A method, apparatus and medium for simulating fracture healing based on growth factor action

Abstract

The application discloses a fracture healing simulation method and device based on growth factor action and a medium, relates to the technical field of digital twinning, and comprises the following steps: simulating the tissue differentiation and callus evolution process of a tissue repair area by using growth factors and a twinning field, feeding back to a mechanical state parameter for synchronous updating, and generating a callus tissue evolution field; constructing an actual healing track by using fracture image data and patient weight data collected during fracture healing, performing deviation matching on the actual healing track and the callus tissue evolution field, deducing the growth factor release position, action range and activity change, generating a growth factor hidden source calibration field, correcting a fracture healing basic twinning body according to the growth factor hidden source calibration field, carrying out multi-scheme healing deduction and simulation verification, and generating a fracture healing simulation result set. The application improves the dynamic calibration capability and tissue evolution simulation precision of the fracture healing digital twinning body by constructing the growth factor hidden source calibration field.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method, device and medium for simulating fracture healing based on the effects of growth factors. Background Technology

[0002] With the development of digital twin technology, computational biology, and biomechanical analysis techniques, digital simulation of the fracture healing process has gradually become an important research direction in the field of orthopedics. Related technologies typically utilize medical imaging data to construct a digital representation of the fracture area and combine it with mechanical environment analysis and tissue repair principles to dynamically simulate callus formation, tissue remodeling, and functional recovery during the fracture healing process, thus providing technical support for the digital reconstruction and tissue evolution simulation of the fracture healing process.

[0003] However, existing fracture healing simulation technologies mainly focus on tissue structure and mechanical changes, lacking an effective expression of the dynamic evolution of the synergistic and mutually restrictive effects among growth factors. This makes it difficult to accurately reflect the impact of synergistic regulation of growth factors on the tissue repair process, resulting in the inability to realistically depict the biological regulatory characteristics during fracture healing, thus affecting the accuracy and individualized adaptability of fracture healing simulation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a fracture healing simulation method based on the role of growth factors to solve the problem of the difficulty in dynamically representing the synergistic regulatory relationship of growth factors during fracture healing simulation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, this invention provides a fracture healing simulation method based on the effects of growth factors. The method includes: acquiring fracture imaging data and patient weight-bearing data; extracting tissue repair characteristics, mechanical response characteristics, and corresponding growth factor activity characteristics; establishing a dynamic mapping relationship between tissue state parameters, growth factor state parameters, and mechanical state parameters; performing dynamic twin modeling of the fracture area; constructing a basic twin of fracture healing; simulating the dynamic evolution process of growth factors through the basic twin of fracture healing; establishing a synergistic regulatory relationship between different growth factors; quantitatively characterizing the changes in the synergistic regulatory relationship; and generating growth factors. A co-epitrophic field is used to simulate the tissue differentiation and callus evolution process in the tissue repair area, and the results are fed back to the mechanical state parameters for synchronous updates, generating a callus tissue evolution field. The actual healing trajectory is constructed using fracture imaging data and patient weight-bearing data continuously collected during fracture healing. The actual healing trajectory is then matched with the callus tissue evolution field to determine the deviation, and the release location, range of action, and activity changes of growth factors are deduced, generating a growth factor cryptogenic calibration field. Based on the growth factor cryptogenic calibration field, the basic twin of fracture healing is corrected, and multiple healing scenarios are simulated and validated, generating a fracture healing simulation result set.

[0007] As a preferred embodiment of the fracture healing simulation method based on growth factor activity described in this invention, the extraction of tissue repair characteristics, mechanical response characteristics, and corresponding growth factor activity characteristics specifically includes: Fracture imaging data is segmented into fracture regions and reconstructed into three-dimensional bone structures to locate fracture gaps and callus formation areas. Tissue repair features are extracted by analyzing the continuity of the three-dimensional bone structure and the spatial distribution of callus. Force transmission and strain evolution analysis were performed on the patient's weight-bearing data to simulate the mechanical evolution process in the fracture area and extract mechanical response characteristics; Based on the changes in tissue repair characteristics at different time points, the changes in growth factor activity corresponding to the progress of tissue repair in the fracture area were tracked, and the corresponding growth factor activity characteristics were extracted.

[0008] As a preferred embodiment of the fracture healing simulation method based on the action of growth factors described in this invention, the construction of the basic twin for fracture healing specifically includes: Tissue repair characteristics are mapped to tissue state parameters, mechanical response characteristics are mapped to mechanical state parameters, and growth factor activity characteristics are mapped to growth factor state parameters. Based on the dynamic relationship between tissue state parameters, growth factor state parameters and mechanical state parameters, a state coupling network is constructed. The tissue repair process, growth factor regulation process and mechanical change process in the fracture area are synchronously and dynamically simulated through the state coupling network to construct a basic twin of fracture healing.

[0009] As a preferred embodiment of the fracture healing simulation method based on the action of growth factors described in this invention, the generation of the growth factor synergistic twin field specifically includes: By utilizing the state parameters of growth factors in the fracture healing twin, the release, diffusion, and activity changes of growth factors are continuously simulated to form a dynamic evolution process of growth factors. Based on the dynamic evolution process of growth factors, the synergistic and mutually restrictive effects of different growth factors in the tissue repair area are analyzed, forming a synergistic regulatory relationship among different growth factors; The intensity and evolution trend of the synergistic regulatory relationship among different growth factors in different tissue repair regions were quantitatively analyzed to form the synergistic regulatory change state; Co-evolutionary analysis of the changes in the synergistic regulation state is performed and mapped to the corresponding tissue repair region of the fracture healing basal twin, generating a growth factor synergistic twin field.

