ΔC represents the minimum allowable stress value of the i-th virtual prosthesis corresponding to the j-th bone defect site of the patient, and ΔC represents the reference deviation of the stress value corresponding to the bone defect site of the patient.

[0090] In this embodiment, shape data describes the geometric differences between the virtual prosthesis and the bone defect at the macroscopic level. For example, the degree of shape matching between the prosthesis and the defect is evaluated by calculating the dimensional parameters (such as length, width, and height) of the prosthesis and the defect. Curvature data is a key indicator used to numerically describe the curvature of the bone defect surface and the prosthesis surface. Curvature describes the degree of curvature of the surface at any point. It helps to assess the degree of matching between the prosthesis and the defect at the microscopic level and to understand whether the prosthesis can closely fit the complex surface morphology of the defect. In practical applications, the preset volume difference is usually a threshold to determine whether the volume difference between the virtual prosthesis and the bone defect is within an acceptable range. If the actual volume difference exceeds this threshold, the design of the prosthesis may need to be adjusted. In addition, the evaluation of the mechanical support effect usually involves the analysis of the stability, deformation, and protective effect of the prosthesis on the surrounding bone tissue when subjected to load, thus achieving a more accurate acquisition of the repair conformity coefficient.

[0091] Furthermore, the specific steps for assessing the stress condition of the virtual restoration are as follows:

[0092] A stress distribution map is generated based on the stress condition of the virtual prosthesis. The stress distribution map is used to visualize the stress concentration areas where stress occurs between the virtual prosthesis and the surrounding bone.

[0093] By analyzing the gradient changes at the intersection of the virtual prosthesis and the bone defect in the stress concentration region, the stress transmission and dispersion can be obtained.

[0094] By combining the stress transmission and dispersion, abnormal stress areas in the stress distribution map are identified and marked, and corresponding solutions are formulated.

[0095] In this embodiment, the stress distribution map is a visualization tool that can intuitively display the stress distribution between the virtual prosthesis and the surrounding bone. In the stress distribution map, different colors and gray levels are typically used to represent different stress values, thus clearly identifying stress concentration areas. These stress concentration areas are regions of high stress at the interface between the prosthesis and the bone defect. These areas are prone to stress concentration under stress. By analyzing the gradient changes at the interface between the virtual prosthesis and the bone defect in these stress concentration areas, the transmission and dispersion of stress can be understood. The gradient change reflects the rate of change of stress value in space. In stress concentration areas, a large gradient change indicates that the stress changes rapidly in that area, possibly indicating a stress peak or a stress abrupt change point. Therefore... By analyzing gradient changes, we can understand how stress is transmitted from the prosthesis to the surrounding bone and how it is dispersed during transmission. By comparing stress levels in different areas, we can also identify abnormal stress regions. For abnormally high stress regions, we can adjust their structure to reduce stress concentration, select materials with higher strength and better toughness to enhance the load-bearing capacity of the prosthesis, or improve the contact method between the prosthesis and the surrounding bone to increase the contact area and stability. For abnormally low stress regions, we can try to increase the connection strength between the prosthesis and the bone defect, such as by using a tighter fixation method, or consider adjusting the elastic modulus of the material to better match the surrounding bone, thereby improving stress transmission and dispersion. This allows for a more accurate assessment of the stress condition of the virtual prosthesis.

[0096] Furthermore, the specific steps for acquiring skeletal 3D data include:

[0097] The patient’s bone surface is positioned and calibrated using a bone fixation device to obtain two-dimensional bone information, which includes geometric and texture information.

[0098] By combining two-dimensional skeletal information, a three-dimensional image of the skeleton is generated. The three-dimensional image of the skeleton is used to visualize the morphological and structural features of the skeleton.

[0099] Key information data is extracted from the three-dimensional image of the bone and registered and aligned with the pre-set prosthesis design data. The key information data is used to describe the morphological, structural and density information of the bone.

