A method, system, product and medium for implant data processing of an edentulous jaw guide plate

Abstract

A method, system, product and medium for implant data processing of an edentulous jaw guide plate, which relates to the technical field of dental implants. The method includes: obtaining the facial static feature data, facial motion video data and jaw tomographic scan data of a target patient, performing spatial registration on the facial static three-dimensional model and the jaw three-dimensional model to obtain the position correspondence relationship between the bony landmark points and the jaw positions; extracting the landmark point motion trajectory data in the facial motion video data to obtain the dynamic displacement data of each landmark point, and according to the dynamic displacement data, calculating the relative distance change amount between each landmark point, and selecting a preset number of landmark points with the smallest relative distance change amount as the guide plate positioning reference points; generating a digital design plan for the guide plate according to the three-dimensional coordinate information of the guide plate positioning reference points and the jaw three-dimensional model. Implementing this method can improve the stability of the edentulous jaw guide plate positioning.

Description

Technical Field

[0001] This application relates to the technical field of dental implants, and particularly to a method, system, product and medium for processing implant data of an edentulous jaw guide plate. Background Art

[0002] With the development of oral medical technology, implant restoration for edentulous jaws has become a common treatment option in dental clinics. Edentulous patients usually face problems such as masticatory dysfunction and facial morphological changes. Therefore, accurate implant restoration is of great significance for improving the quality of life of patients. In implant surgery, the precise design and positioning of the implant guide plate directly affect the accuracy of implant placement and the surgical outcome.

[0003] In the related art, the guide plate can be designed by collecting the jaw CT data of the patient and combining soft tissue marker points. The doctor attaches marker points to the patient's face, collects the facial data and jaw data of the patient in the closed state, and then determines the positioning reference of the guide plate according to the position information of these fixed marker points, and then completes the design of the guide plate.

[0004] However, patients make various facial expressions such as speaking and smiling in real life, and these expressions will cause deformation of facial soft tissues. Since the existing technology only collects data of the patient in the static state and cannot reflect the movement law of soft tissues during dynamic deformation, the guide plate positioning reference points determined based on static data are easily affected by facial expression changes, reducing the stability of guide plate positioning. Summary of the Invention

[0005] This application provides a method, system, product and medium for processing implant data of an edentulous jaw guide plate, which is used to improve the stability of edentulous jaw guide plate positioning.

[0006] In a first aspect, the present application provides a method for processing implant data of an edentulous jaw guide plate, which is applied to an implant data processing system. The method includes: obtaining facial static feature data, facial motion video data, and jaw tomographic scan data of a target patient, where the facial feature data includes three-dimensional spatial position information of bony landmark points; extracting features from the facial static feature data to obtain a facial static three-dimensional model; extracting features from the jaw tomographic scan data to obtain a jaw three-dimensional model; performing spatial registration on the facial static three-dimensional model and the jaw three-dimensional model to obtain the position correspondence between the bony landmark points and the jaw positions; extracting landmark point motion trajectory data from the facial motion video data to obtain dynamic displacement data of each landmark point, where the dynamic displacement data includes three-dimensional spatial coordinate change information of the landmark points in different facial expression states; calculating the relative distance change amount between each of the landmark points according to the dynamic displacement data, and selecting a preset number of landmark points with the smallest relative distance change amount as the guide plate positioning reference points; generating a digital design plan for the guide plate according to the three-dimensional coordinate information of the guide plate positioning reference points and the jaw three-dimensional model.

[0007] In the above embodiment, facial static feature data including three-dimensional spatial position information of bony landmark points, facial motion video data, and jaw tomographic scan data of a target patient are obtained. After feature extraction, a facial static three-dimensional model and a jaw three-dimensional model are obtained. After spatial registration, the guide plate positioning reference points are determined based on the facial motion video data, and then a digital design plan for the guide plate is generated in combination with the jaw three-dimensional model. The comprehensive application and precise processing of multi-source data enable the guide plate design to fully consider the static and dynamic features of the face, improve the accuracy and stability of guide plate positioning, ensure that the implant position is more precise, and effectively improve the implant restoration effect of the edentulous jaw.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the step of performing spatial registration on the facial static three-dimensional model and the jaw three-dimensional model to obtain the position correspondence between the bony landmark points and the jaw positions specifically includes: using the bony landmark points in the facial static three-dimensional model as reference points to establish a coordinate system for the facial static three-dimensional model; extracting jaw feature points based on the anatomical features of the jaw three-dimensional model and establishing a jaw coordinate system; performing registration transformation on the coordinate system of the facial static three-dimensional model and the jaw coordinate system, and calculating the three-dimensional coordinates of the bony landmark points in the jaw coordinate system after registration to obtain the position correspondence between the bony landmark points and the jaw positions.

