An intelligent design method for alveolar bone augmentation morphology based on multi-agent system

Abstract

The present invention discloses an intelligent design method for alveolar bone augmentation morphology based on a multi-agent system, including S1, extracting key biomechanical properties in three-dimensional image data; S2, configuring each agent in the multi-agent system, each agent representing a specific clinical scenario or task; S3, the agent automatically analyzes the quality and structural characteristics of the alveolar bone according to a preset algorithm, and evaluates the risks and potential problems associated with implant placement; S4, the agents interact and collaborate with each other, and optimize the shape, size and position of the implant by simulating different design schemes and implantation strategies, while designing the alveolar bone augmentation morphology, confirming bone support and improving the integration of bone structure and implant; S5, adjusting the design of the alveolar bone and implant according to the simulation results; S6, generating the final design scheme of the optimized alveolar bone and implant. The present invention improves the pre-operative planning and rehearsal effects, and reduces the uncertainty and potential risks in the actual operation.

Description

Technical Field

[0001] The present invention relates to the field of alveolar bone technology, and in particular to an intelligent design method for alveolar bone incremental morphology based on a multi-agent system. Background Art

[0002] Dental implant surgery is an important treatment method in modern stomatology, used to replace missing teeth. However, the success of implant surgery depends on a good match between the implant and the alveolar bone to ensure the stability and biocompatibility of the implant. Traditional implant design methods mainly rely on the doctor's experience and fixed design parameters, which have certain limitations in dealing with individual differences of different patients and complex clinical situations.

[0003] In the existing technology, the design of alveolar bone augmentation morphology is usually evaluated using two-dimensional imaging data, lacking a comprehensive analysis of the three-dimensional structure, which makes it difficult for the design results to accurately reflect the actual bone structure characteristics. In addition, the traditional method of evaluating the quality and structure of the alveolar bone is mostly based on the doctor's subjective judgment, lacking a systematic and accurate analysis method. This situation not only increases the uncertainty of the operation, but may also affect the long-term stability of the implant and the patient's rehabilitation effect.

[0004] Traditional design methods often fail to fully consider individual factors such as the patient's bone condition, tooth arrangement, age-related bone remodeling characteristics, and bone tissue response under the influence of drugs when optimizing the shape, size, and position of implants. As a result, the designed implant may not achieve the expected effect during the actual implantation process, and may even lead to surgical failure or late complications. Therefore, how to provide an intelligent design method for alveolar bone augmentation morphology based on a multi-agent system is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] The purpose of the present invention is to propose an intelligent design method for alveolar bone augmentation morphology based on a multi-agent system, which optimizes the design of alveolar bone augmentation morphology, ensures bone support and the integration of implants and bone structure, thereby greatly improving pre-operative planning and rehearsal effects and reducing uncertainties and potential risks in actual operations.

[0006] According to an embodiment of the present invention, an intelligent design method for alveolar bone augmentation morphology based on a multi-agent system comprises the following steps:

[0007] S1. Obtain three-dimensional image data of the patient's alveolar bone and oral structure through computer tomography, and extract key biomechanical properties within the three-dimensional image data;

[0008] S2. configuring agents in a multi-agent system, where each agent represents a specific clinical scenario or task, including osteoporosis, tooth alignment, age-related bone remodeling characteristics of patients, and bone tissue response under the influence of drugs;

[0009] S3. The intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to the preset algorithm, and evaluates the risks and potential problems associated with implant placement, including evaluating the stress distribution of the alveolar bone and possible fracture risks through the bionic model;

[0010] S4, the intelligent agents interact and collaborate with each other to optimize the shape, size and position of the implant by simulating different design schemes and implantation strategies, and at the same time design the alveolar bone augmentation morphology, confirm bone support and improve the integration of bone structure and implant;

[0011] S5. Adjust the design of the alveolar bone and implant according to the simulation results, so that the design can meet individual needs and maximize implant stability and biocompatibility;

[0012] S6. Generate the final design plan of the optimized alveolar bone and implant, and output surgical guidance and expected effect evaluation report.

[0013] Optionally, the S1 includes the following steps:

[0014] S11. Use a computer tomography scanner to perform a full-scale scan of the patient's oral area to obtain three-dimensional image data D 3D ;

[0015] S12, using image processing software to process the three-dimensional image data D 3D Preprocessing is performed, including image denoising, contrast enhancement, and edge sharpening. After preprocessing, three-dimensional image data is obtained.

[0016] S13. Apply the deep learning-based adaptive threshold segmentation algorithm to separate the alveolar bone area from the 3D image data. The alveolar bone is segmented and an independent image data D is generated. AB ;

[0017]

[0018] Among them, T adaptive (i, j, k) is the adaptive threshold learned through deep neural network, which is dynamically adjusted according to local image features;

[0019] S14. Using morphological methods to analyze independent image data D AB Further processing was performed to identify and extract key structural features of the alveolar bone, including trabecular structure and cortical bone thickness:

[0020] Dilation operation: used to fill holes in the image and enhance structural features:

[0021]

[0022] Erosion operation: used to remove edge noise and maintain the main structural features:

[0023]

[0024] Opening operation: first perform an erosion operation, then a dilation operation to remove small objects and protruding parts:

[0025]

[0026] Closing operation: First perform the dilation operation, then the erosion operation, which is used to fill small holes and closely connected objects:

[0027]

[0028] Skeletonization operation: used to extract the trabecular structure and represent it as image data of the refined trabecular structure:

[0029]

[0030] Among them, B s Represents the structural element, used for morphological operations, (i, j, k) represents the pixel coordinates of the three-dimensional image data, b∈B s Represents the structural element B s The elements in It indicates the expansion operation. represents the corrosion operation, max represents the maximum value operation, min represents the minimum value operation, and N represents the number of iterations in the skeletonization operation;

[0031] S15, using finite element analysis technology based on multi-scale modeling to perform mechanical simulation on the alveolar bone structure, calculate its stress distribution and deformation, and extract bone density ρ, bone structure integrity index I s , bone strength S b biomechanical properties;

[0032] S16, comprehensive biomechanical properties bone density ρ, bone structure integrity index I s , bone strength S b Conduct a detailed assessment of the patient's alveolar bone to determine its implantability and potential risks;

[0033] S17, the processed and analyzed three-dimensional image data D 3Dand biomechanical properties bone density ρ, bone structural integrity index I s , bone strength S b The results are imported into the multi-intelligence system.

