Dental implant optimization simulation design method based on AI simulation technology

Abstract

This invention discloses a method for optimizing and simulating dental implant design based on AI simulation technology, belonging to the field of artificial intelligence simulation technology in oral medicine. The method includes acquiring three-dimensional scans of the patient's oral cavity and jawbone images, fusing and reconstructing them to generate a personalized digital oral model. The model is then input into a pre-trained biomechanical simulation AI agent. This AI agent automatically explores within a solution space composed of multiple preset candidate implant models and combinations of implantation pose parameters. For each candidate scheme, dynamic mechanical load simulation is performed in the digital model, simulating stress distribution, bone interface micro-strain, and long-term stability indicators under different chewing conditions. Based on the simulation results, a multi-objective evaluation is performed to generate a comprehensive score. The scheme with the highest score is selected as the initial optimization design, and a surgical planning blueprint is generated. This invention achieves automated and intelligent optimization of implant surgery plans and quantitative prediction of biomechanical prognosis.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence simulation technology in oral healthcare, specifically a method for optimizing and simulating dental implant design based on AI simulation technology. Background Technology

[0002] Currently, preoperative planning for dental implant surgery heavily relies on the surgeon's clinical experience and static analysis of two-dimensional or three-dimensional medical images. Conventional techniques typically involve the surgeon manually selecting one or more familiar implant models based on the patient's jawbone images and determining the approximate placement and angle based on experience. The planning process is essentially a limited comparison of options based on anatomical morphology, with the core objective of avoiding important anatomical structures such as nerves and blood vessels and ensuring the implant is surrounded by bone tissue. Existing clinical planning tools lack the ability to provide in-depth, quantitative analysis of the long-term biomechanical performance after implantation.

[0003] This experience-based and static matching-based planning model has limitations. The number of possible combinations that doctors can manually assess is extremely limited. It's difficult to systematically find the best mechanically performing implant from a vast solution space containing combinations of parameters such as implant type, diameter, length, implantation depth, and tilt angle, potentially missing better options. Furthermore, there's a lack of a strong scientific link between planning decisions and the crucial prognostic indicator of long-term implant stability. Current technology cannot dynamically simulate and quantitatively predict the long-term functional performance of different options preoperatively, resulting in limitations in the scientific rigor and precision of surgical planning, and leaving some uncertainty regarding long-term postoperative outcomes. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art; Therefore, this invention proposes a dental implant optimization simulation design method based on AI simulation technology, including: Acquire 3D oral cavity scan data and jawbone medical imaging data of the target patient, and perform data fusion and 3D reconstruction to generate a personalized oral digital model; The personalized oral digital model is input into a pre-trained biomechanical simulation AI agent to generate simulation results. The simulation results are the dynamic mechanical loads of each group of candidate implant models and implantation posture parameters in different personalized oral digital models. The simulation results include the stress distribution of the implant under different chewing conditions, bone interface microstrain, and long-term stability indicators. Based on the simulation results, a comprehensive score including mechanical fit, surgical feasibility, and long-term prognosis prediction is generated for each candidate scheme through a biomechanical simulation AI agent. From all candidate solutions, the solution with the highest comprehensive score is selected as the initial optimized design solution, and a surgical planning blueprint is generated based on the initial optimized design solution through virtual surgical simulation.

[0005] Furthermore, the system acquires 3D oral cavity scan data and jawbone medical imaging data of the target patient, performs data fusion and 3D reconstruction, and generates a personalized oral digital model, including: Acquire 3D oral cavity scan data and jawbone medical imaging data of the target patient; Based on the oral cavity 3D scanning data and the jawbone medical imaging data, data fusion and 3D reconstruction are performed to generate a personalized oral digital model that includes alveolar bone density distribution, key nerve and blood vessel locations, and the root morphology of adjacent teeth. The acquisition of the target patient's oral cavity three-dimensional scan data and jawbone medical imaging data includes: The intraoral scanner is used to scan the edentulous area, opposing dentition, and occlusal relationship of the target patient to obtain high-precision three-dimensional oral scan data. Medical imaging data of the jaw region of a target patient is acquired using a cone-beam computed tomography (CBCT) scanner, and the medical imaging data includes multi-layer cross-sectional images.

[0006] Furthermore, based on the oral cavity 3D scan data and the jawbone medical imaging data, data fusion and 3D reconstruction are performed to generate a personalized oral digital model including alveolar bone density distribution, key neurovascular locations, and adjacent tooth root morphology, including: The oral cavity 3D scan data is processed into triangular meshes to generate a 3D surface model of teeth and gingival soft tissue; Threshold segmentation and three-dimensional voxel reconstruction are performed on the aforementioned jawbone medical imaging data to generate a three-dimensional jawbone model containing the cortical and cancellous bone structures. Based on feature point matching and iterative nearest point algorithm, the surface three-dimensional model of the teeth and gingival soft tissue is spatially registered and fused with the three-dimensional model of the jawbone. Based on the grayscale information of the jawbone medical imaging data, a density distribution map of the alveolar bone region is generated on the fused model. The locations of key neurovascular structures, including the mandibular canal, the floor of the maxillary sinus, and the mental foramen, are automatically identified and labeled in the three-dimensional model of the jawbone. Extract the geometric morphology of adjacent teeth from the oral cavity 3D scan data, and accurately reconstruct the 3D morphology and position of the adjacent tooth roots within the jawbone.

[0007] Furthermore, the personalized oral digital model is input into a pre-trained biomechanical simulation AI agent, the pre-training process of which includes: Construct a training dataset containing a large number of historical patients’ oral digital models, corresponding implant parameters, surgical records and long-term follow-up biomechanical data; Define simulation objectives, including minimizing peak stress around the implant, homogenizing stress distribution, and keeping the micro-strain at the bone interface within the range of physiological stimulation. Using the training dataset, an AI agent based on a deep reinforcement learning framework is trained, enabling the AI ​​agent to learn to actively search for and evaluate the impact of different implant models and implantation pose parameter combinations on achieving the simulation goal in a given oral digital model environment. Through repeated iterations and optimization of the reward mechanism, the biomechanical simulation AI agent is able to quickly and accurately simulate complex biomechanical responses.

[0008] Furthermore, the biomechanical simulation AI agent automatically explores and performs simulation calculations within a solution space composed of preset combinations of various candidate implant models and implantation pose parameters, including: The biomechanical simulation AI agent reads available implant diameter, length, thread design, and material parameters from the implant model library to form the model dimension; The biomechanical simulation AI agent defines the implantation pose parameters, which include implantation depth, implantation angle, buccal-lingual position, and mesio-distal position. The biomechanical simulation AI agent combines the model dimension with the implantation pose parameter dimension to form a multidimensional solution space; The biomechanical simulation AI agent adopts an adaptive sampling strategy, selecting representative parameter combinations for priority simulation calculations within the multidimensional solution space.