[0009] In the above embodiments, the bony landmark points in the static three-dimensional facial model are used as reference points to establish a coordinate system. Based on the anatomical features of the three-dimensional jaw model, jaw feature points are extracted to establish a jaw coordinate system, and then a registration transformation is performed to obtain the position correspondence between the bony landmark points and the jaw positions. By utilizing the reference function of the bony landmark points and the anatomical positioning of the jaw feature points, the coordinate system is accurately established and registered, ensuring the precise spatial correspondence between the facial and jaw models, providing an accurate spatial reference for the determination of the reference points for the subsequent guide plate positioning and the guide plate design, enabling the guide plate to better adapt to the individual characteristics of the patient, and improving the accuracy and success rate of the implant surgery.

[0010] Combined with some embodiments of the first aspect, in some embodiments, the steps of extracting the landmark point movement trajectory data from the facial movement video data to obtain the dynamic displacement data of each landmark point specifically include: decomposing the facial movement video data into frames to obtain a sequence of consecutive video frames; using a feature point tracking algorithm to track the movement trajectories of each bony landmark point in the video frame sequence; performing three-dimensional reconstruction on the movement trajectories to obtain the three-dimensional spatial coordinates of each bony landmark point at different times; and based on the three-dimensional spatial coordinates, calculating the displacement amount of each bony landmark point relative to the initial position to obtain the dynamic displacement data.

[0011] In the above embodiments, the facial movement video data is decomposed into frames to obtain a video frame sequence, a feature point tracking algorithm is used to track the movement trajectories of the bony landmark points, three-dimensional reconstruction is performed to obtain the three-dimensional spatial coordinates at different times, and then the dynamic displacement data is calculated. The processing such as frame decomposition and feature point tracking of the video data accurately captures the movement trajectories of the bony landmark points during facial expression changes. The three-dimensional reconstruction and displacement calculation provide accurate dynamic displacement information, which can deeply analyze the deformation law of facial soft tissues, providing a reliable basis for selecting the landmark points with the smallest relative distance change amount as the reference points for guide plate positioning, thereby enhancing the stability of guide plate positioning and ensuring the implant restoration effect.

[0012] Combined with some embodiments of the first aspect, in some embodiments, the steps of calculating the relative distance change amount between each landmark point according to the dynamic displacement data and selecting a preset number of landmark points with the smallest relative distance change amount as the reference points for guide plate positioning specifically include: based on the dynamic displacement data, calculating the Euclidean distance between any two bony landmark points in different facial expression states; obtaining the maximum change amount of the relative distance between each pair of bony landmark points during the entire movement process; sorting the relative distance change amounts from smallest to largest, and selecting the first N bony landmark points with the smallest change amount as the reference points for the guide plate positioning.

[0013] In the above embodiment, the Euclidean distance between the bony landmarks is calculated based on the dynamic displacement data, the relative distance change is obtained, and the preset number of landmarks with the smallest change are sorted and selected as the guide plate positioning reference points. Calculating the Euclidean distance can accurately quantify the change in the spatial relationship between the landmarks. By analyzing the relative distance change during the entire movement process, a combination of landmarks with relatively stable positions when the facial expression changes can be screened out. These stable landmarks serve as positioning reference points to provide a reliable positioning reference for the guide plate, so that the guide plate can maintain an accurate position under different facial conditions, effectively improving the stability of the guide plate positioning and the accuracy of the implant surgery, and ensuring a good implant restoration effect.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the steps of generating a digital design scheme for the guide plate based on the three-dimensional coordinate information of the guide plate positioning reference point and the three-dimensional model of the mandible specifically include: according to the position correspondence, projecting the guide plate positioning reference point onto the surface of the three-dimensional model of the mandible to obtain a reference projection point; calculating the offset area of ​​the reference projection point within the corresponding range of the dynamic displacement data, and constructing a positioning pillar at the boundary of the area; generating a support structure connecting each of the positioning pillars based on the spatial distribution of the three-dimensional model of the mandible and the positioning pillars; constructing a guide plate base based on the surface contour of the three-dimensional model of the mandible and the position of the positioning pillars; and constructing the digital design scheme for the guide plate based on the guide plate base, the positioning pillars and the support structure.