[0034] Optionally, the bone density ρ is obtained by calculating the mass of bone tissue per unit volume:

[0035]

[0036] Among them, m bone Indicates the quality of bone tissue, V bone Represents the volume of bone tissue, Image data representing trabecular bone structure, ρ i,j,k represents the local bone density at position (i, j, k), V i,j,k represents the local volume at position (i, j, k);

[0037] The bone structural integrity index I s By calculating the connection density and distribution of trabecular bone, we can obtain:

[0038]

[0039] Among them, C i,j,k is the connection density, N conn The number of connections.

[0040] The bone strength S b The maximum stress and strain of the alveolar bone under load conditions obtained in multi-scale modeling simulation are expressed as:

[0041] S b =max i,j,k (σ i,j,k );

[0042] Among them, σ i,j,k is the stress distribution calculated in the multi-scale modeling simulation.

[0043] Optionally, S2 includes the following steps:

[0044] S21. Configure each agent A in the multi-agent system i , where each agent represents a specific clinical situation or task;

[0045] S22. Configured clinical scenarios or tasks include:

[0046] Osteoporosis Agent A osteoporosis According to the patient's bone density ρ and bone structural integrity I s Analysis of the impact of osteoporosis on implant placement:

[0047] A osteoporosis =FEA(ρ(x,y,z),I s (x,uy,z));

[0048]

[0049] Among them, ρ(x,y,z) is the bone density distribution, I s (x, y, z) is the distribution of bone structural integrity, FEA represents the finite element analysis function, which is used to analyze the influence of bone density and structural integrity on implant placement, Ω is the analysis area, represents the gradient of bone density in the x direction, represents the rate of change of bone density in the x direction, represents the gradient of bone structural integrity in the y direction, represents the rate of change of bone structural integrity in the y direction, is the gradient of bone structural integrity in the z direction, indicating the rate of change of bone structural integrity in the z direction, R osteoporosis Agent A is an osteoporosis agent osteoporosis The analysis result matrix represents the osteoporosis situation agent A osteoporosis Comprehensive assessment of the patient's osteoporosis status, including bone density, bone structural integrity, and possible fracture risk information;

[0050] Teeth Arrangement Agent A teeth_alignment According to the patient's tooth arrangement and occlusion, optimize the position and angle of the implant:

[0051] A teeth_alignment =Optimization(P i ,T i );

[0052]

[0053] Among them, A teeth_alignment is the permutation position matrix, P i is the arrangement position matrix, which represents the arrangement of implants at different positions, T i is the arrangement angle matrix, which indicates the arrangement of implants at different angles. Optimization is the optimization function used to optimize the arrangement position and angle of implants. occlusion is the occlusal relationship matrix, which represents the occlusal relationship at the position (x, y, z), V is the analysis volume, which represents the spatial range of the optimization calculation, n is the number of implants, and R alignment is the response result of the tooth arrangement intelligent body, which represents the comprehensive result of optimizing the arrangement position and angle of the implant according to the tooth arrangement and occlusal relationship;

[0054] Age-related bone remodeling agent Aage_related Evaluate the response characteristics of bone tissue according to the patient's age and corresponding bone remodeling characteristics:

[0055] A age_related =Age-RelatedModeling(α,β,G age (t));

[0056]

[0057] Among them, α is the age correlation coefficient, which indicates the influence coefficient of age on bone remodeling characteristics, β is the time dependence coefficient, which indicates the influence coefficient of time on bone remodeling characteristics, G age (t) is the age-related bone remodeling characteristic function, which represents the bone remodeling characteristics at time t. is the temporal rate of change of bone remodeling characteristics, R age Age-related bone remodeling agent A age_related The analysis results represent the evaluation results of the bone tissue response characteristics according to the patient's age and the corresponding bone remodeling characteristics;

[0058] Drugs affect Agent A medication Evaluate the effects of medications on bone tissue response and implant stability based on the patient's medication use:

[0059] A medication =MedicationImpactAnalysis(D med ,C med ,γ,E med (t));

[0060] R medication =∫ Ω (D med ·C med (x,y,z)+γ·E med (t))dΩ;

[0061] Among them, D med is the drug distribution matrix, which indicates the distribution of drugs at different locations, C med is the drug response coefficient matrix, which indicates the drug response coefficient to different positions; γ is the time correlation coefficient, which indicates the influence coefficient of time on drug effect; E med (t) is the drug effect function, Ω is the analysis area, D med ·C med (x, y, z) is the product of drug distribution and response, indicating the drug effect at position (x, y, z), γ·E med (t) is the effect of time on drug effect, indicating the drug effect at time t, R medication Represents the effect of drugs on agent Amedication Evaluation of drug response in bone tissue;

[0062] S23. Each agent A i Equipped with specific algorithms and data models to analyze relevant clinical data and generate preliminary design solutions:

[0063]

[0064] Among them, W i is the weight matrix, M i is the clinical data matrix;

[0065] S24. The agents exchange information and work together through a preset communication protocol to form a comprehensive design solution:

[0066]

[0067] Among them, W ij is the weight matrix, M ij is the clinical data matrix, C ij is the communication protocol matrix;

[0068] S25. Information exchange between agents includes sharing of bone density and mechanical stress distribution σ i,j,k Evaluation of the suitability of drug response models:

[0069]

[0070] Among them, R i is the bone density matrix, T i is the stress distribution matrix, S i is the drug response matrix;

[0071] S26. Through collaborative analysis between intelligent entities, optimize the alveolar bone augmentation morphology and implant design to meet individual needs and meet the requirements of biomechanical performance and biocompatibility:

[0072]

[0073] Among them, L i is the design matrix, F i is the biomechanical performance matrix, C i The biocompatibility matrix.