[0010] As a preferred embodiment of the fracture healing simulation method based on the action of growth factors described in this invention, the generation of the callus tissue evolution field specifically includes: By using growth factor synergistic twin field to regionalize the intensity of bioregulation in tissue repair areas, the differentiation tendency of tissue repair areas is identified, and the tissue differentiation process and callus formation range are dynamically deduced based on the differentiation tendency, thus forming a tissue differentiation evolution field. Based on the tissue differentiation and evolution field, the generation and expansion process of callus in the tissue repair area is dynamically simulated, and the structural continuity, maturity and spatial coverage of callus are updated simultaneously to form a callus structure evolution field. Based on the evolution field of the callus structure, feedback analysis is performed on the local load-bearing path, stiffness changes and mechanical transmission process of the fracture area, and the mechanical state parameters are updated simultaneously to generate the callus tissue evolution field.

[0011] As a preferred embodiment of the fracture healing simulation method based on the action of growth factors described in this invention, the construction of the actual healing trajectory specifically includes: The fracture imaging data continuously acquired during the fracture healing period were integrated temporally and reconstructed in three dimensions. Based on the patient's weight-bearing data, the strain and displacement changes in the fracture area were analyzed to form a sequence of structural and mechanical response evolution of the fracture area. A comprehensive analysis of the evolution sequence of fracture area structure and mechanical response is conducted to identify callus formation areas and bone tissue repair progress, and to generate a tissue repair evolution state sequence. By establishing a temporally continuous spatial correlation between the tissue repair evolution state sequence, the actual healing trajectory can be constructed.

[0012] As a preferred embodiment of the fracture healing simulation method based on the action of growth factors described in this invention, the generation of the growth factor hidden source calibration field specifically includes, Spatiotemporal correlation analysis was performed on the actual healing trajectory and the callus tissue evolution field to generate state correlation relationships. The state evolution information in the callus tissue evolution field was then mapped to the actual healing trajectory using the state correlation relationships to form a healing deviation distribution field. Based on the healing deviation distribution field, the tissue evolution path of the deviation area in the callus tissue evolution field is traced, and the abnormal situation of growth factor synergistic regulation in the deviation area is analyzed to form a growth factor regulation deviation field. Based on the inverse deduction of the growth factor regulation deviation field, the release location, range of action and activity changes of growth factors that were not directly observed are reconstructed, the distribution state of latent growth factors is reconstructed, and a latent source calibration field of growth factors is generated.

[0013] As a preferred embodiment of the fracture healing simulation method based on the effects of growth factors described in this invention, the generation of the fracture healing simulation result set specifically includes: Based on the growth factor crypto-source calibration field, the tissue state parameters, growth factor state parameters, and mechanical state parameters in the fracture healing basal twin are dynamically corrected, and the corrected parameters are written into the corresponding tissue repair area to generate the calibrated fracture healing basal twin. In the calibrated fracture healing baseline twin, the callus evolution process, tissue differentiation process and mechanical response under different intervention conditions are simultaneously simulated to form a multi-scheme healing simulation field; The evolution trend of callus, tissue state evolution and mechanical changes in the multi-scheme healing simulation field are continuously recorded and integrated to generate a set of fracture healing simulation results.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the fracture healing simulation method based on the action of growth factors as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the fracture healing simulation method based on the action of growth factors as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By continuously analyzing the synergistic and mutually restrictive effects among different growth factors and quantitatively characterizing the changes in the synergistic regulatory relationship, a growth factor synergistic twin field reflecting the biological regulatory characteristics of the fracture area is formed. This can continuously characterize the changes in the intensity of the synergistic effect of growth factors during tissue repair and the regulatory differences between different tissue repair areas. This allows the dynamic regulatory behavior of growth factors to be spatially expressed and continuously tracked, thereby providing a more biologically consistent regulatory basis for tissue differentiation and callus evolution, and improving the accuracy of the expression of biological regulatory information in the fracture healing process and the authenticity of the evolution process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0018] Figure 1 This is a flowchart of a fracture healing simulation method based on the effects of growth factors; Figure 2 Flowchart for constructing a basic twin for fracture healing; Figure 3 A flowchart for generating the callus tissue evolution field; Figure 4 A flowchart for generating the simulation results set of fracture healing. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Reference Figures 1-4This is one embodiment of the present invention, which provides a fracture healing simulation method based on the action of growth factors, including the following steps: S1. Collect fracture imaging data and patient weight-bearing data, extract tissue repair characteristics, mechanical response characteristics and corresponding growth factor activity characteristics, establish dynamic mapping relationships between tissue state parameters, growth factor state parameters and mechanical state parameters, perform dynamic twin modeling of the fracture area, and construct a basic twin for fracture healing.

[0023] S1.1 Perform fracture region segmentation and three-dimensional reconstruction of bone tissue structure on fracture imaging data, locate fracture gap and callus formation area, and extract tissue repair features by analyzing the continuity of bone tissue three-dimensional structure and the spatial distribution of callus.

[0024] Specifically, three-dimensional voxel segmentation is performed on the fracture image data to distinguish the fracture area from the surrounding normal bone tissue. The gray-level distribution histogram of the fracture image data is used to statistically analyze the gray-level peak distribution corresponding to bone tissue, callus tissue, soft tissue, and fracture gap. A multi-threshold segmentation method is used to obtain the valley positions between adjacent gray-level peaks as gray-level interval boundaries. Image thresholding is then performed on the fracture image data according to these gray-level interval boundaries. In the image thresholding results, the marked bone tissue boundary points within the fracture area are used as region growth seed points. The fracture area contour is expanded by combining the gray-level differences between adjacent pixels and spatial connectivity. The level set method is used to smooth and correct the fracture area contour boundaries, resulting in different layers of fracture area contours. The different layers of fracture area contours are then segmented according to the image acquisition slice thickness, slice spacing, and... Initial alignment is performed using spatial coordinate information. Then, rigid registration based on mutual information is used to correct interlayer translation and rotation deviations, and non-rigid registration is used to correct local deformation deviations, ensuring that the contours of adjacent fracture regions correspond continuously in spatial position. Contour point matching and boundary interpolation are performed on the registered contours of adjacent fracture regions, and the moving cube method is used to reconstruct the three-dimensional surface of the voxel boundary formed by the continuous contours, generating a single, continuous three-dimensional bone tissue structure. In the three-dimensional bone tissue structure, regions of continuous bone tissue interruption are searched, and the location of fracture gaps is marked. At the same time, the expansion range of high-density new tissue within the fracture region is tracked, and the callus formation region is marked. Tissue repair features are extracted by combining the changes in the regions of continuous bone tissue interruption, the recovery of bone tissue connection, and the expansion of the callus formation region.