[0009] Furthermore, the biomechanical simulation AI agent performs dynamic mechanical load simulation on the personalized oral digital model for each group of candidate implant models and implantation posture parameters, simulating the stress distribution, bone interface micro-strain, and long-term stability indicators of the implant under different chewing conditions, including: Based on the implant model and implantation position parameters, a virtual three-dimensional implant entity is generated at the corresponding position in the personalized oral digital model; Multiple standard chewing conditions are set, including anterior tooth cutting, posterior tooth grinding, and lateral movement; A dynamic load force vector within the physiological range is applied to each of the aforementioned standard chewing conditions, the magnitude, direction, and point of application of which vary over time; Perform finite element analysis or smoothed particle fluid dynamics simulation to calculate the instantaneous stress and strain fields of the virtual implant three-dimensional entity and its surrounding bone tissue under the action of the dynamic load force vector; The stress peak values ​​and distribution cloud maps of the implant neck, body, and root tip were extracted from the simulation results. Calculate the microstrain values ​​of each region at the implant-osseointegration interface and count the proportion of them falling within the physiological strain range; Based on a pre-defined biomechanical model of bone remodeling, the adaptive remodeling trend of bone tissue is predicted according to the long-term stress-strain stimulation history, and a long-term stability index is generated.

[0010] Furthermore, the biomechanical simulation AI agent performs multi-objective evaluation on each candidate scheme based on the simulation results, generating a comprehensive score that includes mechanical fit, surgical feasibility, and long-term prognosis prediction, including: The biomechanical simulation AI agent calculates the mechanical risk coefficient based on whether the peak stress of the implant and surrounding bone tissue exceeds the material yield strength or the bone tissue tolerance threshold, and converts it into a mechanical fit score. The biomechanical simulation AI agent analyzes the minimum distance between the implantation pose parameters and the locations of key nerves and blood vessels, the morphology of adjacent tooth roots, and the anatomical boundaries of alveolar bone, assesses the safety margin and difficulty of the surgical operation, and generates a surgical feasibility score. The biomechanical simulation AI agent integrates the long-term stability indicators and individual patient characteristics to predict the success probability of long-term implant retention and generate a long-term prognosis prediction score. The biomechanical simulation AI agent assigns preset weight coefficients to the mechanical fit score, the surgical feasibility score, and the long-term prognosis prediction score, and performs a weighted summation to obtain a comprehensive score for each group of candidate solutions.

[0011] Furthermore, from all candidate solutions, the solution with the highest comprehensive score is selected as the initial optimized design solution, and a surgical planning blueprint is generated based on the initial optimized design solution through virtual surgical simulation, including: From all candidate options, the implant model and implantation position parameter combination with the highest comprehensive score were selected as the initial optimization design scheme. Based on the initial optimized design scheme, a virtual surgical simulation is performed in the personalized oral digital model to generate a surgical planning blueprint that includes the implantation path, cavity preparation sequence, and suggestions for handling adjacent structures. By combining the surgical planning blueprint with the initial optimized design scheme, a final visual dental implant optimized design scheme report is generated; From all candidate options, the implant model and implantation position parameter combination with the highest overall score were selected as the initial optimization design scheme, including: The biomechanical simulation AI agent sorts all candidate solutions and their corresponding comprehensive scores. The top three candidate solutions based on overall scores were selected. Sensitivity analysis was performed on the top three candidate solutions based on the comprehensive score to verify their performance robustness when there are minor perturbations in the bone mineral density parameters and load conditions of the personalized oral digital model. From the candidate solutions that pass the sensitivity analysis, the solution with the highest comprehensive score is selected as the initial optimized design solution.

[0012] Furthermore, based on the initial optimized design scheme, a virtual surgical simulation is performed in the personalized oral digital model to generate a surgical planning blueprint that includes the implantation path, cavity preparation sequence, and suggestions for handling adjacent structures, including: In the personalized oral digital model, the virtual implantation path of the surgical instruments is planned in reverse, with the implantation pose defined by the initial optimized design scheme as the final target position. Simulate the step-by-step preparation process to determine the required drill bit diameter sequence, preparation depth, and cooling scheme; Check whether the virtual implantation path and preparation process interfere with the location of key nerves and blood vessels and the morphology of adjacent tooth roots in the three-dimensional model of the jawbone, and generate adjustment suggestions; Based on the morphology of gingival soft tissue, suggestions are made on incision design, flap treatment, or treatment of adjacent structures of immediate restorations; The virtual implantation path, the prepared hole sequence, adjustment suggestions, and adjacent structure processing suggestions are integrated to form the surgical planning blueprint.

[0013] Furthermore, combining the surgical planning blueprint with the initial optimized design scheme, a final visual dental implant optimized design scheme report is generated, including: The implant 3D model in the initial optimized design scheme is integrated into the personalized oral digital model according to the determined model and position to generate a 3D visualization view of the implantation effect. The key steps and parameters in the surgical planning blueprint are marked with two-dimensional diagrams and text annotations; The scores and analytical basis for the mechanical fit, surgical feasibility, and long-term prognosis prediction provided by the biomechanical simulation AI agent are compiled. All visualizations, diagrams, annotations, and analytical data are compiled into a structured electronic document to form the visualization dental implant optimization design report.

[0014] Compared with the prior art, the beneficial effects of the present invention are: A pre-trained biomechanical simulation AI agent is employed to automatically search and calculate within a solution space comprised of multiple parameters, including implant type, size, implantation angle, and depth. This method replaces manual trial-and-error planning that relies on the physician's personal experience. The AI ​​agent can systematically traverse and simulate and evaluate a large number of candidate parameter combinations, with computational efficiency and coverage far exceeding that of manual operation. This process directly performs parallel screening and ranking of massive solutions from a mechanical performance perspective. The final design solution is based on the result of global simulation optimization, rather than a local selection under limited experience, thereby improving the objectivity and mechanical optimality of the planning solution.

