Segmented embedded structure digital design and manufacturing method for edentulous jaw implant bridge

Abstract

The application relates to the fields of oral medicine and digital manufacturing technology, and discloses a segmented embedded structure digital design and manufacturing method for a toothless jaw implant bridge, which comprises the following steps: collecting and preprocessing multi-modal data of a patient; reconstructing a three-dimensional model and marking key anatomical markers; virtually arranging teeth for the patient and optimizing implant positioning; designing a bridge segmentation and embedded connection structure; performing biomechanical simulation and optimizing structure parameters to obtain an optimized bridge segmentation model; performing digital manufacturing and post-processing; converting the optimized bridge segmentation model into a physical bridge segmentation and performing post-processing; performing clinical trial and completing final prosthesis production; through steps 2, 4 and 5, the balance between biomechanics and clinical experience is realized through stress and comfort double-index control; through bridge segmentation, single-time positioning resistance is reduced, and positioning success rate and positioning accuracy are improved.

Description

Technical Field

[0001] This invention relates to the fields of oral medicine and digital manufacturing technology, and more specifically to a digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge. Background Technology

[0002] For edentulous patients, traditional monolithic implant bridges present several challenges due to factors such as alveolar bone resorption and changes in mucosal elasticity. These challenges include difficulty in placement, stress concentration, sensitivity to manufacturing errors, and inconvenience in maintenance. Monolithic bridges are bulky and limited by oral space, making them prone to interference with the mucosa or adjacent teeth, leading to placement deviations. The excessive rigidity of monolithic bridge structures prevents effective distribution of occlusal forces, which can easily lead to peri-implant bone resorption or bridge fracture over time. Traditional techniques rely on plaster model fabrication, resulting in low precision and an inability to adjust the structure to individual differences. In cases of localized damage, monolithic implants require replacement of the entire bridge, which is costly and invasive.

[0003] Existing technologies employ segmented cable tray designs to compensate for the shortcomings of traditional integral cable trays; however, most existing segmented cable tray designs use bolted connections or adhesive fixation, resulting in problems such as insufficient strength at the connection interface and loose edge density. When using digital processes, they often remain at a single stage such as scanning or design, lacking collaborative optimization across the entire chain from data acquisition to manufacturing.

[0004] In view of this, the present invention proposes a digital design and manufacturing method for a segmented embedded structure of edentulous implant bridges, which integrates segmented embedded structure with full-process digital design. Summary of the Invention

[0005] To overcome the aforementioned deficiencies in the prior art, this invention provides a digital design and manufacturing method for segmented embedded structures of edentulous implant bridges. By using digital technology, the method achieves precise segmentation, embedded connection, and personalized manufacturing of the bridge, thereby improving the biomechanical adaptability and clinical operability of the prosthesis and solving the problems existing in the background art.

[0006] This invention provides the following technical solution: a digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge, comprising the following steps: Step 1: Collect and preprocess multimodal data from patients; Step 2: Reconstruct the 3D model based on the data collected in Step 1 and mark key anatomical landmarks; Step 3: Perform virtual tooth alignment for the patient and optimize implant positioning; Step 4: Based on the optimized implant positioning in Step 3, design the segmented embedded connection structure of the bridge; Step 5: Based on the design in Step 4, perform biomechanical simulation and optimize the structural parameters to obtain the optimized cable tray segment model; Step 6: Perform digital manufacturing and post-processing; convert the optimized cable tray segment model into solid cable tray segments and perform post-processing. Step 7: Conduct clinical fitting and complete the final prosthesis fabrication.

[0007] Preferably, step 1 specifically comprises: Target data of the edentulous region of the patient was acquired and registered. The target data is anatomical data, including intraoral scan data and cone-beam CT data. The intraoral scan data includes mucosal surface morphology, height and width of the remaining alveolar ridge, and occlusal relationship. The cone-beam CT data is three-dimensional structural data of the jawbone, including bone density and nerve canal location in the implant placement area. Intraoral scan data and cone-beam CT data were acquired using an intraoral 3D scanner and cone-beam CT.