[0025] It should also be noted that fracture imaging data is acquired by scanning the fracture area at multiple time points using CT or X-ray equipment during the fracture healing period according to the clinical follow-up plan, and the image information at each time point is saved in the scanning order to form a continuous image sequence; image thresholding is to divide the image grayscale into different levels according to the differences in bone tissue density, with each level corresponding to a specific range of bone tissue density. For example, low-density areas represent fracture gaps or cartilage tissue, medium-density areas represent incompletely healed bone tissue, and high-density areas represent mature bone tissue, in order to distinguish between callus formation areas and fracture gaps; the extension range refers to the spatial distribution range of callus gradually extending from the initial formation site to the surrounding bone tissue within the fracture area.

[0026] S1.2 Perform force transmission analysis and strain evolution analysis on the patient's weight-bearing data to simulate the evolution of mechanical action in the fracture area and extract mechanical response characteristics.

[0027] Specifically, the patient's weight-bearing data was processed as a time series to obtain the magnitude and direction of the weight-bearing at different times. Individualized skeletal geometry was reconstructed based on fracture imaging data, and an individualized skeletal biomechanical analysis model corresponding to the fracture area was established in conjunction with the patient's weight-bearing data. The proximal and distal bone segments, callus tissue, cartilage tissue, and fracture gap were divided into different material regions, and material properties were assigned according to the elastic modulus, Poisson's ratio, and density of bone tissue, callus tissue, cartilage tissue, and fibrous tissue at different healing stages. The magnitude and direction of the weight-bearing data were used as external load inputs, and joint contact force, muscle tension force, and support reaction force were used as... Boundary conditions are established, and displacement and contact constraints are set based on the constraint relationship between adjacent bone segments above and below the fracture area. The stress, strain, and displacement response of each spatial location in the fracture area under load are calculated through finite element analysis to form the force distribution of the fracture area. The force distribution transmission path of the fracture area is traced along the three-dimensional structure of the bone tissue, and the displacement and strain states of each location in the fracture area at different load times are recorded along the three-dimensional structure of the bone tissue. By tracking the displacement and strain change trends of each location time by time, the mechanical response evolution process of the fracture area under load is simulated. The force distribution, transmission path, displacement change, and strain change of the fracture area are used as the mechanical response content to extract mechanical response characteristics.

[0028] It should also be noted that the patient's weight-bearing data was obtained by monitoring the changes in the patient's lower limb load and walking load using pressure sensors during the fracture healing period, and the magnitude and direction of the weight-bearing at different time points were recorded according to the preset follow-up cycle. The follow-up cycle was determined based on the changes in the tissue repair stage during the fracture healing process and was set to different time points after the operation in correspondence with the clinical re-examination cycle, so as to ensure that the patient's weight-bearing data can cover the mechanical changes during the callus formation period, the callus remodeling period, and the bone tissue remodeling period.

[0029] S1.3. Based on the changes in tissue repair characteristics at different time points, track the changes in growth factor activity corresponding to the progress of tissue repair in the fracture area, and extract the corresponding growth factor activity characteristics.

[0030] Specifically, tissue repair characteristics are arranged chronologically to form a sequence of tissue repair characteristic changes. The evolution of fracture gap closure, bone tissue connection, and callus coverage is tracked within this sequence, and the changes in growth factor activity corresponding to the progress of tissue repair in the fracture area are determined. Based on these changes, the activity, intensity, and synergistic effects of the corresponding growth factors are obtained. These changes are then correlated and characterized to form growth factor activity characteristics.

[0031] S1.4 Map tissue repair characteristics to tissue state parameters, mechanical response characteristics to mechanical state parameters, and growth factor activity characteristics to growth factor state parameters.

[0032] Specifically, the changes in fracture gap closure, bone tissue connection, and callus coverage in tissue repair characteristics are categorized and organized, and corresponding status labels are assigned according to the progress of tissue repair to form tissue status parameters; the force distribution, transmission path, displacement changes, and strain changes in mechanical response characteristics are categorized and organized, and corresponding status labels are assigned according to the changes in mechanical action to form mechanical status parameters; the changes in activity, intensity of action, and synergistic effects in growth factor activity characteristics are categorized and organized, and corresponding status labels are assigned according to the changes in growth factor regulation to form growth factor status parameters.

[0033] S1.5. Based on the dynamic relationship between tissue state parameters, growth factor state parameters and mechanical state parameters, a state coupling network is constructed. The tissue repair process, growth factor regulation process and mechanical change process in the fracture area are synchronously and dynamically simulated through the state coupling network to construct a basic twin of fracture healing.

[0034] Specifically, the tissue state parameters, growth factor state parameters, and mechanical state parameters corresponding to each spatial location in the fracture area are used as network nodes, and the state information of each network node changes over time is recorded. Mechanistic connections are established between network nodes, based on tissue interactions, growth factor regulatory effects, and mechanical transmission paths at adjacent spatial locations. The initial connection weights are determined based on historical fracture evolution data. Using the state change data of network nodes at continuous time points, the direction, amplitude, and duration of state changes of adjacent nodes with mechanistic connections are statistically analyzed in each time interval. The number of times the direction is consistent, the number of times the amplitude is close, and the number of times the duration overlaps are collected to form node synchronization change information. Based on the state change characteristics recorded in the node synchronization change information, the strength of tissue interactions, the strength of growth factor regulation, and the mechanical transmission paths corresponding to the mechanistic connections are updated. The intensity of the action is transmitted and the updated intensity is converted into corresponding connection weights according to its relative proportion in all connections, forming updated connection weights. The updated connection weights are written into the state coupling network to characterize the dynamic influence of tissue interaction, growth factor regulation, and mechanical transmission between nodes in a continuous time series. In the calibrated continuous network structure, the updated connection weights are used to correlate and synchronously update the tissue state parameters, growth factor state parameters, and mechanical state parameters between network nodes, using the node state of the previous time node as the input of the next time node to form a multidimensional coupling relationship between tissue state parameters, growth factor state parameters, and mechanical state parameters. The node state update is advanced along the time series to continuously simulate the evolution of tissue state, growth factor state, and mechanical state in the fracture area, constructing a basic twin of fracture healing.