[0074] Optionally, S3 includes the following steps:

[0075] S31. The intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to the preset algorithm, including calculating the bone density ρ and the bone structural integrity I s ;

[0076]

[0077] Among them, ρ i,j,k is the local bone density, C i,j,k is the bone structure connection density, N conn is the number of connections, It is independent image data;

[0078] S32, the intelligent agent evaluates the stress distribution of the alveolar bone through a bionic model based on multi-scale modeling and finite element analysis technology i,j,k and possible fracture risk:

[0079]

[0080] Where, E is the elastic modulus and ν is Poisson’s ratio;

[0081] S33. Through mechanical simulation, calculate the stress concentration and deformation of the alveolar bone during implant placement and assess the fracture risk R f :

[0082]

[0083] Among them, σ t is the tensile strength of bone tissue;

[0084] S34. Based on the assessment results of stress distribution and fracture risk, the intelligent agent generates a risk assessment report, including identification of high-risk areas and suggestions for improvement measures.

[0085] Optionally, S4 includes the following steps:

[0086] S41, the agents exchange information and work together through a preset communication protocol. Each agent A i share and integrate their analysis results;

[0087]

[0088] Among them, ∑ i is the covariance matrix, representing agent A i Covariance of the analysis result distribution; μ i is the mean vector, representing agent A i The mean of the analysis result distribution; x is the sample vector, which represents the specific data point of the current analysis; R i is the analysis result matrix of the intelligent agent; T i is the stress distribution matrix σ i,j,k , represents the stress distribution; S i is the drug response matrix, representing agent A i Assessment of response to medication;

[0089] S42, by simulating different design solutions D sim and implantation strategies to evaluate the effects of implant shape, size, and position on alveolar bone augmentation morphology:

[0090]

[0091] Among them, L i (x, y, z) is the design matrix, which represents the specific parameters of the design solution at the position (x, y, z); F i (x, y, z) is the biomechanical performance matrix, which represents the biomechanical performance at the position (x, y, z); is the biocompatibility gradient, which indicates the rate of change of biocompatibility at position (x, y, z); Ω is the integration area, which indicates the spatial range of the design solution;

[0092] S43, based on the collaborative analysis results between the intelligent agents, optimize the design parameters of the implant, including the shape P of the implant shape , size P size and position P position :

[0093]

[0094] Among them, W i (x, y, z) is the weight matrix, which represents the weight of the design parameter at the position (x, y, z); M i (x, y, z) is the clinical data matrix, which represents the clinical data at the position (x, y, z); V is the integral volume, which represents the spatial volume of the design solution; λ is the regularization parameter, which is used to balance the different objectives in the optimization process; n is the boundary normal vector, which represents the normal direction on the boundary; b is the boundary condition, which represents the specific requirements on the boundary; dV is the boundary integral region, which represents the spatial boundary of the design solution;

[0095] S44. During the optimization process, confirm the bone support of the alveolar bone augmentation design and the integration of the implant and bone structure, and evaluate its stability and long-term effects;

[0096] S45. Generate the final optimized design plan for the implant and alveolar bone augmentation morphology, and output detailed design parameters and expected effect evaluation report.

[0097] The beneficial effects of the present invention are:

[0098] (1) The present invention uses computed tomography to obtain three-dimensional image data of the patient's alveolar bone and oral structure, and extracts key biomechanical properties, making the design process highly accurate and personalized. During the analysis process, the intelligent agent takes into account the patient's bone condition, tooth arrangement, age-related bone remodeling characteristics, and bone tissue response under the influence of drugs, and can perform targeted design optimization according to specific circumstances.

[0099] (2) The multi-agent system of the present invention can simulate a variety of clinical scenarios. Through preset algorithms and communication protocols, agents can exchange information and collaborate with each other, thereby comprehensively simulating different design schemes and implantation strategies. This process not only optimizes the shape, size and position of the implant, but also optimizes the design of the alveolar bone augmentation morphology to ensure bone support and the integration of the implant and bone structure, thereby greatly improving the pre-operative planning and rehearsal effects and reducing the uncertainty and potential risks in the actual operation.

[0100] (3) Each intelligent agent of the present invention automatically analyzes the quality and structural characteristics of the alveolar bone according to a preset algorithm, and evaluates the risks and potential problems associated with implant placement, including evaluating the stress distribution and fracture risk of the alveolar bone through a bionic model. The generated risk assessment report identifies high-risk areas in detail and provides improvement suggestions, which helps doctors make adequate preparations before surgery and improves the success rate and safety of the surgery.

[0101] (4) The collaborative analysis and information sharing between the intelligent agents of the present invention enable the design scheme to be optimized through multiple iterations. Through finite element analysis and multi-scale modeling technology, the optimized implant and alveolar bone augmentation morphology design can meet individual needs to the greatest extent, ensure biomechanical performance and biocompatibility, and provide optimal design parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0103] Figure 1 The present invention proposes a flow chart of an intelligent design method for alveolar bone augmentation morphology based on a multi-agent system. DETAILED DESCRIPTION

[0104] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and thus only show the components related to the present invention.