[0008] Preferably, the data registration involves spatially registering intraoral scan data with cone-beam CT data to generate a fused three-dimensional dataset including the mucosa, bone tissue, and occlusal relationship; specifically: The intraoral scan data and cone-beam CT data were converted and preprocessed. Point cloud denoising was used on the intraoral scan data, and the scanning blind area was filled with holes to generate a continuous and closed crown-gingival surface model. The jawbone contour and alveolar ridge crest line in the cone-beam CT data were extracted by gray-scale threshold segmentation to generate a three-dimensional model of bone tissue. Rigid registration is performed; coarse alignment is achieved through anatomical feature point matching, including feature point extraction and point cloud matching. Non-rigid registration is performed; a deep learning-based deformation network or radial function is used to elastically deform the crown-gingival surface model to fit it with the three-dimensional bone tissue model, and constraints are set; the constraints include anatomical constraints and occlusal constraints. The distance analysis tool was used to examine the gap between the mucosal surface and the bone tissue surface. The dynamic data of the patient's maximum cusp intercuspal position, protruding condylar guide angle and lateral condylar guide angle were recorded using an electronic facebow and exported as an occlusal record. The mandibular movement trajectory of the electronic facebow was imported into the software and aligned with the fused mucosa-bone tissue to mark the occlusal contact points. Data from the mucosa, bone tissue, and occlusal relationship are integrated to form a fused 3D dataset for output.

[0009] Preferably, step 2 specifically comprises: Reconstruct the mucosal model; based on intraoral scan data, generate a high-precision mucosal surface model through threshold segmentation and surface fitting; Reconstruct a bone tissue model; based on cone-beam CT data, generate a jawbone contour model through threshold segmentation and 3D reconstruction; The key anatomical landmarks include the alveolar ridge crest line, the highest point of the palatal vault, the depth of the buccal vestibule, and the implant safety placement area; The threshold segmentation is used to distinguish between the subject and external organizations, and the point cloud data of the surface is extracted by utilizing the grayscale difference between the subject and external organizations.

[0010] Preferably, the implant positioning determines the number, location, and spacing of implants based on bone density, repair space, and aesthetic requirements; specifically: Bone mineral density was assessed, and the bone mineral density was classified into type 2 bone and type 3 bone. The restoration space is analyzed by evaluating the vertical and horizontal distances. The vertical distance is the distance from the occlusal plane to the alveolar ridge crest, and the horizontal distance is the implant spacing and arch span. The evaluation results of the vertical distance include Class I, Class II, Class III, and Class IV. Digital planting guides are used to determine the number, location, and spacing of implants.

[0011] Preferably, the specific process of designing the segmented embedded connection structure of the cable tray is as follows: The structural parameters of the connection structure are determined; the structural parameters include geometric parameters, material parameters, and functional parameters. The geometric parameters include the cross-sectional shape and size of the boss and groove, as well as the diameter and depth of the positioning pin hole. The material parameters include the elastic modulus and Poisson's ratio of the main material of the cable tray. The functional parameters include the gap distance of the connection interface and the positioning accuracy of the anti-rotation structure. The key variables are obtained by associating with the patient's anatomical data. Establish the association between parameters and structure, and bind the geometric parameters with functional parameters and structural features to perform geometric constraints; The dimensions of the connection structure are correlated with implant parameters, including implant diameter and spacing, through parametric equations. The parametric equations include the relationship between the boss height and the implant length, and the relationship between the groove width and the bottom width of the boss. Verify the fit accuracy of the connection structure.

[0012] Preferably, the biomechanical simulation specifically includes: The segmented bridge, implant, bone tissue model and mucosa model were imported into the engineering simulation software, the software properties were set, and the mesh was generated using tetrahedral elements to establish a finite element model containing the segmented bridge, implant, bone tissue and mucosa. A load is applied to simulate the maximum occlusal force, the direction of which is perpendicular to the occlusal surface of the tooth crown; Calculate equivalent stress and mucosal pressure to assess the risk of plastic deformation and comfort. The formula for calculating the mucosal pressure is as follows: ;in, Indicates pressure. Indicates bite force. Indicates the area of ​​the mucosa subjected to force; Preferably, the optimization of structural parameters specifically involves setting an objective function and optimizing the structural parameters by achieving the objective function value; the objective function is the minimum equivalent stress and the minimum mucosal pressure.