[0035] S2. By simulating the dynamic evolution process of growth factors through a basic twin of fracture healing, a synergistic regulatory relationship between different growth factors is established, the changing state of the synergistic regulatory relationship is quantitatively characterized, and a synergistic twin field of growth factors is generated.

[0036] S2.1. Using the growth factor state parameters in the fracture healing twin, the release, diffusion and activity changes of growth factors are continuously simulated to form a dynamic evolution process of growth factors.

[0037] Specifically, by utilizing the growth factor state parameters in the fracture healing twin, the release amount and activity level of growth factors at each spatial location within the fracture area are tracked over time. The diffusion process and concentration changes of growth factors along the fracture area are simulated at continuous time points, and the change trend of growth factor activity over time is recorded in different tissue repair areas, forming a dynamic evolution process of growth factors that reflects the dynamic evolution characteristics of growth factors within the fracture area.

[0038] It should also be noted that the amount of growth factor released refers to the number of growth factors released from a specific location in the fracture area and entering the surrounding tissue environment per unit time; the activity level refers to the strength of the ability of growth factors to participate in tissue repair regulation and exert biological effects at the corresponding time point.

[0039] S2.2. Based on the dynamic evolution process of growth factors, analyze the synergistic and mutually restrictive effects of different growth factors in the tissue repair area, and form a synergistic regulatory relationship among different growth factors.

[0040] Specifically, the activity changes, intensity changes, and corresponding tissue repair status changes of different growth factors within the same tissue repair region were extracted during the dynamic evolution of growth factors. The correlation between changes in the activity of multiple growth factors and changes in tissue repair status was analyzed. When changes in the activity of multiple growth factors collectively correspond to increased callus formation, enhanced tissue connectivity, or accelerated tissue repair progress, the regulatory relationship between the corresponding growth factors was characterized as a synergistic promoting effect. When the increased activity of one growth factor corresponds to a weakened effect of another growth factor, restricted tissue repair progress, or inhibition of tissue differentiation, the regulatory relationship between the corresponding growth factors was characterized as a mutual restrictive effect. The synergistic promoting effect and the mutual restrictive effect were correlated in chronological order to form a synergistic regulatory relationship between different growth factors.

[0041] It should also be noted that the synergistic promoting effect is obtained based on the joint promoting relationship between changes in the activity of multiple growth factors and changes in tissue repair status. When changes in the activity of multiple growth factors jointly correspond to increased callus formation, enhanced bone tissue connection, or increased tissue repair speed, it indicates that there is a synergistic promoting effect between the corresponding growth factors. The mutual restraining effect is obtained based on the inhibitory relationship between changes in the activity of multiple growth factors and changes in tissue repair status. When a change in the activity of one growth factor corresponds to a weakened regulatory effect of another growth factor, a reduced tissue repair speed, or inhibited tissue differentiation, it indicates that there is a mutual restraining effect between the corresponding growth factors.

[0042] The tissue repair area refers to the local bone tissue space within the fracture area that participates in callus formation, bone tissue remodeling, and functional recovery. It includes the injured bone tissue around the fracture gap, the callus-forming area, and the repair tissue adjacent to the callus. It is used to record and reflect the evolution characteristics of growth factor effects, tissue differentiation, and mechanical state parameters over time.

[0043] S2.3 Quantitatively analyze the intensity and evolution trend of the synergistic regulatory relationship between different growth factors in different tissue repair areas to form a synergistic regulatory change state.

[0044] Specifically, corresponding synergistic regulatory relationships are extracted from different tissue repair regions, and the changes in synergistic promotion and mutual restraint over time are recorded. The strengthening, weakening, and stable changes of synergistic promotion and mutual restraint are tracked along the time series to characterize the intensity of changes in synergistic regulatory relationships. The direction of change of synergistic promotion and mutual restraint between consecutive time nodes is tracked simultaneously to characterize the evolution trend of synergistic regulatory relationships. The intensity of change and the evolution trend are correlated to form the state of synergistic regulatory change.

[0045] S2.4 Perform co-evolution analysis on the parameterized state of growth factor state in the corresponding tissue repair region, and map it to the corresponding tissue repair region of the fracture healing basal twin to generate a growth factor co-twin field.

[0046] Specifically, the intensity and evolution trend of changes in the synergistic regulation state are correlated and tracked to obtain the stability and dominant direction of synergistic regulation of growth factors in different tissue repair regions. The stability and dominant direction are spatially correlated with the corresponding tissue repair regions in the fracture healing basal twin, and synergistic regulation characterization information of the corresponding tissue repair regions is formed. The synergistic regulation characterization information is used to update the growth factor state parameters of the corresponding tissue repair regions, so that different tissue repair regions form a synergistic regulation distribution of growth factors with spatial differences and temporal continuity, generating a growth factor synergistic twin field.

[0047] S3. The growth factor-assisted twin field is used to simulate the tissue differentiation and callus evolution process in the tissue repair area, and the mechanical state parameters are fed back for synchronous update to generate a callus tissue evolution field.

[0048] S3.1. Utilize the growth factor synergistic twin field to perform regional analysis on the bioregulatory intensity of the tissue repair area, identify the differentiation tendency of the tissue repair area, and dynamically deduce the tissue differentiation process and callus formation range based on the differentiation tendency to form a tissue differentiation evolution field.