[0015] For each candidate design, dynamic mechanical load simulations were performed to model stress transmission and bone microstrain distribution under various typical chewing conditions. Based on these physical simulation results, multiple quantitative indicators, including mechanical fit, surgical feasibility, and long-term prognostic predictability, were simultaneously calculated and comprehensively evaluated. This method extends the dimensions of preoperative assessment from static anatomical spatial matching to dynamic biomechanical functional simulation. By directly quantifying the mechanical behavior of implants under complex cyclic loading, the potential impact of different designs on bone remodeling can be assessed in advance, thus providing direct simulation data support for predicting long-term clinical stability. Planning decisions are therefore established with a quantifiable scientific link to long-term prognosis. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the dental implant optimization simulation design method based on AI simulation technology described in this invention. Figure 2 A flowchart for data fusion and 3D reconstruction; Figure 3 A bar chart showing the peak stress of implants of different diameters under three chewing conditions; Figure 4 A multi-dimensional radar chart of candidate dental implant schemes; Figure 5 A biaxial graph comparing the performance of four virtual implantation path planning algorithms for dental implants. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] See Figure 1The procedure involves acquiring 3D oral cavity scans and jawbone medical imaging data from the target patient, fusing and reconstructing these two types of data to generate a personalized digital oral model that reflects the patient's unique anatomy. This model is then input into a pre-trained biomechanical simulation AI agent. This AI agent automatically explores and performs simulation calculations within a solution space composed of various preset combinations of candidate implant models and implantation pose parameters. For each set of candidate implant models and implantation pose parameters, the biomechanical simulation AI agent performs dynamic mechanical load simulations within the personalized digital oral model, simulating the stress distribution and bone interface micro-strain of the implant under various masticatory conditions, and calculating long-term stability indices. Based on the simulation results, the biomechanical simulation AI agent performs multi-objective evaluations of each candidate scheme, generating a comprehensive score that includes mechanical fit, surgical feasibility, and long-term prognostic prediction. From all candidate schemes, the scheme with the highest comprehensive score is selected as the initial optimized design scheme, and a virtual surgical simulation is performed based on this scheme, ultimately generating a detailed surgical planning blueprint.

[0019] In one embodiment of the present invention, see [reference] Figure 2 When acquiring 3D oral cavity scan data and jawbone medical imaging data of the target patient, an intraoral scanner is used to scan the edentulous area, opposing dentition, and occlusal relationship of the target patient to obtain high-precision 3D oral cavity scan data. A cone-beam computed tomography (CBCT) device is used to acquire medical imaging data of the jawbone region of the target patient, which includes multi-layer cross-sectional images. Based on the 3D oral cavity scan data and the jawbone medical imaging data, data fusion and 3D reconstruction are performed to generate a personalized digital oral model including alveolar bone density distribution, key neurovascular locations, and adjacent tooth root morphology. This process includes triangulating the 3D oral cavity scan data to generate a surface 3D model of the teeth and gingival soft tissue; performing threshold segmentation and 3D voxel reconstruction on the jawbone medical imaging data to generate a 3D jawbone model including cortical and cancellous bone structures; and spatially registering and fusing the surface 3D model of the teeth and gingival soft tissue with the jawbone 3D model based on feature point matching and iterative nearest-point algorithms. On the fused model, a density distribution map of the alveolar bone region is generated by mapping the grayscale information of the jawbone medical imaging data. The locations of key neurovascular structures, including the mandibular canal, maxillary sinus floor, and mental foramen, are automatically identified and labeled in the 3D model of the jawbone. The geometric morphology of adjacent teeth is extracted from the 3D oral cavity scan data to accurately reconstruct the 3D morphology and position of the adjacent tooth roots within the jawbone.

[0020] In practice, when acquiring 3D oral cavity scan data of the target patient, the operator uses an intraoral scanner to perform a complete scan of the edentulous area, opposing dentition, and occlusal relationship. The scanning process captures tooth surface morphology, gingival margins, and dynamic occlusal contact relationships, thereby obtaining high-precision 3D oral cavity scan data. This data is stored as surface topology information in the form of point clouds or triangular meshes. In acquiring jawbone medical imaging data of the target patient, a cone-beam computed tomography (CBCT) scanner is used to image the jawbone region. The CBCT generates medical imaging data containing multiple cross-sectional images, each reflecting the bone tissue structure, nerve canals, and tooth root morphology at a specific cross-section.

[0021] In some embodiments, data fusion and 3D reconstruction are performed based on oral cavity 3D scanning data and jawbone medical imaging data to generate a personalized oral digital model including alveolar bone density distribution, key neurovascular locations, and adjacent tooth root morphology. The oral cavity 3D scanning data undergoes triangulation processing, including point cloud denoising, surface reconstruction, and mesh smoothing, to generate a surface 3D model of teeth and gingival soft tissue. The jawbone medical imaging data undergoes threshold segmentation and 3D voxel reconstruction. Threshold segmentation distinguishes bone tissue, soft tissue, and cavities based on image grayscale, while 3D voxel reconstruction stacks the segmented 2D sequences into 3D volumetric data, generating a 3D jawbone model including cortical and cancellous bone structures.

[0022] It is understandable that the 3D surface model of teeth and gingival soft tissue is spatially registered and fused with the 3D model of the jawbone based on feature point matching and iterative nearest-point algorithm. Feature point matching selects landmark points on the occlusal surface of teeth or crown morphological feature points. The iterative nearest-point algorithm achieves precise alignment by minimizing the overall distance error between the two point clouds. Spatial registration and fusion establish a unified coordinate system, ensuring that the anatomical positions of the soft tissue surface model and the internal bone tissue model are consistent. The registration process aims to minimize the registration error, which is calculated by the following formula: in: Indicates the total registration error. Indicates the number of matching point pairs. The third dimension represents the surface three-dimensional model derived from teeth and gum soft tissue. One point, Indicates that it comes from the three-dimensional model of the jawbone and The corresponding nearest point, Represents the rotation matrix. This represents the translation vector. On the fused model, a density distribution map of the alveolar bone region is generated by mapping the grayscale information from jawbone medical imaging data. The grayscale information is positively correlated with bone mineral density. The mapping process converts the grayscale value of each voxel unit into the corresponding bone density value, and then overlays it on the surface and interior of the jawbone 3D model in pseudo-color or numerical field form.

[0023] Optionally, the locations of key neurovascular structures such as the mandibular nerve canal, maxillary sinus floor, and mental foramen are automatically identified and labeled in the 3D model of the jawbone. The automatic identification process employs an image segmentation algorithm based on grayscale thresholding and region growing to extract continuous low-grayscale tubular or cavitary structures from jawbone medical imaging data. By comparing spatial relationships with standard anatomical atlases, the course of the mandibular nerve canal, the contour of the maxillary sinus floor, and the location of the mental foramen opening are determined and labeled. The geometric morphology of adjacent teeth is extracted from the 3D oral cavity scan data, and the 3D morphology and position of the adjacent tooth roots within the jawbone are accurately reconstructed. The reconstruction process is based on the anatomical proportions of the crown and root, and references the root portion visible in cone-beam computed tomography (CBCT) images. A complete 3D model of the adjacent tooth roots is constructed within the 3D jawbone model using surface extension and smoothing algorithms.