[0015] In the above embodiment, the guide plate positioning reference point is projected onto the surface of the three-dimensional model of the mandible according to the position correspondence to obtain the reference projection point, the offset area is calculated to construct the positioning pillar, the support structure is generated by combining the mandible model and the pillar distribution, the guide plate matrix is ​​constructed based on the mandible contour and the pillar position, and then the guide plate digital design scheme is constructed. The reference projection point determines the support point, and the pillars and support structure constructed considering the dynamic displacement can adapt to facial movement, and the matrix that fits the mandible ensures overall stability. The various components cooperate with each other, and the guide plate structure designed based on precise data is reasonable, stable and reliable, which can accurately guide the implantation of the implant, improve the success rate of implant restoration, and provide better treatment solutions for edentulous patients.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating a digital design plan of the guide plate based on the three-dimensional coordinate information of the guide plate positioning reference point and the three-dimensional model of the jaw, the method also includes: establishing a patient facial feature database based on the static three-dimensional facial model; and storing the distribution characteristics of the guide plate positioning reference point in the feature database.

[0017] In the above embodiments, after generating the digital design solution of the guide plate, a patient facial feature database is established based on the static three-dimensional facial model, and the distribution characteristics of the guide plate positioning reference points are stored. The static three-dimensional facial model provides basic data for the database, and storing the distribution characteristics of the positioning reference points helps with subsequent analysis. This database can be used for data comparison and effect evaluation during the patient's treatment process, providing comprehensive information for doctors, facilitating doctors' further understanding of the patient's individual characteristics, helping to optimize the treatment plan, improve the personalization of treatment, and at the same time accumulating data for medical research, promoting the development and improvement of edentulous jaw implant technology.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of generating the digital design solution of the guide plate according to the three-dimensional coordinate information of the guide plate positioning reference points and the three-dimensional model of the jawbone, the method further includes: when a solution acquisition instruction is received, sending the digital design solution of the guide plate to a target client.

[0019] In the above embodiments, after generating the digital design solution of the guide plate, when a solution acquisition instruction is received, it is sent to the target client. The digital design solution of the guide plate is an accurate result obtained based on multi-source data processing and contains detailed information about each component of the guide plate. The target client can be a doctor's terminal or other relevant device terminals. Sending the solution in a timely manner can ensure that relevant personnel obtain accurate guide plate design data in the first place, enabling the preparation work for the implant surgery to be carried out quickly and accurately. This ensures the efficient connection of the treatment process, avoids time waste caused by delays in data transmission, improves the efficiency of the entire edentulous jaw implant restoration process, and ensures that patients can receive appropriate treatment faster.

[0020] In a second aspect, an embodiment of the present application provides a planting data processing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the planting data processing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, which, when the computer program product runs on the planting data processing system, enables the planting data processing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, which, when the instructions run on the planting data processing system, enable the planting data processing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] Understandably, the implant data processing system provided in the second aspect above, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. In the present application, by acquiring facial static feature data including the three-dimensional spatial position information of bony landmark points, facial motion video data, and jaw tomographic scan data of the target patient, a facial static three-dimensional model and a jaw three-dimensional model are obtained through feature extraction. After spatial registration, the guide plate positioning reference point is determined based on the facial motion video data, and then a guide plate digital design scheme is generated in combination with the jaw three-dimensional model. The comprehensive utilization and precise processing of multi-source data enable the guide plate design to fully consider facial static and dynamic characteristics, improve the accuracy and stability of guide plate positioning, ensure that the implant position is more precise, and effectively improve the edentulous jaw implant restoration effect.

[0026] 2. In the present application, a coordinate system is established by using the bony landmark points in the facial static three-dimensional model as reference points, and jaw feature points are extracted based on the anatomical features of the jaw three-dimensional model to establish a jaw coordinate system, and then registration transformation is performed to obtain the position correspondence between the bony landmark points and the jaw positions. By utilizing the reference role of the bony landmark points and the anatomical positioning of the jaw feature points, the coordinate system is accurately established and registered, ensuring the precise spatial correspondence between the facial and jaw models, providing an accurate spatial reference for the subsequent determination of the guide plate positioning reference point and the guide plate design, enabling the guide plate to better adapt to the individual characteristics of the patient, and improving the precision and success rate of the implant surgery.