[0049] Specifically, by utilizing the distribution information of growth factor state parameters in the growth factor synergistic twin field, the intensity and duration of growth factor action at different locations within the tissue repair area are extracted, and the distribution of regulatory differences at each location within the tissue repair area is determined by the changing trends of the intensity and duration of action. The distribution of regulatory differences is matched with a preset correspondence of tissue differentiation to obtain the tissue differentiation tendency. The changes in the tissue differentiation tendency at the locations corresponding to the regulatory differences are tracked along the time series to promote the gradual expansion of tissue differentiation in space, and the dynamic evolution of the callus formation range is recorded simultaneously. The tissue differentiation process is continuously correlated with the callus formation range to form a tissue differentiation evolution field.

[0050] It should also be noted that the correspondence between tissue differentiation and the corresponding stages of tissue repair during fracture healing is established in advance based on the correspondence between the intensity of regulation of different growth factors and the tissue repair stages, as well as the expression characteristics of growth factors during the formation of different tissue types. The correspondence between tissue differentiation and the corresponding relationship between different combinations of growth factor regulation intensities and the direction of tissue differentiation is used to characterize the relationship between different combinations of growth factor regulation intensities and the direction of tissue differentiation. Among them, high osteogenic regulation intensity corresponds to bone tissue differentiation tendency, high cartilage regulation intensity corresponds to cartilage tissue differentiation tendency, high fibrous repair regulation intensity corresponds to fibrous tissue differentiation tendency, and when the growth factor regulation intensity is in a transitional state, it corresponds to the differentiation tendency of the tissue transformation stage.

[0051] S3.2. Based on the tissue differentiation and evolution field, the generation and expansion process of callus in the tissue repair area is dynamically simulated, and the structural continuity, maturity and spatial coverage of the callus are updated simultaneously to form a callus structural evolution field.

[0052] Specifically, based on the tissue differentiation process and callus formation range in the tissue differentiation evolution field, the generation and expansion of callus within the tissue repair area are advanced along the time series, and the changes in callus boundaries over time are recorded. During the callus formation and expansion process, the connection changes between adjacent callus regions are tracked to characterize the structural continuity of the callus. At the same time, the changes in the transformation of internal tissues into mature bone tissue are recorded to characterize the maturity of the callus, and the changes in the coverage of the callus within the tissue repair area are recorded to characterize the spatial coverage of the callus. The structural continuity, maturity, and spatial coverage are continuously correlated with the callus formation and expansion process to form a callus structural evolution field.

[0053] S3.3. Based on the callus structure evolution field, feedback analysis is performed on the local load-bearing path, stiffness change and mechanical transmission process of the fracture area, and the mechanical state parameters are updated simultaneously to generate the callus tissue evolution field.

[0054] Specifically, based on the structural continuity, maturity, and spatial coverage in the callus evolution field, different regions of the callus, bone tissue, and unconnected callus areas are classified as different tissue types. Corresponding material properties and stiffness parameters are assigned based on the elastic modulus, Poisson's ratio, and load-bearing capacity of each tissue type at the current healing stage. Loads and boundary conditions corresponding to the patient's weight-bearing data are applied, and based on material properties, stiffness parameters, and load transfer relationships, the loads corresponding to the patient's weight-bearing data are applied to the fracture area. Force and displacement constraints are set according to the connection relationships between the callus, bone tissue, and unconnected callus areas. Based on the force transfer relationships between regions under load, the load transfer process in the fracture area between the bone tissue, callus, and unconnected callus areas is continuously tracked, and the force response of each region is recorded in conjunction with material properties, stiffness parameters, and force constraint conditions. During load transfer, the system dynamically updates the data based on the displacement, strain, and force changes corresponding to each region. The stress state at various locations within the fracture area is determined to obtain the stress distribution, strain distribution, and force transmission direction at each location. Based on the stress changes at each location within the fracture area, the stress distribution, strain distribution, and force transmission direction are obtained. The continuous force transmission area from one side of the bone tissue to the other is traced according to the stress distribution, strain distribution, and force transmission direction, and this continuous force transmission area is mapped as a local load-bearing path. Changes in the stress concentration location, stress diffusion range, and force transmission direction during callus maturation are recorded along the local load-bearing path. The stiffness changes in the fracture area are characterized by changes in load-bearing capacity corresponding to the structural continuity enhancement process and spatial coverage expansion process. The stiffness changes and mechanical transmission process are fed back to the mechanical state parameters, and the stress distribution, transmission path, displacement changes, and strain changes in the mechanical state parameters are updated synchronously. The callus structure evolution field is then continuously correlated with the updated mechanical state parameters over time to generate a callus tissue evolution field.

[0055] S4. Construct the actual healing trajectory using fracture imaging data and patient weight-bearing data continuously collected during fracture healing, match the actual healing trajectory with the callus tissue evolution field, and deduce the release location, range of action, and activity changes of growth factors to generate a growth factor crypto-source calibration field.

[0056] S4.1. The fracture imaging data continuously collected during the fracture healing period are integrated in time and reconstructed in three dimensions. Based on the patient's weight-bearing data, the strain and displacement changes in the fracture area are analyzed to form a sequence of structural and mechanical response evolution of the fracture area.

[0057] Specifically, fracture imaging data continuously acquired during fracture healing are arranged chronologically to form a continuous image time series. Three-dimensional spatial registration and contour stitching are performed on the fracture area images at each time point to reconstruct the changes in the three-dimensional structure of the fracture area over time. The fracture gap and callus growth position are marked in the three-dimensional structure of the fracture area, and the displacement and strain changes at each position are tracked along the time series. Combined with the patient's weight-bearing data, the force direction and magnitude of the fracture area at different weight-bearing times are mapped to the three-dimensional structure of the fracture area and continuously matched with the displacement and strain changes to form a sequence of structural and mechanical response evolution of the fracture area.

[0058] S4.2. Conduct a comprehensive analysis of the evolution sequence of fracture area structure and mechanical response, identify callus formation area and bone tissue repair progress, and generate tissue repair evolution state sequence.