[0024] In one embodiment of the present invention, the personalized oral digital model is input into a pre-trained biomechanical simulation AI agent. The pre-training process of the biomechanical simulation AI agent includes constructing a training dataset containing a large number of historical patients' oral digital models, corresponding implant parameters, surgical records, and long-term follow-up biomechanical data. Simulation objectives are defined, including minimizing peak peri-implant bone stress, homogenizing stress distribution, and ensuring that bone interface microstrain is within physiological stimulation ranges. Using the training dataset, the AI ​​agent, based on a deep reinforcement learning framework, is trained to actively search for and evaluate the impact of different implant models and combinations of implantation pose parameters on achieving the simulation objectives within a given oral digital model environment. Through iterative iteration and reward mechanism optimization, the biomechanical simulation AI agent develops the ability to rapidly and accurately simulate complex biomechanical responses.

[0025] In the implementation, a training dataset was constructed, containing a large number of historical patient oral digital models, corresponding implant parameters, surgical records, and long-term follow-up biomechanical data. The historical patient oral digital models were derived from archived intraoral scan data and cone-beam computed tomography (CBCT) scan data. The corresponding implant parameters included the implant brand, diameter, length, thread pitch, and surface treatment. Surgical records included the actual three-dimensional position and axial angle of the implant. Long-term follow-up biomechanical data were obtained through regularly taken postoperative images and occlusal force measurement equipment. The simulation objectives were defined as minimizing the peak peri-implant bone stress, homogenizing stress distribution, and ensuring that the microstrain at the bone interface was within the physiological stimulation range. Minimizing the peak peri-implant bone stress aimed to reduce the risk of local bone resorption; homogenizing stress distribution aimed to achieve reasonable load transfer between the implant and the bone interface; and ensuring that the microstrain at the bone interface was within the physiological stimulation range meant that the microstrain value should fall within a favorable range that promotes bone remodeling.

[0026] In some embodiments, a deep reinforcement learning-based AI agent is trained using a training dataset. The training process enables the AI ​​agent to actively search for and evaluate the impact of different implant models and combinations of implantation pose parameters on achieving the simulation goal within a given oral digital model environment. The deep reinforcement learning framework uses the oral digital model as the environmental input, changes in implant model and implantation pose parameters as the agent's actions, and the degree of fit between the calculated biomechanical simulation results and the simulation goal as reward feedback. Through extensive trial and error and learning, the AI ​​agent gradually establishes a mapping relationship between complex oral anatomy conditions and optimal implant parameter decisions.

[0027] It is understandable that through iterative iteration and reward mechanism optimization, the biomechanical simulation AI agent can develop the ability to quickly and accurately simulate complex biomechanical responses. Iterative iteration refers to the AI ​​agent repeatedly executing simulation, evaluation, and parameter update processes on multiple different cases in the training dataset. The reward mechanism calculates a comprehensive reward value based on each simulation result to guide the AI ​​agent's policy optimization. Optionally, preprocessing of long-term follow-up biomechanical data in the training dataset includes 3D reconstruction and comparison of image data, quantifying the annual rate of change in peri-implant bone mass, and correlating the rate of change with initial implantation parameters. The specific implementation of the deep reinforcement learning framework can employ proximal policy optimization or deep deterministic policy gradient algorithms. The agent's neural network architecture includes convolutional modules for feature extraction and fully connected modules for decision generation. The quantitative indicators of the simulation objectives need to be clearly defined before training, such as the upper and lower thresholds of the physiological strain range and the calculation standards for stress distribution uniformity.

[0028] In one embodiment of the present invention, when the biomechanical simulation AI agent automatically explores and performs simulation calculations within a solution space composed of a preset combination of multiple candidate implant models and implantation pose parameters, the biomechanical simulation AI agent reads available implant diameter, length, thread design, and material parameters from the implant model library to form a model dimension; it defines implantation pose parameters, including implantation depth, implantation angle, buccal-lingual position, and mesiodistal position. The biomechanical simulation AI agent combines the model dimension with the implantation pose parameter dimension to form a multidimensional solution space; and employs an adaptive sampling strategy to select representative parameter combinations within the multidimensional solution space for priority simulation calculations. The biomechanical simulation AI agent performs dynamic mechanical load simulation on the personalized oral digital model for each candidate implant model and implantation posture parameters. This simulation measures the stress distribution, bone interface micro-strain, and long-term stability indicators of the implant under different chewing conditions. Based on the implant model and implantation posture parameters, the simulation process generates a virtual 3D implant entity at the corresponding position in the personalized oral digital model. Multiple standard chewing conditions are set, including anterior tooth cutting, posterior tooth grinding, and lateral movement. A dynamic load force vector within the physiological range is applied to each of these standard chewing conditions. The magnitude, direction, and point of application of the dynamic load force vector change over time; finite element analysis or smoothed particle fluid dynamics simulation is performed to calculate the instantaneous stress and strain fields of the virtual implant 3D entity and its surrounding bone tissue under the action of the dynamic load force vector; stress peak values ​​and distribution cloud maps of the implant neck, body, and root apex are extracted from the simulation results; micro-strain values ​​of each region of the implant-bone interface are calculated, and the proportion falling within the physiological strain range is statistically analyzed; based on the preset biomechanical theory model of bone remodeling, the adaptive remodeling trend of bone tissue is predicted according to the long-term stress and strain stimulation history, and long-term stability indicators are generated. Based on simulation results, the biomechanical simulation AI agent performs multi-objective evaluations on each candidate scheme, generating a comprehensive score that includes mechanical fit, surgical feasibility, and long-term prognostic prediction. The evaluation process involves the biomechanical simulation AI agent calculating a mechanical risk coefficient based on whether the peak stress of the implant and surrounding bone tissue exceeds the material yield strength or bone tissue tolerance threshold, and converting this coefficient into a mechanical fit score. It analyzes the minimum distance between the implantation posture parameters and the locations of key neurovascular structures, adjacent tooth root morphology, and alveolar bone anatomical boundaries to assess the safety margin and difficulty of the surgical procedure, generating a surgical feasibility score. It integrates long-term stability indicators and individual patient characteristics to predict the success probability of long-term implant retention, generating a long-term prognostic prediction score. The biomechanical simulation AI agent assigns preset weighting coefficients to the mechanical fit score, surgical feasibility score, and long-term prognostic prediction score, performs a weighted summation, and obtains the comprehensive score for each candidate scheme.