[0100] Based on the registration and alignment results, the bone defect repair device is designed and the patient's three-dimensional bone data is obtained by combining the bone matching index. The bone matching index is used to measure the degree of matching between key information data and bone three-dimensional data.

[0101] In this embodiment, after positioning and calibration, a corresponding imaging device (such as an X-ray machine or CT scanner) is used to scan and obtain the patient's two-dimensional bone information. These two-dimensional images not only contain the geometric shape information of the bones (such as length, width, and curvature) but also the texture information (such as the density distribution of the bone surface and the arrangement of trabeculae). By analyzing these two-dimensional images, the detailed condition of the patient's bones can be understood (such as the location and degree of fractures, and the size and shape of bone defects). This information helps doctors to formulate more precise and personalized surgical plans. These two-dimensional images are superimposed and reconstructed using a three-dimensional reconstruction algorithm to generate three-dimensional images of the bones. This process usually involves image registration, interpolation, and filtering. The generated three-dimensional bone images usually have a high degree of realism and three-dimensionality, and can clearly show the surface morphology, internal structure, and relationships between different parts of the bones. By adjusting the image's viewing angle, lighting, and color, the morphological characteristics of the bones can be observed from different angles and levels (such as the direction of fracture lines, the size and location of bone defects). In addition, the three-dimensional bone images can also be used to quantitatively analyze the morphological and structural parameters of the bones (such as volume, surface area, and curvature), thereby improving the accuracy and reliability of the three-dimensional bone data.

[0102] Furthermore, three-dimensional image data of the patient's bone defect and surrounding bones are acquired using medical imaging scanning equipment. Then, computer image processing technology is used to segment, denoise, and reconstruct the images in three dimensions, generating an accurate three-dimensional model of the defective bone and surrounding bones. Finally, registration technology is used to align the model of the defective area with the model of the candidate prosthesis to obtain the bone matching index. Besides obtaining the bone matching index through the above-mentioned three-dimensional reconstruction and registration techniques, it can also be obtained and calculated using the following methods and formulas: The specific steps for obtaining the bone matching index are as follows:

[0103] Analyze 3D images of bones and extract internal structural data from them, including the shape data of the medullary cavity and the porosity data of the bone.

[0104] The extracted key information data is combined to spatially align the internal structural data of the skeleton with the three-dimensional data of the skeleton.

[0105] Within a preset time period, feature point matching is performed between the 3D skeletal data and key information data until the obtained skeletal matching index is equal to the preset skeletal matching index. Otherwise, feature point matching is performed again. Feature point matching means registering and aligning the corner points and edge points in the key information data and the 3D skeletal data.

[0106] The skeletal fit index is calculated using the following formula:

[0107]

[0108] In the formula, m represents the number of feature point matches, m = 1, 2, ..., M, and M is the total number of feature point matches. m Let represent the skeleton matching index of the m-th feature point matching, e be the natural constant, and μ1 be the correction factor for the relative deviation of the skeleton morphology data. This represents the skeletal morphology reference data corresponding to the m-th feature point matching. Let represent the skeletal morphology data in the 3D skeletal data corresponding to the m-th feature point matching, ΔD represent the skeletal morphology reference data, and μ2 represent the correction factor for the relative deviation of the skeletal structure data. This represents the skeletal structure reference data corresponding to the m-th feature point matching. This represents the skeletal structure data in the 3D bone data corresponding to the m-th feature point matching, ΔE represents the skeletal structure reference data, and μ3 represents the correction factor for the relative deviation of the skeletal density data. This represents the bone density reference data corresponding to the m-th feature point matching. ΔP represents the bone density data in the 3D bone data corresponding to the m-th feature point matching, and ΔP represents the bone density reference data.