[0059] Specifically, the changes in fracture gap, bone tissue connection, displacement, and strain in the structural and mechanical response evolution sequence of the fracture area are correlated and organized in chronological order. The process of fracture gap reduction, bone tissue connection enhancement, and mechanical response changes in the fracture area are tracked along the time sequence, and the location range of continuous expansion of new bone tissue is marked to form the callus formation area. The callus formation area, fracture gap reduction, bone tissue connection enhancement, and mechanical response changes in the fracture area are continuously correlated to characterize the evolution of bone tissue repair over time and generate a tissue repair evolution state sequence.

[0060] It should also be noted that the range of continuous expansion of new bone tissue refers to the spatial coverage area in which newly formed bone tissue gradually extends and connects with surrounding bone tissue around the fracture gap during the fracture healing process. Specifically, it includes the connection area between bone tissue on both sides of the fracture gap, the callus formation area, and the continuous coverage area formed by the extension of callus to adjacent bone tissue.

[0061] S4.3. Perform temporal and spatial correlation on the sequence of tissue repair evolution states to construct the actual healing trajectory.

[0062] Specifically, the callus formation area, fracture gap reduction process, bone tissue connection enhancement process, and mechanical response change process of the fracture area in the tissue repair evolution sequence are arranged in chronological order; the positional changes of the callus formation area, fracture gap reduction and bone tissue connection between adjacent time points are tracked, and the corresponding spatial positions are kept continuously correlated; the tissue repair state and spatial position changes under continuous time points are connected in series to construct the actual healing trajectory.

[0063] S4.4 Perform spatiotemporal correlation analysis on the actual healing trajectory and the callus tissue evolution field to generate state correlation relationships. Then, use the state correlation relationships to map the state evolution information in the callus tissue evolution field to the actual healing trajectory to form a healing deviation distribution field.

[0064] Specifically, the tissue repair state and spatial position changes in the actual healing trajectory are correlated with the state evolution information in the callus tissue evolution field according to time nodes and fracture area locations to form a state correlation relationship. The callus evolution state, tissue differentiation evolution state, and mechanical state parameters in the callus tissue evolution field are mapped to the corresponding positions in the actual healing trajectory using the state correlation relationship. At the same time node, the mapping results are correlated and matched with the corresponding states of the actual healing trajectory to obtain the changes and deviations of the callus evolution state, tissue differentiation evolution state, and mechanical state parameters. The changes and deviations are written into the corresponding fracture area locations to form a healing deviation distribution field.

[0065] S4.5. Based on the healing deviation distribution field, trace the tissue evolution path of the deviation area in the callus tissue evolution field, and analyze the abnormal situation of growth factor synergistic regulation in the deviation area to form a growth factor regulation deviation field.

[0066] Specifically, the deviation area is located based on the healing deviation distribution field, and the deviation area is mapped back to the corresponding position in the callus tissue evolution field. The tissue differentiation and evolution state and the callus evolution state at the corresponding position are tracked along the time series to form the tissue evolution path of the deviation area in the callus tissue evolution field. The tissue evolution path is correlated with the synergistic regulation change state in the growth factor synergistic twin field, and the changes of weakened synergistic promotion, enhanced mutual restraint, and synergistic evolution imbalance in the deviation area are extracted to form the growth factor regulation deviation field.

[0067] S4.6. Based on the growth factor regulation deviation field, reverse the deduction of the release location, range of action and activity changes of growth factors that were not directly observed, reconstruct the distribution state of latent growth factors, and generate a latent source calibration field for growth factors.

[0068] Specifically, based on the changes in the synergistic promotion effect weakening, the mutual restraint effect strengthening, and the synergistic evolution imbalance in the growth factor regulatory deviation field, the source of growth factor regulatory changes at corresponding locations is traced back along the tissue evolution path. The location where the synergistic regulatory changes first appear is tracked along the tissue evolution path, and the corresponding location is determined as the unobserved growth factor release location. The expansion of the growth factor regulatory deviation in adjacent tissue repair areas is tracked around the growth factor release location to estimate the unobserved growth factor range of action. A growth factor activity change index is constructed using the change intensity corresponding to the growth factor release location, the expansion intensity corresponding to the growth factor range of action, and the evolutionary deviation intensity corresponding to the tissue evolution path. The expression is: ; Where A represents the growth factor activity change index, and P represents the intensity of change corresponding to the location of growth factor release. This indicates the expansion intensity corresponding to the range of action of the growth factor. This indicates the intensity of evolutionary deviation corresponding to the organizational evolution path. Indicates the weighting of changes in the location of growth factor release. Indicates the weighting of the extended scope of action of growth factors. The weights of deviations in organizational evolution paths are represented; they are determined using a normalization method. , and This ensures that the contributions of the intensity of changes corresponding to the release location of growth factors, the intensity of expansion corresponding to the range of action of growth factors, and the intensity of evolutionary deviation corresponding to the tissue evolution path to the growth factor activity change index are consistent with their respective degrees of change, while also ensuring... , and The sum of these values ​​is always 1, in order to eliminate the influence of different dimensions and numerical ranges on the calculation results of the growth factor activity change index.

[0069] The intensity of change corresponding to the release location of growth factors is obtained based on the degree of weakening of synergistic promotion, the degree of enhancement of mutual restraint, and the degree of imbalance of synergistic evolution at the corresponding location in the growth factor regulation deviation field. The change amplitude at continuous time nodes is normalized to form the intensity of change. The intensity of expansion corresponding to the range of action of growth factors is obtained based on the change in the diffusion range and diffusion duration of the growth factor regulation deviation in the adjacent tissue repair area. The intensity of expansion is normalized to form the intensity of expansion. The intensity of evolution deviation corresponding to the tissue evolution path is obtained based on the deviation of tissue differentiation evolution state, bone callus evolution state, and mechanical state parameter deviation between the actual healing trajectory and the callus tissue evolution field. The intensity of evolution deviation is normalized to form the intensity of evolution deviation.