[0013] The technical effects and advantages of this invention are as follows: This invention, through steps 2, 4, and 5, facilitates the design of segmented and embedded connection structures for the bridging system after optimizing implant positioning by reconstructing a 3D model and marking key anatomical landmarks. It also enables biomechanical simulation and optimization of structural parameters. For the first time, mucosal pressure is incorporated into the segmented bridging optimization process. By controlling stress and comfort as dual indicators, a balance between biomechanics and clinical experience is achieved. Segmented bridging reduces single-attempt resistance, improves placement success rate, and enhances placement accuracy. Segmented bridging effectively reduces maximum stress, minimizes the risk of peri-implant bone resorption, and optimizes stress dispersion. Furthermore, multimodal data acquisition and end-to-end digital design effectively improve the matching degree between the bridging system and the patient's anatomical morphology, providing personalized adaptation solutions. Simultaneously, when a single segment is damaged, only the corresponding segment needs to be replaced, making maintenance convenient and effectively reducing maintenance costs. Attached Figure Description

[0014] Figure 1 This is a flowchart of the digital design and manufacturing method for the segmented embedded structure of the edentulous jaw implant bridge according to the present invention. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The digital design and manufacturing method of segmented embedded structure of edentulous implant bridge involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, this invention provides a digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge, comprising the following steps: Step 1: Collect and preprocess multimodal data from patients. The purpose is to overcome the limitations of traditional single plaster model by fusing and collecting multimodal data, providing a basis for personalized segmentation design and subsequent modeling. Step 2: Reconstruct a three-dimensional model based on the data collected in Step 1 and mark key anatomical landmarks. The purpose is to clarify key anatomical boundaries by constructing a digital model that can be used for design, and to provide constraints for mucosal load assessment and implant positioning during subsequent virtual tooth arrangement by reconstructing a dual model of mucosa and bone tissue. Step 3: Perform virtual tooth alignment for the patient and optimize implant positioning; determine the number and location of implants and the area covered by the bridge to provide a basis for segmented design; Step 4: Based on the optimized implant positioning in Step 3, design the segmented and embedded connection structure of the cable tray; decompose the overall cable tray into several independent segments, and design the embedded connection interface to balance strength and placement convenience. Step 5: Based on the design in Step 4, perform biomechanical simulation and optimize the structural parameters to obtain the optimized cable tray segment model; verify the rationality of the stress distribution of the segmented cable tray through biomechanical simulation, and then optimize the structural parameters. Step 6: Perform digital manufacturing and post-processing; convert the optimized cable tray segment model into solid cable tray segments and perform post-processing. Step 7: Conduct clinical fitting and complete the final prosthesis fabrication.