[0027] 3. In the present application, the video frame sequence is obtained by decomposing the facial motion video data frames, the motion trajectory of the bony landmark points is tracked by using the feature point tracking algorithm, the three-dimensional spatial coordinates at different times are obtained through three-dimensional reconstruction, and then the dynamic displacement data is calculated. Through the processing such as frame decomposition and feature point tracking of the video data, the motion trajectory of the bony landmark points during facial expression changes is accurately captured. The three-dimensional reconstruction and displacement calculation provide accurate dynamic displacement information, which can deeply analyze the deformation law of facial soft tissues, provide a reliable basis for selecting the landmark point with the smallest relative distance change as the guide plate positioning reference point, thereby enhancing the stability of guide plate positioning and ensuring the implant restoration effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flowchart of a method for processing implant data of an edentulous jaw guide plate in an embodiment of the present application;

[0029] Figure 2 It is another process schematic diagram of the implant data processing method for the edentulous jaw guide plate in the embodiments of the present application;

[0030] Figure 3 It is a schematic structural diagram of an entity device of the implant data processing system in the embodiments of the present application. Specific embodiments

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0033] With the aggravation of the aging population, the number of edentulous jaw patients continues to increase. Such patients generally have problems such as masticatory dysfunction, unclear pronunciation, and facial morphological changes, which seriously affect the quality of life. Full-mouth implant restoration is currently the most ideal treatment plan, but it faces the technical problem of difficult to ensure implant accuracy. Especially when determining the position of the implant guide plate, the facial soft tissues of elderly patients have a large degree of relaxation, and obvious deformation occurs during facial expression changes, which leads to easy position deviation of the guide plate designed based on static data during use. Clinical practice shows that the position accuracy of the implant is directly related to the positioning stability of the guide plate. Therefore, how to ensure the positioning stability of the guide plate under the condition of dynamic facial soft tissue deformation has become a key technical problem affecting the treatment effect.

[0034] Traditional implant guide plate design methods mainly rely on static data. Clinically, titanium alloy marker points are attached to the patient's face, and facial CT scan data in the closed state is collected for guide plate design. This method has obvious limitations in practical applications: when the patient performs daily activities such as speaking and swallowing, the facial soft tissues deform, resulting in displacement of the positioning struts of the guide plate designed based on the static position. Practical data shows that this displacement may cause a deviation of 3-5 degrees in the implant angle, affecting the distribution and arrangement of the implants, and reducing the stability and service life of the prosthesis.

[0035] The newly developed dynamic guide design system uses a multi-source data fusion method to collect static feature data, dynamic expression videos, and jaw CT data at the same time. The system analyzes the movement trajectory of the marker points in daily expression activities, identifies relatively stable areas of the face, and uses these areas as the focus points of the guide positioning pillars. Experimental data show that the guide designed based on dynamic data can remain stable when facial expressions change, and the implant placement deviation is controlled within 0.5mm.

[0036] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of the implant data processing method of the edentulous jaw guide plate in the embodiment of the present application.

[0037] S101, acquiring facial static feature data, facial motion video data, and jaw tomography data of a target patient, wherein the facial feature data includes three-dimensional spatial position information of bony landmarks.

[0038] Among them, the target patient refers to a patient who needs edentulous jaw implant restoration, which is used to represent the object of data collection. Facial static feature data refers to the collected patient facial surface morphology information, including three-dimensional point cloud data and the position of bony landmark points. Facial motion video data refers to a continuous image sequence recording the patient completing standard facial expressions. Jaw tomography data refers to a sequence of jaw cross-sectional images obtained using CT and other equipment. Bone landmark points refer to spherical markers attached to specific anatomical positions on the patient's face, which are used to establish the spatial correspondence between the face and the jaw. Three-dimensional spatial position information represents the X, Y, and Z coordinate values ​​of the landmark points in the three-dimensional coordinate system.

[0039] This step is performed when the patient first visits the doctor to collect the basic data required for the subsequent guide plate design. Specifically, the system first uses a three-dimensional optical scanner to scan the patient's face and collect facial surface point cloud data with a resolution of 0.1mm. At the same time, a titanium alloy spherical marker with a diameter of 3mm is attached to the obvious anatomical landmarks on the patient's face. A binocular high-speed camera system is used to record the process of the patient completing standard facial expressions such as smiling and chewing. The video sampling rate is 120fps and the resolution is 4096×3072 pixels. Finally, cone beam CT is used to scan the patient's jaw area with a layer thickness of 0.125mm, and the scanning range covers the entire alveolar bone area.

[0040] In some embodiments, the data acquisition process can be implemented in various ways: Optionally, a structured light 3D scanner is used to acquire facial static data, the scanning path is controlled by a robotic arm, and a complete facial model is stitched by using a multi-angle scanning method; an industrial camera array is used to synchronously acquire facial motion videos, and time and space synchronization calibration is performed between the cameras; spiral CT is used for jaw bone scanning and motion artifact correction. Optionally, a handheld laser scanner is used to acquire facial data, and real-time registration and stitching are performed during the scanning process; a monocular high-speed camera is used in combination with a depth camera to acquire facial motion videos; CBCT is used for jaw bone scanning, and a metal artifact reduction algorithm is used to optimize the image quality. It can be understood that other data acquisition devices and methods can also be used to obtain the required data, which is not limited here.