[0109] In this embodiment, before spatial alignment, a suitable coordinate system or reference frame needs to be determined so that the internal structural data and three-dimensional data can be compared and matched in a unified space. Then, through a series of transformation operations (such as rotation, translation, and scaling), the internal structural data and three-dimensional data are aligned to the optimal state in space. In this process, key information data not only provides the precise reference points or features required for alignment, but can also be used to optimize the alignment process and ensure the accuracy and reliability of the results. For example, specific morphological features of the bone surface or the distribution of key tissues in the internal structure can be used as the basis for alignment. After spatial alignment is completed, the position and orientation of the bone's internal structural data are consistent with the bone's three-dimensional data. Doctors or researchers can obtain a more complete and accurate bone data model. This model not only includes the surface morphology and three-dimensional structure of the bone, but also reveals its complex internal structure and tissue relationships, enabling doctors to have a deeper understanding of the physiological and pathological state of the bone and achieving a more accurate acquisition of the bone matching index.

[0110] Furthermore, such as Figure 3 The diagram shown is a flowchart illustrating the construction process of a three-dimensional model of a bone defect provided in this embodiment of the application. The specific steps for constructing the three-dimensional model of a bone defect include:

[0111] The acquired 3D bone data is standardized to obtain 3D data of the bone defect site, which is then imported into medical image processing software.

[0112] The patient's bone tissue and bone defect sites are coded and edge detection is performed according to edge detection indicators until the edge detection results meet the preset edge detection indicators. Edge detection is used to detect the boundary of the patient's bone tissue and the boundary of the bone defect site. Edge detection indicators are used to measure the degree of matching between the boundary of the patient's bone tissue and the boundary of the bone defect site.

[0113] The three-dimensional data of the bone defect site is compared and verified with the preset medical imaging data, and a three-dimensional model of the bone defect is constructed using a three-dimensional reconstruction algorithm.

[0114] In this embodiment, after edge detection is completed, the detection results need to be evaluated according to preset edge detection indicators (such as edge sharpness, continuity, and positioning accuracy). This evaluation can be performed using objective indicators (such as edge detection precision and recall) or subjective evaluation (such as expert review). If the edge detection results do not meet the preset indicators, the parameters of the edge detection algorithm need to be readjusted and edge detection needs to be performed again. This process may require multiple iterations until a satisfactory edge detection result is achieved. The specific steps of the comparative verification include: using image registration technology to align the three-dimensional data of the bone defect with preset medical image data to eliminate differences caused by scanning conditions and patient position; comparing the bone defect areas. The morphological features (such as size, shape, and location) of the bone defect are compared with those in the preset data using quantitative indicators (such as volume and surface area) or qualitative descriptions (such as morphological similarity). Statistical analysis is performed based on the comparison results to assess the degree and significance of the differences between the bone defect and the preset data. Then, according to the characteristics and requirements of the bone defect, a suitable 3D reconstruction algorithm is selected. This may involve voxel reconstruction, surface reconstruction, and machine learning-based reconstruction methods. The selected 3D reconstruction algorithm is applied to construct a 3D model of the bone defect using the 3D data of the bone defect. This typically involves post-processing steps such as smoothing, hole filling, and detail enhancement, thereby improving the accuracy and visualization of the 3D model of the bone defect.

[0115] Furthermore, the specific method for obtaining edge detection metrics is as follows:

[0116] The pixels belonging to the edge points in the bone defect area are obtained from the edge detection results and marked to obtain the boundary fit coefficient and boundary similarity coefficient. The boundary fit coefficient represents the ratio of the deviation of the boundary fit to the corresponding reference deviation. The boundary fit is used to measure the degree of fit between the boundary of the patient's bone tissue and the boundary of the bone defect area. The boundary similarity coefficient represents the ratio of the deviation of the boundary similarity to the corresponding reference deviation. The boundary similarity is used to measure the degree of similarity between the boundary of the patient's bone tissue and the boundary of the bone defect area.

[0117] Edge detection metrics are derived by combining the obtained boundary fit coefficient and boundary similarity coefficient with boundary accuracy and detection environment metrics. Boundary accuracy is used to measure the accuracy between the boundary of the patient's bone tissue and the boundary of the bone defect site. Detection environment metrics are used to quantify the influence of environmental conditions on edge detection.