[0017] In this embodiment, it should be specifically explained that step 1 is as follows: Target data of the edentulous region of the patient is acquired and registered. The target data is anatomical data, including intraoral scan data and cone-beam CT data. The intraoral scan data includes, but is not limited to, mucosal surface morphology, height and width of the remaining alveolar ridge, and occlusal relationship. The cone-beam CT data is three-dimensional structural data of the jawbone, including but not limited to bone density and nerve canal location in the implant placement area. Intraoral scan data and cone-beam CT data can be acquired using an intraoral 3D scanner and cone-beam CT. The data registration process specifically involves: Intraoral scan data and cone-beam CT data are spatially registered to generate a fused 3D dataset containing mucosa, bone tissue, and occlusal relationships. The spatial registration is used to align the soft tissue surface of the intraoral scan with the bone tissue interior of the CBCT to resolve coordinate system differences and geometric deformations caused by the acquisition methods. CBCT is cone-beam CT. The intraoral scan data and cone-beam CT data were converted and preprocessed. Point cloud denoising was used on the intraoral scan data, and the scanning blind area was filled with holes to generate a continuous and closed crown-gingival surface model. The jawbone contour and alveolar ridge crest line in the cone-beam CT data were extracted by gray-scale threshold segmentation to generate a three-dimensional model of bone tissue. Rigid registration is performed; coarse alignment is achieved through anatomical feature point matching, laying the foundation for subsequent fine registration; this includes feature point extraction and point cloud matching; the feature point extraction specifically involves: marking anatomical landmarks on the crown-gingival surface model, such as the midpoint of the incisal edge of the anterior teeth, the mesiobuccal cusp of the bilateral first molars, and the highest point of the palatal vault, etc. The midpoint of the incisal edge of the anterior teeth can be marked as point A, the mesiobuccal cusp of the bilateral first molars can be marked as point B, and the highest point of the palatal vault can be marked as point C; corresponding feature points are marked on the three-dimensional model of bone tissue, such as point A corresponding to the alveolar ridge crest incisal edge, points B and C corresponding to the alveolar bone crest of the first molars, and point D corresponding to the palatal protuberance; the point cloud matching uses an iterative nearest point algorithm or a feature point registration algorithm to calculate the rigid body transformation matrix to achieve initial alignment; Non-rigid registration is performed; a deep learning-based deformation network or radial function is used to elastically deform the crown-gingival surface model to make it fit the three-dimensional bone tissue model, and constraints are set; the constraints include anatomical constraints and occlusal constraints. The anatomical constraints force bone tissue landmarks such as the alveolar ridge crest line and palatal dome contour to be consistent with the crown-gingival surface model. The occlusal constraints ensure that the occlusal relationship of the intercuspal position matches the temporomandibular joint position in CBCT by importing occlusal records. Distance analysis tools were used to examine the gap between the mucosal surface and the bone tissue surface to ensure the accuracy of the anatomical relationship between the mucosa and bone tissue; dynamic data of the patient's maximum cusp intersection, protruding condyle inclination, and lateral condyle inclination were recorded using an electronic facebow and exported as an occlusal record; the mandibular movement trajectory of the electronic facebow was imported into the software and aligned with the fused mucosa and bone tissue to mark the occlusal contact points; Data from the mucosa, bone tissue, and occlusal relationship are integrated to form a fused 3D dataset for output.

[0018] In this embodiment, it should be specifically explained that step 2 is as follows: Reconstruct the mucosal model; based on intraoral scan data, generate a high-precision mucosal surface model through threshold segmentation and surface fitting; Reconstruct a bone tissue model; based on cone-beam CT data, generate a jawbone contour model through threshold segmentation and 3D reconstruction; The key anatomical landmarks include, but are not limited to, the alveolar ridge crest line, the highest point of the palatal vault, the depth of the buccal vestibule, and the implant safety placement area; The threshold segmentation is used to distinguish between the subject and external organizations, and the point cloud data of the surface is extracted by utilizing the grayscale difference between the subject and external organizations; specifically: A threshold range is set through grayscale histogram analysis to extract point clouds. Morphological erosion is then performed on the extracted point clouds to remove small noise points, followed by dilation to fill gaps and improve connectivity. The specific value of the threshold range can be adjusted by those skilled in the art based on equipment calibration. Visual inspection can confirm the integrity of the point cloud, and the threshold can be adjusted as necessary. In this embodiment, a threshold range of 150-250 is selected. The main body can be either mucosa or bone tissue. When used to reconstruct a mucosal model, the main body is mucosa; when used to reconstruct a bone tissue model, the main body is bone tissue. The surface fitting process converts discrete mucosal point clouds into continuous NURBS surfaces to ensure the smoothness and anatomical accuracy of the surfaces, thus constructing high-precision mucosal surfaces; a high-precision mucosal surface model is generated based on digital processing software. The 3D reconstruction converts discrete jawbone point clouds into a continuous NURBS surface model; a bone tissue model is generated based on digital processing software.