[0041] S102. Extract features from the facial static feature data to obtain a facial static 3D model.

[0042] Among them, feature extraction refers to the process of extracting key information required to construct a 3D model from the original data. Features include geometric features such as spatial coordinates, normal vectors, and curvatures in the point cloud data. The facial static 3D model refers to a digital model that reconstructs the patient's facial appearance through computer graphics methods, including facial contours, surface details, and marker point position information.

[0043] This step is executed after obtaining the facial static feature data and is used to generate a standardized 3D model for subsequent processing. Specifically, the system first preprocesses the original point cloud data, including outlier removal and noise smoothing. Then, point cloud downsampling and mesh reconstruction are performed to generate a regular triangular mesh model. The reconstructed mesh is optimized, including operations such as filling holes, optimizing the topology structure, and simplifying the mesh. Finally, the exact positions of the bony landmark points are extracted and marked, and the local geometric features around each marker point are calculated.

[0044] In some embodiments, feature extraction can be implemented in various ways: Optionally, statistical outlier detection is used to remove abnormal points; moving least squares is used for point cloud smoothing; Poisson reconstruction algorithm is used to generate a watertight triangular mesh model; marker point positions are identified based on local curvature analysis. Optionally, a density-based clustering algorithm is used to filter noise points; bilateral filtering is used for point cloud smoothing; greedy projection triangulation algorithm is used to generate a mesh model; marker points are located based on template matching methods. It can be understood that other feature extraction and model reconstruction algorithms can also be used to construct the 3D model, which is not limited here.

[0045] S103. Extract features from the jaw bone tomographic scan data to obtain a jaw bone 3D model.

[0046] Among them, the jawbone tomography data refers to the cross-sectional sequence images of the jawbone obtained using imaging devices such as CT, which are used to represent the internal structure information of the jawbone. Feature extraction refers to the process of identifying and extracting jawbone tissues from continuous two-dimensional tomographic images. The three-dimensional jawbone model refers to a digital anatomical model constructed through image processing and three-dimensional reconstruction techniques, which is used to represent the external shape and internal structure of the jawbone. The jawbone tissues include two types of bone tissues with different densities, cortical bone and cancellous bone, which are respectively represented by different gray value ranges in CT images.

[0047] This step is executed after obtaining the jawbone tomography data and is used to generate an accurate three-dimensional model of the jawbone. Specifically, the system first preprocesses the CT image sequence, including image enhancement and noise suppression, to improve the clarity of tissue boundaries. Then, a multi-threshold segmentation method is used to extract the cortical bone and cancellous bone regions respectively, and the threshold range is determined based on the Hounsfield value. Morphological processing is performed on the segmentation results, including boundary smoothing and hole filling. Finally, a three-dimensional reconstruction algorithm is used to reconstruct the continuous segmentation results into a high-precision three-dimensional mesh model while retaining the bone density information.

[0048] In some embodiments, the construction of the three-dimensional jawbone model can be achieved in various ways: Optionally, adaptive histogram equalization is used to enhance the image contrast; a multi-threshold segmentation method based on region growing is adopted to separate different bone tissues; morphological closing operations are used to fill small holes; the marching cubes algorithm is used to reconstruct the three-dimensional surface. Optionally, anisotropic diffusion filtering is used to eliminate image noise; an active contour segmentation method based on level sets is adopted to extract the bone tissue boundaries; distance transformation is used to smooth the segmentation boundaries; a surface reconstruction algorithm is used to generate the three-dimensional model. It can be understood that other image processing and model reconstruction methods can also be adopted to achieve the construction of the three-dimensional jawbone model, which is not limited here.

[0049] S104. Perform spatial registration on the static three-dimensional facial model and the three-dimensional jawbone model to obtain the position correspondence relationship between the bony landmark points and the jawbone positions.

[0050] Among them, spatial registration refers to the process of transforming the static three-dimensional facial model and the three-dimensional jawbone model into the same coordinate system. The position correspondence relationship represents the spatial position description of the bony landmark points in the jawbone coordinate system. The registration transformation includes two basic transformations, rotation and translation, which are used to adjust the spatial position and attitude of the models. The bony landmark points, as the reference points for registration, have clear spatial coordinates in both models.