[0029] In practical implementation, the biomechanical simulation AI agent reads available implant diameter, length, thread design, and material parameters from the implant model library to form the model dimension. The implant model library is a database storing the geometric and physical attributes of various commercial implant systems. In practical implementation, the biomechanical simulation AI agent defines implantation pose parameters, including implantation depth, implantation angle, buccal-lingual position, and mesiodistal position. Implantation depth is defined as the vertical distance of the implant platform relative to the preset alveolar ridge crest plane, and implantation angle is defined as the three-dimensional spatial angle between the implant's long axis and the preset reference plane. The biomechanical simulation AI agent combines the model dimension and the implantation pose parameter dimension to form a multidimensional solution space. Each point in the multidimensional solution space represents a set of implant models and implantation pose parameter combinations. The biomechanical simulation AI agent employs an adaptive sampling strategy, selecting representative parameter combinations within the multidimensional solution space for priority simulation calculations. The adaptive sampling strategy predicts the performance of unsampled areas based on previous simulation results and dynamically adjusts the distribution of sampling points to focus on potentially high-scoring areas.

[0030] In some embodiments, the biomechanical simulation AI agent performs dynamic mechanical load simulation in a personalized oral digital model for each candidate implant model and implantation posture parameters, simulating the stress distribution, bone interface micro-strain, and long-term stability indicators of the implant under different chewing conditions. Based on the implant model and implantation posture parameters, a virtual 3D implant entity is generated at the corresponding position in the personalized oral digital model. This virtual 3D implant entity has precise thread geometry, platform structure, and internal connection morphology. Multiple standard chewing conditions are set, including anterior tooth cutting, posterior tooth grinding, and lateral movement. Each standard chewing condition corresponds to a mandibular movement trajectory and food load type. A dynamic load force vector conforming to physiological range is applied to each standard chewing condition. The magnitude, direction, and point of application of the dynamic load force vector change over time according to the chewing cycle, and its numerical range is referenced from clinical occlusal force measurement research data. Perform finite element analysis or smoothed particle fluid dynamics simulation to calculate the instantaneous stress and strain fields of the virtual implant 3D entity and its surrounding bone tissue under dynamic load force vector. The calculation process models the bone tissue as an anisotropic or orthotropic elastoplastic material.

[0031] It is understandable that the stress peak values ​​and distribution maps of the implant neck, body, and apex are extracted from the simulation results. The stress peak value is the maximum von Mises stress value in a specific region, and the distribution map uses a color gradient to show the stress magnitude variation across the entire structure. The micro-strain values ​​of each region at the implant-bone integration interface are calculated, and the proportion falling within the physiological strain range is statistically analyzed. The physiological strain range is defined as the micro-strain range that can maintain bone balance or promote bone formation. Based on a pre-defined biomechanical model of bone remodeling, the adaptive remodeling trend of bone tissue is predicted according to the long-term stress-strain stimulation history, generating long-term stability indicators. The biomechanical model of bone remodeling describes the quantitative relationship between bone resorption and bone formation rates and local mechanical stimulation.

[0032] Optionally, the biomechanical simulation AI agent performs multi-objective evaluations on each candidate scheme based on simulation results, generating a comprehensive score including mechanical fit, surgical feasibility, and long-term prognostic prediction. The biomechanical simulation AI agent calculates a mechanical risk coefficient based on whether the peak stress of the implant and surrounding bone exceeds the material yield strength or bone tissue tolerance threshold, and converts it into a mechanical fit score. The conversion process uses linear or nonlinear mapping functions to normalize the risk coefficient to a value within a preset score range. The biomechanical simulation AI agent analyzes the minimum distance between the implantation pose parameters and the locations of key neurovascular structures, adjacent tooth root morphology, and alveolar bone anatomical boundaries to assess the safety margin and difficulty of the surgical procedure, generating a surgical feasibility score. The minimum distance is obtained through three-dimensional spatial geometric calculations. The biomechanical simulation AI agent integrates long-term stability indicators and individual patient characteristics to predict the success probability of long-term implant retention, generating a long-term prognostic prediction score. Individual patient characteristics include age, bone density, overall health status, and oral hygiene habits. The biomechanical simulation AI agent assigns preset weight coefficients to the mechanical fit score, surgical feasibility score, and long-term prognosis prediction score, and performs a weighted sum to obtain a comprehensive score for each group of candidate solutions. The comprehensive score is calculated according to the following formula: in: This indicates the final overall score. , , These are pre-defined positive weighting coefficients that sum to 1. This represents the mechanical fit score. This indicates the feasibility score of the surgery. This represents the long-term prognostic predictive value.

[0033] See Figure 3This is a bar chart showing the peak stress of implants of different diameters under three chewing conditions, reflecting the core results of implant biomechanical simulation. In all three chewing conditions, the peak stress generated by posterior tooth grinding is consistently the highest, followed by lateral movement, and the lowest is generated by anterior tooth cutting. This aligns with clinical reality, as posterior teeth bear the primary chewing and grinding functions and experience greater stress. As the implant diameter increases from 3.5 mm to 6.0 mm, the peak stress under all three conditions shows a significant decreasing trend. For example, the stress in the posterior tooth grinding condition decreases from approximately 150 MPa to approximately 115 MPa. This indicates that larger diameter implants can more effectively distribute stress, reducing the mechanical burden on bone tissue and the implant. This result can provide a basis for implant size selection; where bone conditions permit, selecting larger diameter implants helps improve mechanical stability and reduce the risk of bone resorption.

[0034] In one embodiment of the present invention, the scheme with the highest comprehensive score is selected as the initial optimized design scheme from all candidate schemes. When generating a surgical planning blueprint for virtual surgical pre-simulation based on the initial optimized design scheme, the biomechanical simulation AI agent sorts all candidate schemes and their corresponding comprehensive scores; selects the top three candidate schemes with the highest comprehensive scores; performs sensitivity analysis on the top three candidate schemes with the highest comprehensive scores to verify their performance robustness when there are minor perturbations in the bone density parameters and load conditions of the personalized oral digital model; and finally selects the scheme with the highest comprehensive score from the candidate schemes that pass the sensitivity analysis as the initial optimized design scheme.

[0035] In practice, the biomechanical simulation AI agent sorts all candidate solutions and their corresponding comprehensive scores. The sorting process is based on descending order of the comprehensive score values, generating a sequence of candidate solutions from best to worst. The biomechanical simulation AI agent then selects the top three candidate solutions based on their comprehensive scores. This selection process extracts the top three records from the sorted sequence; these records contain complete implant models, implantation pose parameters, and various evaluation scores. The biomechanical simulation AI agent performs sensitivity analysis on the top three candidate solutions to verify their robustness under minor perturbations in bone mineral density parameters and loading conditions within the personalized oral digital model. The sensitivity analysis aims to assess the stability of the simulation output results when the input parameters have a reasonable range of uncertainty.