[0019] In this embodiment, it should be specifically noted that in step 3, the virtual tooth arrangement is based on the patient's original occlusal record or ideal occlusal relationship, and artificial teeth are arranged on the mucosal model to ensure occlusal balance; the ideal occlusal relationship can refer to the standard occlusal plane in professional books or materials. The implant placement is determined based on bone density, repair space, and aesthetic requirements, specifying the number, location, and spacing of the implants; specifically: Bone mineral density (BMD) is assessed to determine the biomechanical boundaries of the implant. BMD is measured using instruments. BMD is categorized into type 1 and type 3.5. Type 1 bone is dense or moderately dense with strong osseointegration, allowing for the selection of implants with standard diameter and length, with a spacing of at least 3 mm to avoid stress concentration. Type 3 bone is porous with weak osseointegration, requiring implants with a wide diameter and short length to increase the contact area with the bone, with a spacing of at least 4 mm to distribute stress. The standard diameter is 4 mm to 5 mm, the standard length is 8 mm to 12 mm, the wide diameter is 5 mm to 6 mm, and the short length is 6 mm to 8 mm. By evaluating vertical and horizontal distances, a restorative space analysis is performed to determine the functional boundaries of the implants. The vertical distance is the distance from the occlusal plane to the alveolar ridge crest, and the horizontal distance is the distance between implants and the arch span. The evaluation results of the vertical distance are classified into Class I, Class II, Class III, and Class IV. A vertical distance greater than or equal to 15 mm is classified as Class I, 12-14 mm as Class II, 9-11 mm as Class III, and less than 9 mm as Class IV. A Class I evaluation indicates sufficient restorative space, and implants can be selected. To reduce the number of implants, a Class II assessment indicates adequate restoration space, allowing for the selection of either a fixed implant or a cover denture. Attention should be paid to the thickness of the prosthesis to prevent breakage. A Class III assessment indicates limited restoration space, prioritizing a fixed implant with short or angled implants. A Class IV assessment indicates insufficient restoration space, requiring an increase in vertical distance. The adjacent distance between implants should be greater than or equal to 3mm to avoid stress concentration, and the arch span should be greater than or equal to 40mm or 50mm to distribute occlusal forces. A digital planting guide is used to determine the number, location, and spacing of the implants; The aesthetic requirements are of concern to those skilled in the art regarding the placement of implants in the anterior region to avoid gingival recession and metal exposure.

[0020] In this embodiment, it should be specifically noted that the principle for segmenting the cable tray is as follows: Corresponding to the optimized implant positions in step 3, ensure that each bridge segment connects at least 2 implants to avoid the cantilever beam effect; the embedded connection structure is parametrically modeled using 3D design software to facilitate subsequent mechanical optimization; The bridge is segmented according to functional zones or anatomical barrier zones. The functional zones are based on the physiological functions of the oral cavity, and the anatomical barrier zones are based on the barrier functions of tissue anatomical structures. The physiological functions include, but are not limited to, chewing, swallowing, and speech. The barrier functions include, but are not limited to, separating areas, resisting stimuli, and protecting deep tissues. In this embodiment, the bridge is segmented according to functional zones, dividing it into anterior teeth segments, posterior teeth segments, and connecting segments. The embedded connection structure mainly adopts a convex-concave matching structure and an anti-rotation structure. The convex-concave matching structure consists of a dovetail groove boss at one end of the cable tray and a corresponding dovetail groove at the other end. The anti-rotation structure involves adding a positioning pin hole on the side of the boss and a cylindrical pin at the corresponding position in the groove to prevent rotational misalignment. In terms of material selection, titanium alloy can be used as the main body of the cable tray, and the connection interface is polished to reduce stress concentration. Compared with traditional bolt connections, the composite embedded connection of dovetail groove and positioning pin can eliminate exposed screws, thereby preventing aesthetic impact and effectively improving connection strength. The specific process for designing the embedded connection structure is as follows: The structural parameters of the connection structure are determined; the structural parameters include, but are not limited to, geometric parameters, material parameters, and functional parameters. The geometric parameters include, but are not limited to, the cross-sectional shape and size of the boss and groove, and the diameter and depth of the positioning pin hole. The material parameters include the elastic modulus and Poisson's ratio of the main material of the cable tray. The functional parameters include, but are not limited to, the gap distance of the connection interface and the positioning accuracy of the anti-rotation structure. The key variables are obtained by associating with the patient's anatomical data. Establish the association between parameters and structure, bind the geometric parameters with functional parameters and structural features, and impose geometric constraints; the boss is based on the implant axis, and the position constraint range and shape constraint range of the boss are defined; the groove corresponds to the boss, and the size constraint range and position constraint range of the groove are defined; the anti-rotation structure adds positioning pin holes on the side of the boss, and designs cylindrical pins at the corresponding positions of the grooves. The dimensions of the connection structure are correlated with implant parameters through parametric equations, including but not limited to implant diameter and spacing; the parametric equations include but are not limited to the relationship between the boss height and the implant length, and the relationship between the groove width and the bottom width of the boss. Verify the fit accuracy of the connection structure; check the uniformity of the gap between the boss and the groove, and verify the fit accuracy between the locating pin and the hole to avoid rotational misalignment.