[0105] refer to Figure 1 , an intelligent design method for alveolar bone augmentation morphology based on a multi-agent system, comprising the following steps:

[0106] S1. Obtain three-dimensional image data of the patient's alveolar bone and oral structure through computer tomography, and extract key biomechanical properties within the three-dimensional image data;

[0107] S2. configuring agents in a multi-agent system, where each agent represents a specific clinical scenario or task, including osteoporosis, tooth alignment, age-related bone remodeling characteristics of patients, and bone tissue response under the influence of drugs;

[0108] S3. The intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to the preset algorithm, and evaluates the risks and potential problems associated with implant placement, including evaluating the stress distribution of the alveolar bone and possible fracture risks through the bionic model;

[0109] S4, the intelligent agents interact and collaborate with each other to optimize the shape, size and position of the implant by simulating different design schemes and implantation strategies, and at the same time design the alveolar bone augmentation morphology, confirm bone support and improve the integration of bone structure and implant;

[0110] S5. Adjust the design of the alveolar bone and implant according to the simulation results, so that the design can meet individual needs and maximize implant stability and biocompatibility;

[0111] S6. Generate the final design plan of the optimized alveolar bone and implant, and output surgical guidance and expected effect evaluation report.

[0112] In this embodiment, S1 includes the following steps:

[0113] S11. Use a computer tomography scanner to perform a full-scale scan of the patient's oral area to obtain three-dimensional image data D 3D ;

[0114] S12, using image processing software to process the three-dimensional image data D 3D Preprocessing is performed, including image denoising, contrast enhancement, and edge sharpening. After preprocessing, three-dimensional image data is obtained.

[0115] S13. Apply the deep learning-based adaptive threshold segmentation algorithm to separate the alveolar bone area from the 3D image data. The alveolar bone is segmented and an independent image data D is generated. AB ;

[0116]

[0117] Among them, T adaptive(i, j, k) is the adaptive threshold learned through deep neural network, which is dynamically adjusted according to local image features;

[0118] S14. Using morphological methods to analyze independent image data D AB Further processing was performed to identify and extract key structural features of the alveolar bone, including trabecular structure and cortical bone thickness:

[0119] Dilation operation: used to fill holes in the image and enhance structural features:

[0120]

[0121] Erosion operation: used to remove edge noise and maintain the main structural features:

[0122]

[0123] Opening operation: first perform an erosion operation, then a dilation operation to remove small objects and protruding parts:

[0124]

[0125] Closing operation: First perform the dilation operation, then the erosion operation, which is used to fill small holes and closely connected objects:

[0126]

[0127] Skeletonization operation: used to extract the trabecular structure and represent it as image data of the refined trabecular structure:

[0128]

[0129] Among them, B s Represents the structural element, used for morphological operations, (i, j, k) represents the pixel coordinates of the three-dimensional image data, b∈B s Represents the structural element B s The elements in It indicates the expansion operation. represents the corrosion operation, max represents the maximum value operation, min represents the minimum value operation, and N represents the number of iterations in the skeletonization operation;

[0130] S15, using finite element analysis technology based on multi-scale modeling to perform mechanical simulation on the alveolar bone structure, calculate its stress distribution and deformation, and extract bone density ρ, bone structure integrity index I s , bone strength S b biomechanical properties;

[0131] S16, comprehensive biomechanical properties bone density ρ, bone structure integrity index I s , bone strength S b Conduct a detailed assessment of the patient's alveolar bone to determine its implantability and potential risks;

[0132] S17, the processed and analyzed three-dimensional image data D 3D and biomechanical properties bone density ρ, bone structural integrity index I s , bone strength S b The results are imported into the multi-intelligence system.

[0133] In this embodiment, the bone density ρ is obtained by calculating the mass of bone tissue per unit volume:

[0134]

[0135] Among them, m bone Indicates the quality of bone tissue, V bone Represents the volume of bone tissue, Image data representing trabecular bone structure, ρ i,j,k represents the local bone density at position (i, j, k), V i,j,k represents the local volume at position (i, j, k);

[0136] The bone structural integrity index I s By calculating the connection density and distribution of trabecular bone, we can obtain:

[0137]

[0138] Among them, C i,j,k is the connection density, N conn The number of connections.

[0139] The bone strength S b The maximum stress and strain of the alveolar bone under load conditions obtained in multi-scale modeling simulation are expressed as:

[0140] S b =max i,j,k (σ i,j,k );

[0141] Among them, σ i,j,k is the stress distribution calculated in the multi-scale modeling simulation.

[0142] In this implementation, S2 includes the following steps:

[0143] S21. Configure each agent A in the multi-agent system i , where each agent represents a specific clinical situation or task;

[0144] S22. Configured clinical scenarios or tasks include:

[0145] Osteoporosis Agent A osteoporosis According to the patient's bone density ρ and bone structural integrity I s Analysis of the impact of osteoporosis on implant placement:

[0146] A osteoporosis =FEA(ρ(x,y,z),I s (x,y,z));

[0147]

[0148] Among them, ρ(x,y,z) is the bone density distribution, I s (x, y, z) is the distribution of bone structural integrity, FEA represents the finite element analysis function, which is used to analyze the influence of bone density and structural integrity on implant placement, Ω is the analysis area, represents the gradient of bone density in the x direction, represents the rate of change of bone density in the x direction, represents the gradient of bone structural integrity in the y direction, represents the rate of change of bone structural integrity in the y direction, is the gradient of bone structural integrity in the z direction, indicating the rate of change of bone structural integrity in the z direction, R osteoporosis Agent A is an osteoporosis agent osteoporosis The analysis result matrix represents the osteoporosis situation agent A osteoporosis Comprehensive assessment of the patient's osteoporosis status, including bone density, bone structural integrity, and possible fracture risk information;