[0036] In some embodiments, when performing sensitivity analysis on the top three candidate schemes based on comprehensive scores, it is necessary to define a set of perturbation variables and set their perturbation range for each scheme. The perturbation of bone mineral density parameters is achieved by uniformly adjusting the density values ​​mapped to different regions of the jawbone in the personalized oral digital model. The perturbation of load conditions is achieved by proportionally changing the magnitude of the dynamic load force vector or slightly adjusting its direction cosine. For each set of perturbed input parameters, the dynamic mechanical load simulation is re-executed, and the mechanical fit, surgical feasibility, and long-term prognostic predictivity scores of the scheme under the new parameter conditions are calculated. By comparing the variation amplitude of simulation results under the original parameters and multiple sets of perturbation parameters, the performance stability of each candidate scheme is quantified; schemes with smaller variation amplitudes are considered to have better robustness.

[0037] It is understandable that, from the candidate solutions that pass the sensitivity analysis, the solution with the highest overall score is ultimately selected as the initial optimized design. A candidate solution that passes the sensitivity analysis is one whose overall score or key score does not decrease beyond an allowable threshold during multiple pre-set perturbation tests. If the top-ranked solution exhibits excessive performance fluctuations in the sensitivity analysis, while the second or third-ranked solution demonstrates better stability, then the solution with the highest overall score and that passes robustness verification is selected as the initial optimized design. This decision-making logic ensures that the selected solution not only performs optimally under ideal conditions but also remains reliable under real-world parameter fluctuations. The quantitative evaluation of sensitivity analysis can be calculated using the following formula to determine the sensitivity coefficient of a solution: in: This represents the sensitivity coefficient of the proposed solution. This indicates the total number of preset disturbance tests. This represents the overall score calculated under the original, unperturbed parameters. Indicates the first The comprehensive score is calculated under the parameters of the disturbance test. The lower the value, the less sensitive the scheme is to parameter disturbances and the stronger its robustness.

[0038] Optionally, the perturbation range for bone mineral density parameters and loading conditions is set based on the common range of variation in clinical measurements. The raw and perturbed key assessment data recorded during the sensitivity analysis can be formatted for intuitive comparison; see Table 1.

[0039] Table 1: Comparison of Perturbation Variables and Results in Sensitivity Analysis of Candidate Solutions In some embodiments, the preset allowable threshold can be an absolute score difference or a relative percentage change. When finally determining the initial optimized design scheme, if the overall scores are very close, the sensitivity coefficient... This will become an important basis for decision-making.

[0040] See Figure 4 This is a multi-dimensional radar chart of candidate dental implant options, visually comparing Option 1, Option 2, and Option 3 across five dimensions: biomechanical fit, surgical feasibility, long-term prognostic predictability, robustness score, and overall score. The overall score, weighted by multiple dimensions, shows Option 2 (approximately 90 points) is comparable to Option 1 (approximately 90 points), but Option 2 demonstrates superior robustness, making it more advantageous in high-risk clinical scenarios. While Option 1 excels in surgical and prognostic dimensions, its robustness is insufficient, requiring stable bone conditions. For patients with complex anatomy and a high risk of bone condition fluctuations, Option 2 is the preferred choice; for patients with stable bone conditions and prioritizing surgical convenience, Option 1 is a better option. Although Option 3 exhibits strong robustness, its surgical feasibility and prognostic performance are average, making it suitable only for special cases with extreme stability requirements. This provides intuitive quantitative evidence for the performance verification of multi-objective optimization algorithms and can be used for comparing the effectiveness of different options in academic research.

[0041] In one embodiment of the present invention, based on the initial optimized design scheme, when performing virtual surgical rehearsal in the personalized oral digital model to generate a surgical planning blueprint containing implantation path, cavity preparation sequence, and adjacent structure treatment suggestions, the virtual implantation path of the surgical instruments is planned in reverse in the personalized oral digital model with the implantation pose defined by the initial optimized design scheme as the final target position; the step-by-step cavity preparation process is simulated to determine the required drill diameter sequence, cavity preparation depth, and cooling scheme; whether the virtual implantation path and cavity preparation process interfere with the key neurovascular locations and adjacent tooth root morphology in the three-dimensional jawbone model, and adjustment suggestions are generated; for the gingival soft tissue morphology, suggestions for incision design, valve treatment, or adjacent structure treatment of immediate restorations are proposed; the virtual implantation path, cavity preparation sequence, adjustment suggestions, and adjacent structure treatment suggestions are integrated to form the surgical planning blueprint. When generating the final visualized dental implant optimization design report by combining the surgical planning blueprint and the initial optimized design scheme, the 3D model of the implant in the initial optimized design scheme is integrated into the personalized oral digital model according to the determined model and position to generate a 3D visualized view of the implantation effect; the key steps and parameters in the surgical planning blueprint are marked with 2D diagrams and text annotations; the mechanical fit, surgical feasibility, and long-term prognosis prediction scores and analysis basis provided by the biomechanical simulation AI agent are organized; and all visualized views, diagrams, annotations, and analysis data are compiled into a structured electronic document to form the visualized dental implant optimization design report.

[0042] In practical implementation, when generating a surgical planning blueprint through virtual surgical simulation in a personalized oral digital model based on the initial optimized design scheme, the virtual implantation path of the surgical instruments is planned backwards using the implantation pose defined in the initial optimized design scheme as the final target position. This backward planning involves tracing the movement trajectory of the surgical drill or implant from the entry point to the target point, starting from the target implantation position. The simulation of the step-by-step cavity preparation process determines the required drill diameter sequence, cavity depth, and cooling scheme. The diameter sequence is arranged from smallest to largest according to the surgical guidelines for the selected implant system. The cavity depth is calculated based on the implantation depth and implant length in the initial optimized design scheme. The cooling scheme involves simulating the flow rate and timing of saline irrigation. The virtual implantation path and cavity preparation process are checked for interference with the locations of key neurovascular structures and adjacent tooth roots in the three-dimensional model of the jawbone, and adjustment suggestions are generated. Interference checks are performed by calculating the minimum spatial distance between the three-dimensional models of the surgical instruments and the three-dimensional models of key anatomical structures.

[0043] In some embodiments, suggestions are made regarding incision design, flap treatment, or adjacent structure treatment for immediate restorations, taking into account the morphology of the gingival soft tissue. Incision design is based on the morphology of the alveolar ridge crest and the width of the keratinized gingiva in the edentulous area. Flap treatment suggestions concern the flap elevation range and suture location for full-thickness or half-thickness flaps. Adjacent structure treatment suggestions concern the morphological design of the transgingival contour of temporary or permanent restorations. The virtual implantation path, cavity preparation sequence, adjustment suggestions, and adjacent structure treatment suggestions are integrated to form a surgical planning blueprint. During the integration process, all information is labeled and correlated in a unified three-dimensional coordinate system, generating an interactive digital file.