[0021] In this embodiment, it should be specifically explained that the biomechanical simulation specifically refers to: The segmented bridge, implant, bone tissue model and mucosa model were imported into the engineering simulation software, the software properties were set, and the mesh was generated using tetrahedral elements to establish a finite element model containing the segmented bridge, implant, bone tissue and mucosa. A load is applied to simulate the maximum occlusal force, the direction of which is perpendicular to the occlusal surface of the tooth crown; Calculate equivalent stress and mucosal pressure to assess the risk of plastic deformation and comfort. The formula for calculating the mucosal pressure is as follows: ;in, Indicates pressure. Indicates bite force. Indicates the area of ​​the mucosa subjected to force; if If the pressure exceeds the mucosal tolerance threshold, the patient will feel discomfort. In this case, it is necessary to adjust the coverage area or connection structure of the bridge to reduce the pressure. In this embodiment, the mucosal tolerance threshold is 30. The optimized structural parameters are specifically: Set an objective function, and optimize the structural parameters by achieving the objective function value; the objective function is the minimum equivalent stress and the minimum mucosal pressure. This embodiment is the first to incorporate mucosal pressure into the segmented cable tray optimization process, achieving a balance between biomechanics and clinical experience through dual index control of stress and comfort.

[0022] In this embodiment, it should be specifically noted that the post-processing includes: Remove the supporting structure and sand the connection interface. Electropolishing is used to reduce the surface friction coefficient. Ultrasonic cleaning to remove residual powder; The digital manufacturing can employ 3D printing technology.

[0023] In this embodiment, it should be specifically explained that the clinical trial fitting is as follows: the trial fitting is performed in segmented order to check the fit of the connection interface and the occlusal balance, and the early contact points are marked with occlusal paper; if there are micro gaps, they are adjusted by resin filling; by verifying the placement and occlusal relationship of the segmented bridge, the final restoration is fabricated.

[0024] In this embodiment, it should be specifically noted that the software attribute settings can be configured by those skilled in the art based on the actual software attributes. An example is provided in Table 1 of this embodiment: Table 1 In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0025] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge, characterized in that: Includes the following steps: Step 1: Collect and preprocess multimodal data from patients; Step 2: Reconstruct the 3D model based on the data collected in Step 1 and mark key anatomical landmarks; Step 3: Perform virtual tooth alignment for the patient and optimize implant positioning; Step 4: Based on the optimized implant positioning in Step 3, design the segmented embedded connection structure of the bridge; Step 5: Based on the design in Step 4, perform biomechanical simulation and optimize the structural parameters to obtain the optimized cable tray segment model; Step 6: Perform digital manufacturing and post-processing; The optimized cable tray segment model is converted into solid cable tray segments and then post-processed. Step 7: Conduct clinical fitting and complete the final prosthesis fabrication.

2. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 1, characterized in that: Step 1 specifically involves: Target data of the edentulous region of the patient was acquired and registered. The target data is anatomical data, including intraoral scan data and cone-beam CT data. The intraoral scan data includes mucosal surface morphology, height and width of the remaining alveolar ridge, and occlusal relationship. The cone-beam CT data is three-dimensional structural data of the jawbone, including bone density and nerve canal location in the implant placement area. Intraoral scan data and cone-beam CT data were acquired using an intraoral 3D scanner and cone-beam CT.

3. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 2, characterized in that: The data registration process involves spatially registering intraoral scan data with cone-beam CT data to generate a fused three-dimensional dataset that includes mucosa, bone tissue, and occlusal relationships. Specifically: The intraoral scan data and cone-beam CT data were converted and preprocessed. Point cloud denoising was used on the intraoral scan data, and the scanning blind area was filled with holes to generate a continuous and closed crown-gingival surface model. The jawbone contour and alveolar ridge crest line in the cone-beam CT data were extracted by gray-scale threshold segmentation to generate a three-dimensional model of bone tissue. Rigid registration is performed; coarse alignment is achieved through anatomical feature point matching, including feature point extraction and point cloud matching. Non-rigid registration is performed; a deep learning-based deformation network or radial function is used to elastically deform the crown-gingival surface model to fit it with the three-dimensional bone tissue model, and constraints are set; the constraints include anatomical constraints and occlusal constraints. The distance analysis tool was used to examine the gap between the mucosal surface and the bone tissue surface. The dynamic data of the patient's maximum cusp intercuspal position, protruding condylar guide angle and lateral condylar guide angle were recorded using an electronic facebow and exported as an occlusal record. The mandibular movement trajectory of the electronic facebow was imported into the software and aligned with the fused mucosa-bone tissue to mark the occlusal contact points. Data from the mucosa, bone tissue, and occlusal relationship are integrated to form a fused 3D dataset for output.

4. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 3, characterized in that: Step 2 specifically involves: Reconstruct the mucosal model; based on intraoral scan data, generate a high-precision mucosal surface model through threshold segmentation and surface fitting; Reconstruct a bone tissue model; based on cone-beam CT data, generate a jawbone contour model through threshold segmentation and 3D reconstruction; The key anatomical landmarks include the alveolar ridge crest line, the highest point of the palatal vault, the depth of the buccal vestibule, and the implant safety placement area; The threshold segmentation is used to distinguish between the subject and external organizations, and the point cloud data of the surface is extracted by utilizing the grayscale difference between the subject and external organizations.

5. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 4, characterized in that: The implant placement is determined based on bone density, repair space, and aesthetic requirements, specifying the number, location, and spacing of the implants; specifically: Bone mineral density was assessed, and the bone mineral density was classified into type 2 bone and type 3 bone. The restoration space is analyzed by evaluating the vertical and horizontal distances. The vertical distance is the distance from the occlusal plane to the alveolar ridge crest, and the horizontal distance is the implant spacing and arch span. The evaluation results of the vertical distance include Class I, Class II, Class III, and Class IV. Digital planting guides are used to determine the number, location, and spacing of implants.

6. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 5, characterized in that: The specific process of designing the segmented embedded connection structure of the cable tray is as follows: The structural parameters of the connection structure are determined; the structural parameters include geometric parameters, material parameters, and functional parameters. The geometric parameters include the cross-sectional shape and size of the boss and groove, as well as the diameter and depth of the positioning pin hole. The material parameters include the elastic modulus and Poisson's ratio of the main material of the cable tray. The functional parameters include the gap distance of the connection interface and the positioning accuracy of the anti-rotation structure. The key variables are obtained by associating with the patient's anatomical data. Establish the association between parameters and structure, and bind the geometric parameters with functional parameters and structural features to perform geometric constraints; The dimensions of the connection structure are correlated with implant parameters, including implant diameter and spacing, through parametric equations. The parametric equations include the relationship between the boss height and the implant length, and the relationship between the groove width and the bottom width of the boss. Verify the fit accuracy of the connection structure.

7. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 6, characterized in that: The biomechanical simulation specifically refers to: The segmented bridge, implant, bone tissue model and mucosa model were imported into the engineering simulation software, the software properties were set, and the mesh was generated using tetrahedral elements to establish a finite element model containing the segmented bridge, implant, bone tissue and mucosa. A load is applied to simulate the maximum occlusal force, the direction of which is perpendicular to the occlusal surface of the tooth crown; Calculate equivalent stress and mucosal pressure to assess the risk of plastic deformation and comfort. The formula for calculating the mucosal pressure is as follows: ;in, Indicates pressure. Indicates bite force. This indicates the area of ​​the mucosa subjected to force.

8. The digital design and manufacturing method for a segmented embedded structure of an edentulous implant bridge according to claim 1, characterized in that: The optimization of structural parameters specifically involves setting an objective function and optimizing the structural parameters by achieving the objective function value; the objective function is the minimum equivalent stress and the minimum mucosal pressure.

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