[0149] Teeth Arrangement Agent A teeth_alignment According to the patient's tooth arrangement and occlusion, optimize the position and angle of the implant:

[0150] A teeth_alignment =Optimization(P i ,T i );

[0151]

[0152] Among them, A teeth_alignment is the permutation position matrix, P i is the arrangement position matrix, which represents the arrangement of implants at different positions, T i is the arrangement angle matrix, which indicates the arrangement of implants at different angles. Optimization is the optimization function used to optimize the arrangement position and angle of implants. occlusiionis the occlusal relationship matrix, which represents the occlusal relationship at the position (x, y, z), V is the analysis volume, which represents the spatial range of the optimization calculation, n is the number of implants, and R alignment is the response result of the tooth arrangement intelligent body, which represents the comprehensive result of optimizing the arrangement position and angle of the implant according to the tooth arrangement and occlusal relationship;

[0153] Age-related bone remodeling agent A age_related Evaluate the response characteristics of bone tissue according to the patient's age and corresponding bone remodeling characteristics:

[0154] A age_related =Age-RelatedModeling(α,β,G age (t));

[0155]

[0156] Among them, α is the age correlation coefficient, which indicates the influence coefficient of age on bone remodeling characteristics, β is the time dependence coefficient, which indicates the influence coefficient of time on bone remodeling characteristics, G age (t) is the age-related bone remodeling characteristic function, which represents the bone remodeling characteristics at time t. is the temporal rate of change of bone remodeling characteristics, R age Age-related bone remodeling agent A age_related The analysis results represent the evaluation results of the bone tissue response characteristics according to the patient's age and the corresponding bone remodeling characteristics;

[0157] Drugs affect Agent A medication Evaluate the effects of medications on bone tissue response and implant stability based on the patient's medication use:

[0158] A medication =MedicationImpactAnalysis(D med ,C med ,γ,E med (t));

[0159] R medication =∫ Ω (D med ·C med (x,y,z)+γ·E med (t))dΩ;

[0160] Among them, D med is the drug distribution matrix, which indicates the distribution of drugs at different locations, C med is the drug response coefficient matrix, which indicates the drug response coefficient to different positions; γ is the time correlation coefficient, which indicates the influence coefficient of time on drug effect; Emed (t) is the drug effect function, Ω is the analysis area, D med ·C med (x, y, z) is the product of drug distribution and response, indicating the drug effect at position (x, y, z), γ·E med (t) is the effect of time on drug effect, indicating the drug effect at time t, R medication Represents the effect of drugs on agent A medication Evaluation of drug response in bone tissue;

[0161] S23. Each agent A i Equipped with specific algorithms and data models to analyze relevant clinical data and generate preliminary design solutions:

[0162]

[0163] Among them, W i is the weight matrix, M i is the clinical data matrix;

[0164] S24. The agents exchange information and work together through a preset communication protocol to form a comprehensive design solution:

[0165]

[0166] Among them, W ij is the weight matrix, M ij is the clinical data matrix, C ij is the communication protocol matrix;

[0167] S25. Information exchange between agents includes sharing of bone density and mechanical stress distribution σ i,j,k Evaluation of the suitability of drug response models:

[0168]

[0169] Among them, R i is the bone density matrix, T i is the stress distribution matrix, S i is the drug response matrix;

[0170] S26. Through collaborative analysis between intelligent entities, optimize the alveolar bone augmentation morphology and implant design to meet individual needs and meet the requirements of biomechanical performance and biocompatibility:

[0171]

[0172] Among them, L i is the design matrix, F iis the biomechanical performance matrix, C i The biocompatibility matrix.

[0173] In this implementation, S3 includes the following steps:

[0174] S31. The intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to the preset algorithm, including calculating the bone density ρ and the bone structural integrity I s ;

[0175]

[0176] Among them, ρ i,j,k is the local bone density, C i,j,k is the bone structure connection density, N conn is the number of connections, It is independent image data;

[0177] S32, the intelligent agent evaluates the stress distribution of the alveolar bone through a bionic model based on multi-scale modeling and finite element analysis technology i,j,k and possible fracture risk:

[0178] σ i,j,k =FEA(D ABi,j,k ,E,v);

[0179] Where, E is the elastic modulus, v is Poisson’s ratio;

[0180] S33. Through mechanical simulation, calculate the stress concentration and deformation of the alveolar bone during implant placement and assess the fracture risk R f :

[0181]

[0182] Among them, σ t is the tensile strength of bone tissue;

[0183] S34. Based on the assessment results of stress distribution and fracture risk, the intelligent agent generates a risk assessment report, including identification of high-risk areas and suggestions for improvement measures.