[0044] It is understandable that when generating the final visualized dental implant optimization design report by combining the surgical planning blueprint and the initial optimized design scheme, the 3D model of the implant in the initial optimized design scheme is integrated into the personalized oral digital model according to the determined model and position, generating a 3D visualized view of the implantation effect. The integration operation is completed in the 3D modeling software through Boolean operations and assembly functions. Key steps and parameters in the surgical planning blueprint are marked with 2D diagrams and text annotations. The 2D diagrams extract key cross-sectional views from the 3D model, and the text annotations explain the specific dimensions, angles, and operation sequence. The scores and analysis basis of the mechanical fit, surgical feasibility, and long-term prognosis prediction provided by the biomechanical simulation artificial intelligence agent are compiled. The analysis basis includes the specific stress peak value, safety distance value, and bone remodeling prediction curve extracted during the simulation process.

[0045] Optionally, all visualizations, diagrams, annotations, and analytical data are compiled into a structured electronic document to form a visualized dental implant optimization design report. The electronic document uses a hierarchical structure, including a patient information summary, a 3D model visualization and interactive module, a detailed surgical procedure module, and a biomechanical analysis data module. The accuracy of the virtual implantation path in the surgical planning blueprint can be assessed by calculating the average deviation between the planned path and the ideal straight path. The deviation calculation follows the formula: in: This represents the average geometric deviation of the virtual implantation path. This indicates the number of points sampled along the path. Indicates the first The three-dimensional coordinates of each sampling point on the virtual implantation path Indicates the first The three-dimensional coordinates of each sampling point on the ideal straight path connecting the entry point and the target point. This indicates the calculation of the Euclidean distance between two points.

[0046] In some embodiments, generating a 3D visualization of the implantation effect allows for multi-angle rotation, sectioning, and transparent rendering to observe the three-dimensional relationship between the implant and bone tissue, and adjacent teeth. The drill diameter sequence, depth, and stopping position of each preparation in the surgical planning blueprint are presented in the report in the form of a list and 3D animation. The analysis of mechanical fit, surgical feasibility, and long-term prognostic predictability scores lists specific risk distance values, such as the shortest distance between the implant tip and the mandibular nerve canal. The diameter is 2.1 mm. The final visualized dental implant optimization design report is output as a file compatible with common medical data formats, which can be retrieved and displayed in clinical workstations or surgical navigation systems.

[0047] See Figure 5 This is a biaxial chart comparing the performance of four virtual implant path planning algorithms for dental implants. The bar chart represents the average path deviation, and the line chart represents the path accuracy, reflecting the core performance of different algorithms in surgical simulation. The average path deviation and path accuracy are significantly negatively correlated; the smaller the deviation, the higher the accuracy. This pattern aligns with clinical needs, as smaller implant path deviations reduce surgical risks and improve implant placement accuracy. In clinical practice, prioritizing 3D navigation or adaptive planning methods can significantly improve surgical accuracy and reduce risks such as nerve and blood vessel damage and damage to adjacent teeth. The path deviation value can predict the risk of implant deviation from the target position under different algorithms. For example, a deviation of 0.85mm in the straight-line path method may lead to implant invasion of the nerve and blood vessel area; dentists can avoid such approaches in advance.

[0048] The above embodiments are only used to illustrate the technical methods 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 methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for optimizing simulation design of a dental implant based on AI simulation technology, characterized in that, include: Acquire 3D oral cavity scan data and jawbone medical imaging data of the target patient, and perform data fusion and 3D reconstruction to generate a personalized oral digital model; The personalized oral digital model is input into a pre-trained biomechanical simulation AI agent to generate simulation results. The simulation results are the dynamic mechanical loads of each group of candidate implant models and implantation posture parameters in different personalized oral digital models. The simulation results include the stress distribution of the implant under different chewing conditions, bone interface microstrain, and long-term stability indicators. Based on the simulation results, a comprehensive score including mechanical fit, surgical feasibility, and long-term prognosis prediction is generated for each candidate scheme through a biomechanical simulation AI agent. From all candidate solutions, the solution with the highest comprehensive score is selected as the initial optimized design solution, and a surgical planning blueprint is generated based on the initial optimized design solution through virtual surgical simulation.

2. The implant optimization simulation design method based on AI simulation technology according to claim 1, characterized in that, Acquire 3D oral cavity scan data and jawbone medical imaging data of the target patient, perform data fusion and 3D reconstruction, and generate a personalized oral digital model, including: Acquire 3D oral cavity scan data and jawbone medical imaging data of the target patient; Based on the oral cavity 3D scanning data and the jawbone medical imaging data, data fusion and 3D reconstruction are performed to generate a personalized oral digital model that includes alveolar bone density distribution, key nerve and blood vessel locations, and the root morphology of adjacent teeth. The acquisition of the target patient's oral cavity three-dimensional scan data and jawbone medical imaging data includes: The intraoral scanner is used to scan the edentulous area, opposing dentition, and occlusal relationship of the target patient to obtain high-precision three-dimensional oral scan data. Medical imaging data of the jaw region of a target patient is acquired using a cone-beam computed tomography (CBCT) scanner, and the medical imaging data includes multi-layer cross-sectional images.

3. The implant optimization simulation design method based on AI simulation technology according to claim 2, characterized in that, Based on the oral cavity 3D scan data and the jawbone medical imaging data, data fusion and 3D reconstruction are performed to generate a personalized oral digital model that includes alveolar bone density distribution, key neurovascular locations, and adjacent tooth root morphology, including: The oral cavity 3D scan data is processed into triangular meshes to generate a 3D surface model of teeth and gingival soft tissue; Threshold segmentation and three-dimensional voxel reconstruction are performed on the aforementioned jawbone medical imaging data to generate a three-dimensional jawbone model containing the cortical and cancellous bone structures. Based on feature point matching and iterative nearest point algorithm, the surface three-dimensional model of the teeth and gingival soft tissue is spatially registered and fused with the three-dimensional model of the jawbone. Based on the grayscale information of the jawbone medical imaging data, a density distribution map of the alveolar bone region is generated on the fused model. The locations of key neurovascular structures, including the mandibular canal, the floor of the maxillary sinus, and the mental foramen, are automatically identified and labeled in the three-dimensional model of the jawbone. Extract the geometric morphology of adjacent teeth from the oral cavity 3D scan data, and accurately reconstruct the 3D morphology and position of the adjacent tooth roots within the jawbone.

4. The implant optimization simulation design method based on AI simulation technology according to claim 1, characterized in that, The personalized oral digital model is input into a pre-trained biomechanical simulation AI agent. The pre-training process of the biomechanical simulation AI agent includes: Construct a training dataset containing a large number of historical patients’ oral digital models, corresponding implant parameters, surgical records and long-term follow-up biomechanical data; Define simulation objectives, including minimizing peak stress around the implant, homogenizing stress distribution, and keeping the micro-strain at the bone interface within the range of physiological stimulation. Using the training dataset, an AI agent based on a deep reinforcement learning framework is trained, enabling the AI ​​agent to learn to actively search for and evaluate the impact of different implant models and implantation pose parameter combinations on achieving the simulation goal in a given oral digital model environment. Through repeated iterations and optimization of the reward mechanism, the biomechanical simulation AI agent is able to quickly and accurately simulate complex biomechanical responses.