[0118] In this embodiment, before obtaining the boundary fit coefficient and boundary similarity coefficient, the following steps are included: edge detection post-processing: applying a selected edge detection algorithm to process the medical image of the bone defect site, obtaining the edge detection result and performing denoising processing to remove non-real edge points caused by noise or algorithm errors, while refining the edges to ensure that each edge point accurately represents the boundary of the bone defect site; edge point pixel labeling: extracting pixels belonging to the edge of the bone defect site from the edge detection result and labeling them, usually using different colors, labels, or values ​​to identify these points, and then adding the labeling information to the original medical image; the specific steps for obtaining the boundary fit coefficient are: obtaining a reference boundary, which is usually based on the gold standard boundary annotated by experts, or from other reliable medical... For each detected edge point in the image data, a machine learning algorithm is used to calculate the distance between it and the nearest point on the reference boundary. All distances are statistically analyzed, and a comprehensive similarity coefficient is calculated, such as by calculating the average distance, maximum distance, or a weighted score based on distance. The specific steps for obtaining the boundary similarity coefficient are as follows: features (such as shape, curvature, and orientation) are extracted from the detected edge points and the reference boundary. Similarity measurement methods (such as Euclidean distance and dynamic time warping) are used to calculate the similarity between the detected edges and the reference boundary. Based on the similarity measurement results, the boundary similarity coefficient is obtained. Then, the edge detection index is obtained by combining the boundary accuracy and detection environment indicators. Besides obtaining the edge detection index through the above methods, it can also be analyzed using the following formula:

[0119]

[0120] In the formula, r represents the number of edge detections, r = 1, 2, ..., R, and R is the total number of edge detections, WI r U represents the edge detection index corresponding to the r-th edge detection, where e is the natural constant. r U0 represents the boundary matching degree corresponding to the r-th edge detection, ΔU represents the reference deviation of the boundary matching degree, γ1 represents the correction factor for the relative deviation of the boundary matching degree, and S represents the boundary matching degree reference deviation. r Let Sr represent the boundary similarity corresponding to the r-th edge detection, S0 represent the preset boundary similarity, ΔS represent the boundary similarity reference deviation, γ2 represent the correction factor for the relative deviation of the boundary similarity, and Hr represent the boundary similarity. rH0 represents the boundary accuracy corresponding to the r-th edge detection, ΔH represents the preset boundary accuracy, and V represents the boundary accuracy reference deviation. r V represents the detection environment index corresponding to the r-th edge detection, and V0 represents the preset detection environment index; this improves the accuracy and reliability of edge detection.

[0121] Furthermore, the specific procedures for simulating bone defect surgery include:

[0122] The constructed 3D model of the bone defect is imported into the medical simulation environment. Based on the location, size and shape of the bone defect, the simulated surgical path of the bone defect is planned to formulate the corresponding simulated surgical plan for the bone defect.

[0123] The simulated surgical procedure was performed according to the simulated surgical plan for bone defects, and a simulated evaluation was conducted to determine the feasibility of the simulated surgical procedure. The simulated evaluation included checking the stability of the implanted bone defect repair, the repair status of the bone defect site, and the physiological response of the patient's bone tissue.

[0124] In this embodiment, the specific steps of surgical path planning include: visualizing a three-dimensional model of the bone defect in a medical simulation environment to comprehensively observe the location, size, and shape of the bone defect; analyzing the boundary, depth, and surface roughness of the bone defect to understand the severity of the defect and the difficulty of the surgery; then determining a suitable surgical entry point based on the location and shape of the bone defect; and finally simulating the path of surgical instruments from the entry point to the bone defect area in the medical simulation environment, taking into account avoiding important blood vessels and nerves; thereby improving the success rate of simulated surgical operations.

[0125] like Figure 4 The diagram shown is a structural schematic of the tumor-type bone defect reconstruction system provided in this application embodiment. The tumor-type bone defect reconstruction system provided in this application embodiment includes: a bone defect information acquisition module, a bone defect repair body construction module, a bone three-dimensional data acquisition module, and a bone defect surgery simulation module.