[0051] This step is performed after the three-dimensional model reconstruction of the face and jawbone, and is used to establish the spatial mapping relationship between the two models. Specifically, the system first extracts the three-dimensional coordinates of all bony landmark points from the facial model, and extracts the corresponding marker point positions from the jawbone model. The facial coordinate system and the jawbone coordinate system are established, and the transformation matrix between the two coordinate systems is calculated. The rigid body transformation algorithm is used for model registration to ensure that the shape of the model does not change before and after the transformation. Finally, the registration accuracy is verified, the residuals between all corresponding points are calculated, and the registration error is ensured to be within the clinically acceptable range.

[0052] In some embodiments, the spatial registration process can be implemented in various ways: Optionally, the principal component analysis method is used to initialize the model direction; the iterative closest point algorithm is used for rough registration; the improved quaternion method is used to optimize the registration result; the registration error is calculated and position fine-tuning is performed. Optionally, feature point matching is used to determine the initial position; the registration algorithm based on gradient descent is used to optimize the transformation parameters; the robust estimation method is used to eliminate the influence of abnormal matching points; the registration quality score is calculated and optimized. It can be understood that other registration algorithms and optimization methods can also be used to achieve the spatial alignment of the two models, which are not limited here.

[0053] S105. Extract the landmark motion trajectory data from the facial motion video data to obtain the dynamic displacement data of each landmark. The dynamic displacement data includes the three-dimensional spatial coordinate change information of the landmark under different expression states.

[0054] Among them, the facial motion video data refers to the sequence of facial motion images of the patient recorded by a high-speed camera, which is used to represent the dynamic process of facial expression changes. The landmark motion trajectory data represents the continuous position changes of the bony landmarks in the video sequence. The dynamic displacement data is the spatial displacement amount of the landmark relative to the initial position, which is used to describe the deformation characteristics of the facial soft tissue. The three-dimensional spatial coordinate change information represents the displacement components of the landmark in the X, Y, and Z directions. The expression state refers to the standard expression actions made by the patient, including daily facial activities such as smiling and chewing.

[0055] This step is performed after the video data acquisition, and is used to obtain the motion characteristics of the landmarks. Specifically, the system first decomposes the video data into frames to generate a continuous image sequence at 120 fps. Each frame of the image is subjected to distortion correction and noise suppression to improve the image quality. The deep learning object detection algorithm is used to identify the marker point positions in each frame, and the sub-pixel level two-dimensional coordinates are output. Based on the binocular stereo vision principle, the three-dimensional coordinates of the marker points are restored through disparity calculation. The three-dimensional coordinate sequence is filtered in the time domain to eliminate tracking noise and measurement errors. Finally, the displacement amount of each marker point relative to the initial frame is calculated to form a complete dynamic displacement data set.

[0056] In some embodiments, the extraction of the fiducial point trajectory can be achieved in various ways: Optionally, an improved YOLOv5 network is used to detect the fiducial points; Kalman filtering is adopted to predict the positions of the fiducial points; the template matching algorithm is combined to improve the positioning accuracy; triangulation is used to calculate the three-dimensional coordinates; and a Butterworth filter is adopted to smooth the trajectory. Optionally, a Faster R-CNN detector is used to locate the fiducial points; the optical flow method is adopted to track the movement of the fiducial points; feature point matching is used to optimize the tracking results; three-dimensional positions are reconstructed based on multi-view geometry; and wavelet transform is used for denoising. It can be understood that other object detection and trajectory tracking algorithms can also be adopted to achieve the extraction of the fiducial point movement trajectory, which is not limited herein.

[0057] S106. According to the dynamic displacement data, calculate the relative distance change amount between each pair of the fiducial points, and select a preset number of fiducial points with the smallest relative distance change amount as the guide plate positioning reference points.

[0058] Among them, the relative distance change amount refers to the change amplitude of the Euclidean distance between any two fiducial points over time, which is used to represent the stability of the positional relationship between the fiducial point pairs. The preset number represents the number of reference points determined according to the guide plate design requirements. The guide plate positioning reference points refer to the reference points used to determine the spatial position of the implant guide plate, and these points have high spatial stability during the facial expression change process.