[0070] The growth factor activity change index is used to characterize the degree of activity change corresponding to the release location, range of action, and tissue evolution path of growth factors that are not directly observed. The activity change of growth factors that are not directly observed is determined based on the numerical change corresponding to the growth factor activity change index. The release location, range of action, and activity change of growth factors are spatially continuously correlated to reconstruct the distribution state of latent growth factors and generate a latent source calibration field of growth factors.

[0071] S5. Correct the basic twin of fracture healing based on the growth factor hidden source calibration field, and carry out multi-scheme healing inference and simulation verification to generate a set of fracture healing simulation results.

[0072] S5.1. Based on the growth factor crypto-source calibration field, the tissue state parameters, growth factor state parameters, and mechanical state parameters in the fracture healing basal twin are dynamically corrected, and the corrected parameters are written into the corresponding tissue repair area to generate the calibrated fracture healing basal twin.

[0073] Specifically, based on the growth factor release location, range of action, and changes in activity in the growth factor crypto-source calibration field, the growth factor state parameters of the corresponding tissue repair area in the fracture healing basal twin are corrected. The tissue state parameters of the corresponding tissue repair area are updated based on the corrected growth factor state parameters, and the callus evolution state of the corresponding tissue repair area is updated simultaneously. Based on the updated tissue state parameters and callus evolution state, the mechanical state parameters corresponding to the force distribution, displacement changes, and strain changes in the corresponding tissue repair area are corrected. The corrected tissue state parameters, growth factor state parameters, and mechanical state parameters are written into the corresponding tissue repair area, and the fracture healing basal twin is updated according to spatial location correlation and temporal evolution relationship to generate a calibrated fracture healing basal twin.

[0074] S5.2 In the calibrated fracture healing baseline twin, the callus evolution process, tissue differentiation process and mechanical response under different intervention conditions are simultaneously simulated to form a multi-scheme healing simulation field.

[0075] Specifically, different intervention conditions are set based on the calibrated fracture healing baseline twin, and the growth factor state parameters, tissue state parameters, or mechanical state parameters are updated according to the parameter mapping relationship between different intervention conditions and state parameters. Under each intervention condition, the callus evolution process, tissue differentiation process, and mechanical response are continuously simulated, and the simulation time nodes are kept consistent. The callus evolution process, tissue differentiation process, and mechanical response under different intervention conditions are correlated in parallel according to spatial location and time nodes to form a multi-scheme healing simulation field.

[0076] It should also be noted that the parameter mapping relationship between different intervention conditions and state parameters is established based on the parameter change patterns corresponding to the clinical intervention measures. When the intervention measure is exogenous growth factor administration, the administration information is mapped to the growth factor state parameters at the corresponding spatial location and time node based on the administration dose, administration location, and administration duration, combined with the correspondence between the administration dose and the increase in growth factor activity. When the intervention measure is weight-bearing training adjustment, the mechanical state parameters are updated based on the weight-bearing intensity, weight-bearing frequency, and weight-bearing duration. When the intervention measure is fixation adjustment, the force transmission parameters and displacement constraint parameters are adjusted based on the changes in the constraint of the fracture area. When the intervention measure is treatment to promote tissue repair, the tissue state parameters are updated based on changes in tissue state.

[0077] S5.3 Continuously record and integrate the callus evolution trend, tissue state evolution and mechanical changes in the multi-scheme healing simulation field to generate a fracture healing simulation result set.

[0078] Specifically, in the multi-scheme healing simulation field, the expansion range, maturity level, and structural continuity of the callus evolution process under each intervention condition are continuously recorded. The evolution of tissue state and the force distribution, displacement changes, and strain changes corresponding to the mechanical state parameters of the fracture area are recorded along a time series. The callus evolution process, tissue state evolution process, and mechanical response changes under different intervention conditions are correlated according to a unified time node, and the evolutionary features corresponding to changes in fracture gap, callus coverage, callus structural continuity, and mechanical state parameters are extracted. The evolutionary features corresponding to each intervention condition are collected and sorted according to the intervention condition category to form multiple sets of fracture healing evolution results. These multiple sets of fracture healing evolution results are then integrated to generate a fracture healing simulation result set.

[0079] The fracture healing simulation results set includes the temporal evolution information corresponding to the changes in fracture gap, callus coverage, callus structure continuity, tissue state evolution, growth factor state changes, and mechanical state parameter changes under different intervention conditions.

[0080] This embodiment also provides a computer device applicable to the fracture healing simulation method based on the action of growth factors, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the fracture healing simulation method based on the action of growth factors as proposed in the above embodiment.

[0081] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0082] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the fracture healing simulation method based on the effects of growth factors as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0083] In summary, this invention, through continuous evolutionary analysis of the synergistic and mutually restrictive effects among different growth factors and quantitative characterization of the changing states of synergistic regulatory relationships, forms a growth factor synergistic twin field reflecting the biological regulatory characteristics of the fracture area. This field can continuously characterize the changes in the intensity of synergistic effects of growth factors during tissue repair and the regulatory differences between different tissue repair areas. It enables the spatial expression and continuous tracking of the dynamic regulatory behavior of growth factors, thereby providing a more biologically sound regulatory basis for tissue differentiation and callus evolution, and improving the accuracy of the expression of biological regulatory information and the authenticity of the evolutionary process in the fracture healing process.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for simulating fracture healing based on the effects of growth factors, characterized in that: include, Fracture imaging data and patient weight-bearing data were collected, tissue repair characteristics, mechanical response characteristics and corresponding growth factor activity characteristics were extracted, dynamic mapping relationship between tissue state parameters, growth factor state parameters and mechanical state parameters was established, dynamic twin modeling of fracture area was performed, and basic twin of fracture healing was constructed. By simulating the dynamic evolution process of growth factors through a basic twin of fracture healing, a synergistic regulatory relationship between different growth factors is established, the changing state of the synergistic regulatory relationship is quantitatively characterized, and a growth factor synergistic twin field is generated. The tissue differentiation and callus evolution process in the tissue repair area is simulated by using growth factors and twin fields, and the results are fed back to the mechanical state parameters for synchronous updating, generating a callus tissue evolution field. Using fracture imaging data and patient weight-bearing data continuously collected during fracture healing, the actual healing trajectory is constructed. The actual healing trajectory is then matched with the callus tissue evolution field to determine the deviation, and the release location, range of action, and activity changes of growth factors are deduced to generate a growth factor crypto-source calibration field. The fracture healing baseline twin was corrected based on the growth factor hidden source calibration field, and multiple healing scenarios were extrapolated and simulated to generate a fracture healing simulation result set.