[0126] Among them, the bone defect information acquisition module is used to assess the patient's condition and obtain bone defect information. The condition assessment is used to quantify the severity of the patient's bone tumor and the patient's physical condition. The bone defect information includes the location information and the extent of infiltration of the bone defect.

[0127] The bone defect repair module is used to assess the anatomical structure of a patient's bone tumor using modern medical imaging and to construct a bone defect repair based on the corresponding bone defect information. The structural assessment is used to determine the structure and implantation location of the bone defect repair.

[0128] The bone 3D data acquisition module is used to design the bone defect prosthesis based on the preset prosthesis design data and acquire the patient's bone 3D data in real time. The repair design is used to repair the difference between the bone defect prosthesis and the patient's own bone structure. The bone 3D data includes bone morphology data, bone structure data and bone density data.

[0129] The bone defect surgery simulation module is used to construct a three-dimensional model of bone defects based on the acquired three-dimensional bone data and to perform simulated bone defect surgery. At the same time, it predicts and optimizes the implantation position, direction and angle of the bone defect repair. The three-dimensional model of bone defects is used to visualize the position of the bone defect repair and the relationship between the tissues surrounding the bone tumor.

[0130] In this embodiment, prior to the repair design, the bone defect repair is adjusted according to a pre-designed repair. Specific adjustment steps include: a comprehensive assessment of the patient's bone defect, including analyzing the size, shape, location, and surrounding bone structure of the defect; obtaining detailed three-dimensional data of the bone defect using medical imaging techniques (such as CT or MRI); comparing the pre-designed repair data with the patient's three-dimensional bone defect data to check the compatibility of the repair's shape, size, and material with the bone defect site; adjusting the shape of the repair according to the actual morphology of the bone defect, typically involving adjustments to the repair's edges, surface structure, and overall shape; and adjusting the size of the repair according to the size of the bone defect, as a repair that is too large or too small may affect the surgical outcome and the patient's recovery. This improves the accuracy and stability of the repair design.

[0131] The following points need to be explained:

[0132] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0133] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the present invention; that is, these drawings are not drawn to actual scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements.

[0134] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0135] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A tumor-type bone defect reconstruction system, characterized in that, include: The module includes a bone defect information acquisition module, a bone defect prosthesis construction module, a bone 3D data acquisition module, and a bone defect surgery simulation module. The bone defect information acquisition module is used to assess the patient's condition and acquire bone defect information. The condition assessment is used to quantify the severity of the patient's bone tumor and the patient's physical condition. The bone defect information includes the location information and infiltration range information of the bone defect. The bone defect repair module is used to perform structural assessment of the anatomical structure of the patient's bone tumor through modern medical imaging and to construct a bone defect repair in combination with the corresponding bone defect information. The structural assessment is used to determine the structure and implantation location of the bone defect repair. The bone 3D data acquisition module is used to design a bone defect prosthesis based on preset prosthesis design data and acquire the patient's bone 3D data in real time. The repair design is used to repair the difference between the bone defect prosthesis and the patient's own bone structure. The bone 3D data includes bone morphology data, bone structure data and bone density data. The bone defect surgery simulation module is used to construct a three-dimensional model of bone defects based on the acquired three-dimensional bone data and perform bone defect simulation surgery. At the same time, it predicts and optimizes the implantation position, direction and angle of the bone defect repair. The three-dimensional model of bone defects is used to visualize the position of the bone defect repair and the relationship between the tissues surrounding the bone tumor. The specific construction steps of the bone defect repair body are as follows: Based on the results of the disease assessment and in conjunction with the preset prosthesis factors, a corresponding virtual prosthesis is designed. The preset prosthesis factors include the matching degree of the surrounding bone, mechanical stability and biocompatibility. The implantation position, direction, and angle of the virtual prosthesis at the bone defect site are determined by using the obtained repair conformity coefficient and combining it with the results of structural assessment. The repair conformity coefficient is used to measure the degree of fit between the virtual prosthesis and the bone defect site. A virtual simulation surgery is performed on the virtual prosthesis, and the implantation effect of the virtual prosthesis is pre-evaluated. The pre-evaluation is used to obtain a numerical description of the fusion between the virtual prosthesis and the bone defect site. Based on the results of the pre-assessment, the virtual prosthesis is repaired and optimized to construct a bone defect prosthesis until the preset implantation effect is met; The specific steps for obtaining the repair compliance coefficient include: The surface morphology of the virtual prosthesis is compared with that of the bone defect site to obtain morphological difference data. The morphological difference data includes shape data and curvature data. The curvature data is used to numerically describe the degree of curvature of the bone defect surface. The matching degree between the volume of the virtual prosthesis and the volume of the bone defect site is analyzed and compared, and the virtual prosthesis is used in conjunction with the preset volume difference to determine whether the virtual prosthesis completely fills the bone defect area. The preset volume difference represents the maximum allowable volume between the virtual prosthesis and the bone defect site. The stress distribution values ​​after the virtual prosthesis is implanted into the bone defect site are obtained and the stress condition of the virtual prosthesis is evaluated. At the same time, the mechanical support effect of the virtual prosthesis on the bone defect site is evaluated. The stress distribution values ​​include the maximum allowable stress value and the minimum allowable stress value. The repair compliance coefficient is obtained by combining morphological difference data, analysis and comparison results, and evaluation results. The specific steps for evaluating the stress condition of the virtual restoration are as follows: A stress distribution map is generated based on the stress condition of the virtual prosthesis. The stress distribution map is used to visualize the stress concentration area where the virtual prosthesis and the surrounding bone experience stress. By analyzing the gradient changes at the intersection of the virtual prosthesis and the bone defect in the stress concentration region, the stress transmission and dispersion can be obtained. By combining the stress transmission and dispersion, abnormal stress areas in the stress distribution map are identified and marked, and corresponding solutions are formulated.