[0059] This step is executed after obtaining the dynamic displacement data of the fiducial points and is used to screen the most stable combination of fiducial points. Specifically, the system first calculates the Euclidean distance between each pair of fiducial points in the entire motion sequence to generate a distance-time curve. Extreme value analysis is performed on each curve, and the difference between the maximum value and the minimum value is calculated as the relative distance change amount. The change amounts of all fiducial point pairs are sorted in ascending order to establish a change amount sorting table. Starting from the top of the sorting table, select the point pairs with the smallest change amount, and at the same time consider the spatial distribution characteristics of the points to ensure that the selected reference points can form a stable spatial configuration. Finally, a specified number of fiducial points are selected as the guide plate positioning reference points.

[0060] In some embodiments, the selection of the reference points can be achieved in various ways: Optionally, the Euclidean distance is used to calculate the distance between point pairs; the sliding window method is adopted to detect the distance extreme values; the relative change amount is calculated and a sorting heap is constructed; candidate points are screened based on spatial distribution constraints; and the stability of the selected point set is verified. Optionally, the Mahalanobis distance is used to measure the relationship between point pairs; the peak detection algorithm is adopted to find the distance change range; quicksort is used to construct the change amount sequence; the convex hull analysis is combined to select a point set with reasonable spatial distribution; and the geometric constraint ability of the point set is evaluated. It can be understood that other distance metrics and point set selection methods can also be adopted to determine the guide plate positioning reference points, which is not limited herein.

[0061] S107, generating a digital design scheme for the guide plate according to the three-dimensional coordinate information of the guide plate positioning reference point and the three-dimensional model of the jaw.

[0062] Among them, the three-dimensional coordinate information of the guide plate positioning reference point refers to the spatial position data of the selected reference point in the mandibular coordinate system, which is used to determine the spatial positioning of the guide plate. The three-dimensional model of the mandible refers to a digital model that expresses the patient's mandibular anatomical structure, including the outer contour and internal structure information. The digital design plan of the guide plate represents the complete three-dimensional model of the implant guide, including the geometric parameters and spatial relationships of components such as the guide plate base, positioning pillars and supporting structure. The guide plate base refers to the main structure that fits the surface of the mandible. The positioning pillar refers to the supporting pillar connecting the base and the reference point. The supporting structure refers to the reinforcing structure that connects the pillars to form an overall framework.

[0063] This step is performed after the guide plate positioning reference point is determined, and is used to generate a complete guide plate design plan. Specifically, the system first projects the reference point onto the mandibular surface to determine the position of the pillar's fulcrum. A variable-section pillar is constructed at each projection point, and the pillar height is determined according to the local anatomical gap. The mandibular surface mesh is extracted and locally optimized to generate a guide plate matrix that fits the mandibular surface precisely. Connecting beams are designed between the pillars to form a support frame, and the frame adopts an I-shaped cross-section to increase strength. Finite element analysis is performed on the overall structure to verify its mechanical properties under the use load. Finally, a 3D model file in a standard format is generated, containing parametric design information of all components.

[0064] In some embodiments, the digital design of the guide plate can be achieved in a variety of ways: optionally, the position of the pillar is determined using normal projection; the surface mesh of the jaw is extracted based on the scan line algorithm; the guide plate matrix is ​​generated using the offset algorithm; the pillar model is created using parametric modeling; a B-spline surface is constructed to connect adjacent pillars; and structural strength and stability analysis is performed. Optionally, the pillar is positioned using the nearest point projection; surface features are extracted based on octree partitioning; a matrix is ​​generated using hierarchical Boolean operations; pillars are constructed using feature templates; support structures are designed using stress-guided topology optimization; and dynamic load simulation analysis is performed. It is understandable that other three-dimensional modeling and structural optimization methods can also be used to achieve the digital design of the guide plate, which is not limited here.

[0065] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the implant data processing method of the edentulous jaw guide plate in the embodiment of the present application.

[0066] S201, acquiring facial static feature data, facial motion video data, and jaw tomography data of a target patient, wherein the facial feature data includes three-dimensional spatial position information of bony landmarks.

[0067] It can be understood that this step is similar to step S101 and will not be elaborated here.

[0068] S202. Extract features from the facial static feature data to obtain a facial static three-dimensional model.

[0069] It can be understood that this step is similar to step S102 and will not be elaborated here.

[0070] S203. Extract features from the jawbone tomography data to obtain a jawbone three-dimensional model.

[0071] It can be understood that this step is similar to step S103 and will not be elaborated here.

[0072] S204. Use the bony landmark points in the facial static three-dimensional model as reference points to establish a coordinate system for the facial static three-dimensional model.

[0073] Bony landmark points refer to radiopaque markers attached to the surface of the patient's face, which are located at bony prominences with fixed anatomical positions and easy to identify. Reference points refer to the reference points used to establish a coordinate system. Usually, three or more non-collinear bony landmark points are selected as reference points. The facial static three-dimensional model coordinate system is a three-dimensional rectangular coordinate system established based on the selected reference points, used to describe the spatial positions of points in the facial model.