2. The fracture healing simulation method based on growth factor action according to claim 1, characterized in that: The extracted tissue repair characteristics, mechanical response characteristics, and corresponding growth factor activity characteristics specifically include, Fracture imaging data is segmented into fracture regions and reconstructed into three-dimensional bone structures to locate fracture gaps and callus formation areas. Tissue repair features are extracted by analyzing the continuity of the three-dimensional bone structure and the spatial distribution of callus. Force transmission and strain evolution analysis were performed on the patient's weight-bearing data to simulate the mechanical evolution process in the fracture area and extract mechanical response characteristics; Based on the changes in tissue repair characteristics at different time points, the changes in growth factor activity corresponding to the progress of tissue repair in the fracture area were tracked, and the corresponding growth factor activity characteristics were extracted.

3. The fracture healing simulation method based on growth factor action according to claim 2, characterized in that: The construction of the basic twin for fracture healing specifically includes, Tissue repair characteristics are mapped to tissue state parameters, mechanical response characteristics are mapped to mechanical state parameters, and growth factor activity characteristics are mapped to growth factor state parameters. Based on the dynamic relationship between tissue state parameters, growth factor state parameters and mechanical state parameters, a state coupling network is constructed. The tissue repair process, growth factor regulation process and mechanical change process in the fracture area are synchronously and dynamically simulated through the state coupling network to construct a basic twin of fracture healing.

4. The fracture healing simulation method based on growth factor action according to claim 3, characterized in that: The aforementioned growth factor synergistic twin field specifically includes, By utilizing the state parameters of growth factors in the fracture healing twin, the release, diffusion, and activity changes of growth factors are continuously simulated to form a dynamic evolution process of growth factors. Based on the dynamic evolution process of growth factors, the synergistic and mutually restrictive effects of different growth factors in the tissue repair area are analyzed, forming a synergistic regulatory relationship among different growth factors; The intensity and evolution trend of the synergistic regulatory relationship among different growth factors in different tissue repair regions were quantitatively analyzed to form the synergistic regulatory change state; Co-evolutionary analysis of the changes in the synergistic regulation state is performed and mapped to the corresponding tissue repair region of the fracture healing basal twin, generating a growth factor synergistic twin field.

5. The fracture healing simulation method based on growth factor action according to claim 4, characterized in that: The aforementioned callus tissue evolution field specifically includes, By using growth factor synergistic twin field to regionalize the intensity of bioregulation in tissue repair areas, the differentiation tendency of tissue repair areas is identified, and the tissue differentiation process and callus formation range are dynamically deduced based on the differentiation tendency, thus forming a tissue differentiation evolution field. Based on the tissue differentiation and evolution field, the generation and expansion process of callus in the tissue repair area is dynamically simulated, and the structural continuity, maturity and spatial coverage of callus are updated simultaneously to form a callus structure evolution field. Based on the evolution field of the callus structure, feedback analysis is performed on the local load-bearing path, stiffness changes and mechanical transmission process of the fracture area, and the mechanical state parameters are updated simultaneously to generate the callus tissue evolution field.

6. The fracture healing simulation method based on growth factor effects according to claim 1, characterized in that: The construction of the actual healing trajectory specifically includes, The fracture imaging data continuously acquired during the fracture healing period were integrated temporally and reconstructed in three dimensions. Based on the patient's weight-bearing data, the strain and displacement changes in the fracture area were analyzed to form a sequence of structural and mechanical response evolution of the fracture area. A comprehensive analysis of the evolution sequence of fracture area structure and mechanical response is conducted to identify callus formation areas and bone tissue repair progress, and to generate a tissue repair evolution state sequence. By establishing a temporally continuous spatial correlation between the tissue repair evolution state sequence, the actual healing trajectory can be constructed.

7. The fracture healing simulation method based on growth factor action according to claim 6, characterized in that: The generated growth factor hidden source calibration field specifically includes, Spatiotemporal correlation analysis was performed on the actual healing trajectory and the callus tissue evolution field to generate state correlation relationships. The state evolution information in the callus tissue evolution field was then mapped to the actual healing trajectory using the state correlation relationships to form a healing deviation distribution field. Based on the healing deviation distribution field, the tissue evolution path of the deviation area in the callus tissue evolution field is traced, and the abnormal situation of growth factor synergistic regulation in the deviation area is analyzed to form a growth factor regulation deviation field. Based on the inverse deduction of the growth factor regulation deviation field, the release location, range of action and activity changes of growth factors that were not directly observed are reconstructed, the distribution state of latent growth factors is reconstructed, and a latent source calibration field of growth factors is generated.

8. The fracture healing simulation method based on growth factor action according to claim 7, characterized in that: The generated fracture healing simulation result set specifically includes, Based on the growth factor crypto-source calibration field, the tissue state parameters, growth factor state parameters, and mechanical state parameters in the fracture healing basal twin are dynamically corrected, and the corrected parameters are written into the corresponding tissue repair area to generate the calibrated fracture healing basal twin. In the calibrated fracture healing baseline twin, the callus evolution process, tissue differentiation process and mechanical response under different intervention conditions are simultaneously simulated to form a multi-scheme healing simulation field; The evolution trend of callus, tissue state evolution and mechanical changes in the multi-scheme healing simulation field are continuously recorded and integrated to generate a set of fracture healing simulation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the fracture healing simulation method based on the action of growth factors as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the fracture healing simulation method based on the action of growth factors as described in any one of claims 1 to 8.

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