2. The tumor-type bone defect reconstruction system as described in claim 1, characterized in that, The repair compliance coefficient is calculated using the following formula: ， In the formula, j is the number of the bone defect site in the patient. X represents the total number of bone defects in the patient. Let be the number of the virtual prosthesis corresponding to the j-th bone defect site of the patient. , This represents the total number of virtual prostheses corresponding to the j-th bone defect site in the patient. This represents the repair conformity coefficient of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient, where e is a natural constant. This indicates the weighting of the relative deviation of the curvature data relative to the repair compliance coefficient. This represents the curvature data of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. A represents the reference deviation of the curvature data. This represents the average curvature data. This indicates the weighting of the relative volume deviation relative to the repair compliance coefficient. This represents the volume of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. This represents the volume corresponding to the j-th bone defect site. Indicates the preset volume difference. This indicates the weighting of the relative deviation of the stress distribution value relative to the repair compliance coefficient. This represents the maximum allowable stress value of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. This represents the minimum allowable stress value of the i-th virtual prosthesis corresponding to the j-th bone defect site in the patient. This indicates the reference deviation of the stress value corresponding to the bone defect site in the patient.

3. The tumor-type bone defect reconstruction system as described in claim 1, characterized in that, The specific steps for acquiring the 3D skeletal data include: Two-dimensional bone information is obtained by positioning and calibrating the patient's bone surface using a bone fixation device. The two-dimensional bone information includes geometric shape information and texture information. A three-dimensional image of the skeleton is generated by combining two-dimensional skeleton information. The three-dimensional image of the skeleton is used to visualize the morphological and structural features of the skeleton. Key information data is extracted from the three-dimensional image of the bone and registered and aligned with the preset prosthesis design data. The key information data is used to describe the morphological information, structural information and density information of the bone. Based on the registration and alignment results, the bone defect repair device is designed and the patient's three-dimensional bone data is obtained by combining the bone matching index. The bone matching index is used to measure the degree of matching between key information data and bone three-dimensional data.