5. The implant optimization simulation design method based on AI simulation technology according to claim 1, characterized in that, The biomechanical simulation AI agent automatically explores and performs simulation calculations within a preset solution space composed of various candidate implant models and implantation pose parameter combinations, including: The biomechanical simulation AI agent reads available implant diameter, length, thread design, and material parameters from the implant model library to form the model dimension; The biomechanical simulation AI agent defines the implantation pose parameters, which include implantation depth, implantation angle, buccal-lingual position, and mesio-distal position. The biomechanical simulation AI agent combines the model dimension with the implantation pose parameter dimension to form a multidimensional solution space; The biomechanical simulation AI agent adopts an adaptive sampling strategy, selecting representative parameter combinations for priority simulation calculations within the multidimensional solution space.

6. The implant optimization simulation design method based on AI simulation technology according to claim 5, characterized in that, The biomechanical simulation AI agent performs dynamic mechanical load simulation on the personalized oral digital model for each group of candidate implant models and implantation posture parameters. This simulation measures the stress distribution, bone interface micro-strain, and long-term stability indicators of the implant under different chewing conditions, including: Based on the implant model and implantation position parameters, a virtual three-dimensional implant entity is generated at the corresponding position in the personalized oral digital model; Multiple standard chewing conditions are set, including anterior tooth cutting, posterior tooth grinding, and lateral movement; A dynamic load force vector within the physiological range is applied to each of the aforementioned standard chewing conditions, the magnitude, direction, and point of application of which vary over time; Perform finite element analysis or smoothed particle fluid dynamics simulation to calculate the instantaneous stress and strain fields of the virtual implant three-dimensional entity and its surrounding bone tissue under the action of the dynamic load force vector; The stress peak values ​​and distribution cloud maps of the implant neck, body, and root tip were extracted from the simulation results. Calculate the microstrain values ​​of each region at the implant-osseointegration interface and statistically analyze the proportion of these values ​​falling within the physiological strain range. Based on a pre-defined biomechanical model of bone remodeling, the adaptive remodeling trend of bone tissue is predicted according to the long-term stress-strain stimulation history, and a long-term stability index is generated.

7. The implant optimization simulation design method based on AI simulation technology according to claim 1, characterized in that, The biomechanical simulation AI agent performs multi-objective evaluation on each candidate scheme based on the simulation results, generating a comprehensive score that includes mechanical fit, surgical feasibility, and long-term prognosis prediction, including: The biomechanical simulation AI agent calculates the mechanical risk coefficient based on whether the peak stress of the implant and surrounding bone tissue exceeds the material yield strength or the bone tissue tolerance threshold, and converts it into a mechanical fit score. The biomechanical simulation AI agent analyzes the minimum distance between the implantation pose parameters and the locations of key nerves and blood vessels, the morphology of adjacent tooth roots, and the anatomical boundaries of alveolar bone, assesses the safety margin and difficulty of the surgical operation, and generates a surgical feasibility score. The biomechanical simulation AI agent integrates the long-term stability indicators and individual patient characteristics to predict the success probability of long-term implant retention and generate a long-term prognosis prediction score. The biomechanical simulation AI agent assigns preset weight coefficients to the mechanical fit score, the surgical feasibility score, and the long-term prognosis prediction score, and performs a weighted summation to obtain a comprehensive score for each group of candidate solutions.

8. The implant optimization simulation design method based on AI simulation technology according to claim 7, characterized in that, From all candidate solutions, the solution with the highest comprehensive score is selected as the initial optimized design solution. Based on the initial optimized design solution, a virtual surgical simulation is performed to generate a surgical planning blueprint, including: From all candidate options, the implant model and implantation position parameter combination with the highest comprehensive score were selected as the initial optimization design scheme. Based on the initial optimized design scheme, a virtual surgical simulation is performed in the personalized oral digital model to generate a surgical planning blueprint that includes the implantation path, cavity preparation sequence, and suggestions for handling adjacent structures. By combining the surgical planning blueprint with the initial optimized design scheme, a final visual dental implant optimized design scheme report is generated; From all candidate options, the implant model and implantation position parameter combination with the highest overall score were selected as the initial optimization design scheme, including: The biomechanical simulation AI agent sorts all candidate solutions and their corresponding comprehensive scores. The top three candidate solutions based on overall scores were selected. Sensitivity analysis was performed on the top three candidate solutions based on the comprehensive score to verify their performance robustness when there are minor perturbations in the bone mineral density parameters and load conditions of the personalized oral digital model. From the candidate solutions that pass the sensitivity analysis, the solution with the highest comprehensive score is selected as the initial optimized design solution.

9. The implant optimization simulation design method based on AI simulation technology according to claim 8, characterized in that, Based on the initial optimized design scheme, a virtual surgical simulation is performed in the personalized oral digital model to generate a surgical planning blueprint that includes the implantation path, cavity preparation sequence, and suggestions for handling adjacent structures, including: In the personalized oral digital model, the virtual implantation path of the surgical instruments is planned in reverse, with the implantation pose defined by the initial optimized design scheme as the final target position. Simulate the step-by-step preparation process to determine the required drill bit diameter sequence, preparation depth, and cooling scheme; Check whether the virtual implantation path and preparation process interfere with the location of key nerves and blood vessels and the morphology of adjacent tooth roots in the three-dimensional model of the jawbone, and generate adjustment suggestions; Based on the morphology of gingival soft tissue, suggestions are made on incision design, flap treatment, or treatment of adjacent structures of immediate restorations; The virtual implantation path, the prepared hole sequence, adjustment suggestions, and adjacent structure processing suggestions are integrated to form the surgical planning blueprint.

10. The implant optimization simulation design method based on AI simulation technology according to claim 8, characterized in that, Combining the surgical planning blueprint with the initial optimized design scheme, a final visual dental implant optimized design scheme report is generated, including: The implant 3D model in the initial optimized design scheme is integrated into the personalized oral digital model according to the determined model and position to generate a 3D visualization view of the implantation effect. The key steps and parameters in the surgical planning blueprint are marked with two-dimensional diagrams and text annotations; The scores and analytical basis for the mechanical fit, surgical feasibility, and long-term prognosis prediction provided by the biomechanical simulation AI agent are compiled. All visualizations, diagrams, annotations, and analytical data are compiled into a structured electronic document to form the visualization dental implant optimization design report.

Images