[0184] In this implementation manner, the S4 includes the following steps:

[0185] S41, the agents exchange information and work together through a preset communication protocol. Each agent A i share and integrate their analysis results;

[0186]

[0187] Among them, ∑ i is the covariance matrix, representing agent Ai Covariance of the analysis result distribution; μ i is the mean vector, representing agent A i The mean of the analysis result distribution; x is the sample vector, which represents the specific data point of the current analysis; R i is the analysis result matrix of the intelligent agent; T i is the stress distribution matrix σ i,j,k , represents the stress distribution; S i is the drug response matrix, representing agent A i Assessment of response to medication;

[0188] S42, by simulating different design solutions D sim and implantation strategies to evaluate the effects of implant shape, size, and position on alveolar bone augmentation morphology:

[0189]

[0190] Among them, L i (x, y, z) is the design matrix, which represents the specific parameters of the design solution at the position (x, y, z); F i (x, y, z) is the biomechanical performance matrix, which represents the biomechanical performance at the position (x, y, z); is the biocompatibility gradient, which indicates the rate of change of biocompatibility at position (x, y, z); Ω is the integration area, which indicates the spatial range of the design solution;

[0191] S43, based on the collaborative analysis results between the intelligent agents, optimize the design parameters of the implant, including the shape P of the implant shape , size P size and position P position :

[0192]

[0193] Among them, W i (x, y, z) is the weight matrix, which represents the weight of the design parameter at the position (x, y, z); M i (x, y, z) is the clinical data matrix, which represents the clinical data at the position (x, y, z); V is the integral volume, which represents the spatial volume of the design solution; λ is the regularization parameter, which is used to balance the different objectives in the optimization process; n is the boundary normal vector, which represents the normal direction on the boundary; b is the boundary condition, which represents the specific requirements on the boundary; dV is the boundary integral region, which represents the spatial boundary of the design solution;

[0194] S44. During the optimization process, confirm the bone support of the alveolar bone augmentation design and the integration of the implant and bone structure, and evaluate its stability and long-term effects;

[0195] S45. Generate the final optimized design plan for the implant and alveolar bone augmentation morphology, and output detailed design parameters and expected effect evaluation report.

[0196] The multi-agent system of this embodiment proposes a new solution for the intelligent design of alveolar bone augmentation morphology. It simulates and analyzes various complex clinical scenarios by configuring different agents, thereby achieving individualization and precision in implant design. First, three-dimensional image data of the patient's alveolar bone and oral structure are acquired through computed tomography, and key biomechanical properties in the three-dimensional image data are extracted. These steps ensure that the acquired data is highly accurate and reliable, providing a solid foundation for subsequent intelligent analysis.

[0197] In the multi-agent system, each agent is configured according to different clinical scenarios or tasks, such as osteoporosis, tooth arrangement, age-related bone remodeling characteristics, and bone tissue response under the influence of drugs. Each agent automatically analyzes the quality and structural characteristics of the alveolar bone through a preset algorithm, and evaluates the risks and potential problems associated with implant placement, including evaluating the stress distribution of the alveolar bone and possible fracture risks through a bionic model. Agents exchange information and collaborate with each other through a preset communication protocol, and optimize the shape, size, and position of the implant by simulating different design schemes and implantation strategies. At the same time, the alveolar bone augmentation morphology is designed to confirm bone support and improve the integration of bone structure and implants.

[0198] Compared with the prior art, the present invention overcomes the limitations of traditional design methods and achieves comprehensive simulation and precise analysis of complex clinical situations through a multi-agent system. Due to the lack of comprehensive analysis of three-dimensional structures and systematic biomechanical evaluation in the prior art, it is difficult to achieve individualized and precise optimization of implant design. The method based on a multi-agent system can comprehensively consider various individual differences of patients and generate the optimal design plan, thereby improving the success rate of implant surgery and the rehabilitation effect of patients.

[0199] In summary, the intelligent design method of alveolar bone augmentation morphology based on a multi-agent system, based on the existing technology, realizes the individualization and precision optimization of alveolar bone and implant design by introducing a multi-agent system and advanced biomechanical evaluation methods, overcomes the shortcomings of traditional methods, and provides a more scientific and reliable solution.

[0200] The present invention uses computer tomography to obtain three-dimensional image data of the patient's alveolar bone and oral structure, and extracts key biomechanical properties, making the design process highly accurate and personalized. During the analysis process, the intelligent agent takes into account the patient's bone condition, tooth arrangement, age-related bone remodeling characteristics, and bone tissue response under the influence of drugs, and can perform targeted design optimization according to specific circumstances.

[0201] The multi-agent system of the present invention can simulate a variety of clinical scenarios. Through preset algorithms and communication protocols, agents can exchange information and collaborate with each other, thereby comprehensively simulating different design schemes and implantation strategies. This process not only optimizes the shape, size and position of the implant, but also optimizes the design of the alveolar bone augmentation morphology to ensure bone support and the integration of the implant and bone structure, thereby greatly improving the planning and rehearsal effects before surgery and reducing the uncertainty and potential risks in the actual surgery.

[0202] In the present invention, each intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to a preset algorithm, and evaluates the risks and potential problems associated with implant placement, including evaluating the stress distribution and fracture risk of the alveolar bone through a bionic model. The generated risk assessment report identifies high-risk areas in detail and provides improvement suggestions, which helps doctors make adequate preparations before surgery and improves the success rate and safety of the surgery.

[0203] The collaborative analysis and information sharing between the intelligent agents of the present invention enable the design scheme to be optimized through multiple iterations. Through finite element analysis and multi-scale modeling technology, the optimized implant and alveolar bone augmentation morphology design can meet individual needs to the greatest extent, ensure biomechanical performance and biocompatibility, and provide optimal design parameters.

[0204] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent design method for alveolar bone augmentation morphology based on a multi-agent system, characterized in that: The following steps are involved: S1. Obtain three-dimensional image data of the patient's alveolar bone and oral structure through computer tomography, and extract key biomechanical properties within the three-dimensional image data; S2. configuring agents in a multi-agent system, where each agent represents a specific clinical scenario or task, including osteoporosis, tooth alignment, age-related bone remodeling characteristics of patients, and bone tissue response under the influence of drugs; S3. The intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to the preset algorithm, and evaluates the risks and potential problems associated with implant placement, including evaluating the stress distribution and fracture risk of the alveolar bone through the bionic model; S4, the intelligent agents interact and collaborate with each other to optimize the shape, size and position of the implant by simulating different design schemes and implantation strategies, and at the same time design the alveolar bone augmentation morphology, confirm bone support and improve the integration of bone structure and implant; S5. Adjust the design of the alveolar bone and implant according to the simulation results, so that the design can meet individual needs and maximize implant stability and biocompatibility; S6. Generate the final design plan of the optimized alveolar bone and implant, and output surgical guidance and expected effect evaluation report.