4. The tumor-type bone defect reconstruction system as described in claim 3, characterized in that, The specific steps for obtaining the bone matching index are as follows: Analyze three-dimensional images of bones and extract internal structural data of bones from the three-dimensional images of bones. The internal structural data includes the shape data of the medullary cavity and the porosity data of the bones. The extracted key information data is combined to spatially align the internal structural data of the skeleton with the three-dimensional data of the skeleton. Within a preset time period, feature point matching is performed between the 3D skeletal data and key information data until the obtained skeletal matching index is equal to the preset skeletal matching index. Otherwise, feature point matching is performed again. Feature point matching means registering and aligning the corner points and edge points in the key information data and the 3D skeletal data. The skeletal matching index is calculated using the following formula: ， In the formula, m represents the number of feature point matching operations. M represents the total number of feature point matches. Let e ​​represent the skeleton matching index of the m-th feature point matching, where e is the natural constant. A correction factor representing the relative deviation of skeletal morphology data. This represents the skeletal morphology reference data corresponding to the m-th feature point matching. This represents the skeletal morphology data in the 3D skeletal data corresponding to the m-th feature point matching. This represents reference data for skeletal morphology. A correction factor representing the relative deviation of skeletal structure data. This represents the skeletal structure reference data corresponding to the m-th feature point matching. This represents the skeletal structure data in the 3D skeletal data corresponding to the m-th feature point matching. This represents reference data for skeletal structure. A correction factor representing the relative deviation of bone density data. This represents the bone density reference data corresponding to the m-th feature point matching. This represents the bone density data in the 3D bone data corresponding to the m-th feature point matching. This represents reference data for bone density.

5. The tumor-type bone defect reconstruction system as described in claim 1, characterized in that, The specific steps for constructing the three-dimensional model of the bone defect include: The acquired 3D bone data is standardized to obtain 3D data of the bone defect site, which is then imported into medical image processing software. The patient's bone tissue and bone defect sites are encoded and edge detection is performed according to edge detection indicators until the edge detection results meet the preset edge detection indicators. The edge detection is used to detect the boundary of the patient's bone tissue and the boundary of the bone defect site. The edge detection indicators are used to measure the degree of matching between the boundary of the patient's bone tissue and the boundary of the bone defect site. The three-dimensional data of the bone defect site is compared and verified with the preset medical imaging data, and a three-dimensional model of the bone defect is constructed using a three-dimensional reconstruction algorithm.

6. The tumor-type bone defect reconstruction system as described in claim 5, characterized in that, The specific method for obtaining the edge detection index is as follows: The pixels belonging to the edge points in the bone defect area are obtained from the edge detection results and marked to obtain the boundary fit coefficient and boundary similarity coefficient. The boundary fit coefficient represents the ratio of the boundary fit deviation to the corresponding reference deviation. The boundary fit is used to measure the degree of fit between the boundary of the patient's bone tissue and the boundary of the bone defect area. The boundary similarity coefficient represents the ratio of the boundary similarity deviation to the corresponding reference deviation. The boundary similarity is used to measure the degree of similarity between the boundary of the patient's bone tissue and the boundary of the bone defect area. Edge detection metrics are obtained by combining the obtained boundary fit coefficient and boundary similarity coefficient with boundary accuracy and detection environment metrics. The boundary accuracy is used to measure the accuracy between the boundary of the patient's bone tissue and the boundary of the bone defect site. The detection environment metrics are used to quantify the influence of environmental conditions on edge detection.

7. The tumor-type bone defect reconstruction system as described in claim 1, characterized in that, The specific process of the simulated surgery for bone defects includes: The constructed 3D model of the bone defect is imported into the medical simulation environment. Based on the location, size and shape of the bone defect, the simulated surgical path of the bone defect is planned to formulate the corresponding simulated surgical plan for the bone defect. The simulated surgical procedure was performed according to the simulated surgical plan for bone defects, and a simulated evaluation was conducted to determine the feasibility of the simulated surgical procedure. The simulated evaluation included checking the stability of the implanted bone defect prosthesis, the repair status of the bone defect site, and the physiological response of the patient's bone tissue.

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