2. The method for intelligent design of alveolar bone augmentation morphology based on a multi-agent system according to claim 1, characterized in that: The S1 comprises the following steps: S11. Use a computer tomography scanner to perform a full-scale scan of the patient's oral area to obtain three-dimensional image data D 3D ; S12, using image processing software to process the three-dimensional image data D 3D Preprocessing is performed, including image denoising, contrast enhancement, and edge sharpening. After preprocessing, three-dimensional image data is obtained. S13. Apply the deep learning-based adaptive threshold segmentation algorithm to separate the alveolar bone area from the 3D image data. The alveolar bone is segmented and an independent image data D is generated. AB ; Among them, T adaptive (i, j, k) is the adaptive threshold learned through deep neural network, which is dynamically adjusted according to local image features; S14. Using morphological methods to analyze independent image data D AB Further processing was performed to identify and extract key structural features of the alveolar bone, including trabecular structure and cortical bone thickness: Dilation operation: used to fill holes in the image and enhance structural features: Erosion operation: used to remove edge noise and maintain the main structural features: Opening operation: first perform an erosion operation, then a dilation operation to remove small objects and protruding parts: Closing operation: First perform the dilation operation, then the erosion operation, which is used to fill small holes and closely connected objects: Skeletonization operation: used to extract the trabecular structure and represent it as image data of the refined trabecular structure: Among them, B s Represents the structural element, used for morphological operations, (i, j, k) represents the pixel coordinates of the three-dimensional image data, b∈B s Represents the structural element B s The elements in represents the expansion operation, represents the corrosion operation, max represents the maximum value operation, min represents the minimum value operation, and N represents the number of iterations in the skeletonization operation; S15, using finite element analysis technology based on multi-scale modeling to perform mechanical simulation on the alveolar bone structure, calculate its stress distribution and deformation, and extract bone density ρ, bone structure integrity index I s , bone strength S b biomechanical properties; S16. Comprehensive bone density ρ and bone structure integrity index I s , bone strength S b Conduct a detailed assessment of the patient's alveolar bone to determine its implantability and potential risks; S17, the processed and analyzed three-dimensional image data D 3D and bone density ρ, bone structural integrity index I s , bone strength S b The results are imported into the multi-intelligence system.

3. The method for intelligent design of alveolar bone augmentation morphology based on a multi-agent system according to claim 2, characterized in that: The S2 comprises the following steps: S21. Configure each agent A in the multi-agent system i , where each agent represents a specific clinical situation or task; S22. Configured clinical scenarios or tasks include: Osteoporosis Agent A osteoporosis According to the patient's bone density ρ and bone structural integrity index I s Analysis of the impact of osteoporosis on implant placement: TO osteoporosis =FEA(ρ(x,y,z),I s (x,y,z)); Among them, ρ(x,y,z) is the bone density distribution, I s (x, y, z) is the distribution of bone structural integrity, FEA represents the finite element analysis function, which is used to analyze the influence of bone density and structural integrity on implant placement, Ω is the analysis area, is the gradient of bone density in the x direction, indicating the rate of change of bone density in the x direction, is the gradient of bone structural integrity in the y direction, indicating the rate of change of bone structural integrity in the y direction, is the gradient of bone structural integrity in the z direction, indicating the rate of change of bone structural integrity in the z direction, R osteoporosis Agent A is an osteoporosis agent osteoporosis The analysis result matrix represents the osteoporosis situation agent A osteoporosis Comprehensive assessment of the patient's osteoporosis status, including bone density, bone structural integrity, and fracture risk information; Teeth Arrangement Agent A teeth_alignment According to the patient's tooth arrangement and occlusion, optimize the position and angle of the implant: A teeth_alignment =Optimization(P i ,T i ); Among them, A teeth_alignment For the tooth arrangement agent, P i is the arrangement position matrix, which represents the arrangement of implants at different positions, T i is the arrangement angle matrix, which indicates the arrangement of implants at different angles. Optimization is the optimization function used to optimize the arrangement position and angle of implants. occlusion is the occlusal relationship matrix, which represents the occlusal relationship at position (x, y, z), V is the analysis volume, n is the number of implants, and R alignment is the response result of the tooth arrangement intelligent body, which represents the comprehensive result of optimizing the arrangement position and angle of the implant according to the tooth arrangement and occlusal relationship; Age-related bone remodeling agent A age_related Evaluate the response characteristics of bone tissue according to the patient's age and corresponding bone remodeling characteristics: A age_related =Age-RelatedModeling(α,β,G age (t)); Among them, α is the age correlation coefficient, which indicates the influence coefficient of age on bone remodeling characteristics, β is the time dependence coefficient, which indicates the influence coefficient of time on bone remodeling characteristics, G age (t) is the age-related bone remodeling characteristic function, which represents the bone remodeling characteristics at time t. is the temporal rate of change of bone remodeling characteristics, R age Age-related bone remodeling agent A age_related The analysis results represent the evaluation results of the bone tissue response characteristics according to the patient's age and the corresponding bone remodeling characteristics; Drugs affect Agent A medication Evaluate the effects of medications on bone tissue response and implant stability based on the patient's medication use: From medication =MedicationImpactAnalysis(D med ,C med ,γ,E med (t)); R medication =∫ Ω (D med ·C med (x,y,z)+γ·E med (t))dΩ; Among them, D med is the drug distribution matrix, which indicates the distribution of drugs at different locations, C med is the drug response coefficient matrix, which indicates the drug response coefficient to different positions; γ is the time correlation coefficient, which indicates the influence coefficient of time on drug effect; E med (t) is the drug effect function, Ω is the analysis area, D med ·C med (x, y, z) is the product of drug distribution and response, indicating the drug effect at position (x, y, z), γ·E med (t) is the effect of time on drug effect, indicating the drug effect at time t, R medication Represents the effect of drugs on agent A medication Evaluation of drug response in bone tissue; S23. Each agent A i Equipped with specific algorithms and data models to analyze relevant clinical data and generate preliminary design solutions: Among them, W i is the weight matrix, M i is the clinical data matrix; S24. The agents exchange information and work together through a preset communication protocol to form a comprehensive design solution: Among them, W ij is the weight matrix, M ij is the clinical data matrix, C ij is the communication protocol matrix; S25, information exchange between agents including stress distribution σ calculated in multi-scale modeling simulation i,j,k Evaluation of the suitability of drug response models: Among them, R i is the bone density matrix, T i is the stress distribution matrix, S i is the drug response matrix; S26. Through collaborative analysis between intelligent entities, optimize the alveolar bone augmentation morphology and implant design to meet individual needs and meet the requirements of biomechanical performance and biocompatibility: Among them, L i is the design matrix, F i is the biomechanical performance matrix, C i The biocompatibility matrix.

4. The method for intelligent design of alveolar bone augmentation morphology based on a multi-agent system according to claim 3, characterized in that: The bone density ρ is obtained by calculating the mass of bone tissue per unit volume: Among them, m bone Indicates the quality of bone tissue, V bone Represents the volume of bone tissue, Image data representing trabecular bone structure, ρ i,j,k represents the local bone density at the pixel coordinate (i, j, k) of the three-dimensional image data, V i,j,k represents the local volume at the pixel coordinate (i, j, k) of the three-dimensional image data; The bone structural integrity index I s By calculating the connection density and distribution of trabecular bone, we can obtain: Among them, C i,j,k is the bone structure connection density, N conn is the number of connections; The bone strength S b The maximum stress and strain of the alveolar bone under load conditions obtained in multi-scale modeling simulation are expressed as: S b =max i,j,k (s i,j,k ); Among them, σ i,j,k is the stress distribution calculated in the multi-scale modeling simulation.

5. The method for intelligent design of alveolar bone augmentation morphology based on a multi-agent system according to claim 1, characterized in that: The S3 comprises the following steps: S31. The intelligent agent automatically analyzes the quality and structural characteristics of the alveolar bone according to the preset algorithm, including calculating the bone density ρ and the bone structure integrity index I s ; Among them, ρ i,j,k represents the local bone density at the pixel coordinate (i, j, k) of the three-dimensional image data, C i,j,k is the bone structure connection density, N conn is the number of connections, It is independent image data; S32. The intelligent agent evaluates the stress distribution σ of the alveolar bone calculated in the multi-scale modeling simulation through a bionic model based on multi-scale modeling and finite element analysis technology. i,j,k and fracture risk: Where, E is the elastic modulus and ν is the Poisson's ratio; S33. Calculate the stress concentration and deformation of the alveolar bone during implant placement through mechanical simulation to assess the fracture risk R f : Among them, σ t is the tensile strength of bone tissue; S34. Based on the assessment results of stress distribution and fracture risk, the intelligent agent generates a risk assessment report, including identification of high-risk areas and suggestions for improvement measures.

6. The method for intelligent design of alveolar bone augmentation morphology based on a multi-agent system according to claim 5, characterized in that: The S4 comprises the following steps: S41, the agents exchange information and work together through a preset communication protocol. Each agent A i share and integrate their analysis results; Among them, Σ i is the covariance matrix, representing agent A i Covariance of the analysis result distribution; μ i is the mean vector, representing agent A i The mean of the analysis result distribution; x1 is the sample vector, which represents the specific data point of the current analysis; R i is the bone density matrix; T i is the stress distribution matrix, which indicates the stress distribution; S i is the drug response matrix, representing agent A i Assessment of response to medication; S42, by simulating different design solutions D sim and implantation strategies to evaluate the effects of implant shape, size, and position on alveolar bone augmentation morphology: Among them, L i (x, y, z) is the design matrix, which represents the specific parameters of the design solution at the position (x, y, z); F i (x, y, z) is the biomechanical performance matrix, which represents the biomechanical performance at the position (x, y, z); is the biocompatibility gradient, which indicates the rate of change of biocompatibility at position (x, y, z); Ω1 is the integration area, which indicates the spatial range of the design solution; S43, based on the collaborative analysis results between the intelligent agents, optimize the design parameters of the implant, including the shape P of the implant shape , size P size and position P position : Among them, W i (x, y, z) is the weight matrix, which represents the weight of the design parameter at the position (x, y, z); M i (x, y, z) is the clinical data matrix, which represents the clinical data at the position (x, y, z); V is the analysis volume; λ is the regularization parameter, which is used to balance the different objectives in the optimization process; n1 is the boundary normal vector, which represents the normal direction on the boundary; b1 is the boundary condition, which represents the preset requirements on the boundary; dV is the boundary integral region, which represents the spatial boundary of the design solution; S44. During the optimization process, confirm the bone support of the alveolar bone augmentation design and the integration of the implant and bone structure, and evaluate its stability and long-term effects; S45. Generate the final optimized design plan for the implant and alveolar bone augmentation morphology, and output detailed design parameters and expected effect evaluation report.

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