[0074] The system first extracts the spatial coordinates of all bony landmark points from the facial static three-dimensional model, and selects two points on the left and right supraorbital margins and the nasion as the main reference points. The origin and main axis direction of the coordinate system are determined by these three points: the nasion is set as the coordinate origin, the connection direction of the two supraorbital margin points is defined as the positive direction of the X axis, the connection direction from the nasion to the midpoint of the two supraorbital margin points is defined as the positive direction of the Y axis, and the positive direction of the Z axis is determined according to the right-hand rule. The system uses the least squares method to optimize the coordinates of the reference points to ensure that the established coordinate system has the best orientation accuracy.

[0075] S205. Extract jawbone feature points based on the anatomical features of the jawbone three-dimensional model and establish a jawbone coordinate system.

[0076] Jawbone feature points refer to the characteristic positions on the jawbone anatomical structure, including anatomical landmarks such as the highest point of the alveolar ridge, the mandibular angle, and the mandibular foramen. The jawbone coordinate system is a three-dimensional rectangular coordinate system established based on the extracted jawbone feature points, used to describe the spatial position relationship of the jawbone anatomical structure.

[0077] The system adopts curvature analysis and morphological feature extraction methods to automatically identify anatomical feature points on the surface of the three-dimensional jaw model. For the maxilla, the deepest points of the left and right lateral incisor fossae and the posterior end point of the median palatine suture are selected as reference points; for the mandible, the left and right mandibular angles and mental foramina are selected as reference points. Based on these feature points, the system establishes a jaw coordinate system: the midpoint of the incisor fossa or the midpoint of the mental foramen is used as the origin, the direction of the line connecting the symmetric points on both sides is defined as the X-axis, and the direction perpendicular to the occlusal plane is defined as the Z-axis, thereby determining the complete jaw coordinate system.

[0078] S206. Perform a registration transformation on the coordinate system of the static three-dimensional facial model and the jaw coordinate system, and calculate the three-dimensional coordinates of the bony landmark points in the jaw coordinate system after registration to obtain the position correspondence relationship between the bony landmark points and the jaw position.

[0079] Registration transformation refers to the mathematical operation process of converting points in the coordinate system of the static three-dimensional facial model to the jaw coordinate system, including rotation and translation transformations. The position correspondence relationship refers to the spatial coordinates of the bony landmark points in the jaw coordinate system, which reflects the relative position of the landmark points with respect to the jaw anatomical structure.

[0080] The system uses a rigid body transformation algorithm for coordinate system registration. The specific steps include: first, calculating the rotation matrix and translation vector between the two coordinate systems; then, converting the coordinates of all bony landmark points to obtain their new coordinates in the jaw coordinate system; finally, establishing a spatial correspondence relationship database between the landmark points and the jaw anatomical structure. The system adopts the ICP (Iterative Closest Point) algorithm for registration optimization, and the registration accuracy is better than 0.5 mm. The calculation of the transformation matrix uses the SVD (Singular Value Decomposition) method, which ensures the rigid body characteristics of the transformation and avoids model deformation.

[0081] S207. Decompose the facial motion video data into frames to obtain a continuous sequence of video frames.

[0082] Facial motion video data refers to a continuous sequence of dynamic images that record the facial expression changes of a patient, which are collected by a high-speed imaging device at a fixed frame rate. The video frame sequence refers to decomposing the continuous video data into discrete image frames in chronological order, and each frame represents the facial state at a specific moment. Frame decomposition is the process of converting video data into an image sequence that can be processed separately.

[0083] The system uses a video decoder to decompose the original video data at the acquisition frame rate, generating 120 image sequences per second. The decomposition process includes: first, reading the encoding format and resolution information of the video file; then, splitting the data stream into independent frames according to the video timestamp; and finally, converting each frame into a standard image format and saving it. The system uses the H.264 decoding algorithm to process the video data, generating an RGB image sequence with a resolution of 1920×1080 pixels to ensure that the image quality meets the requirements of subsequent processing. The time interval between video frames is maintained at 8.33 milliseconds, ensuring the continuity of motion capture.

[0084] S208. Use the feature point tracking algorithm to track the motion trajectory of each bony landmark in this video frame sequence.

[0085] The feature point tracking algorithm refers to a calculation method for automatically identifying and tracking the position of target points in a continuous video frame sequence. The motion trajectory of the bony landmark is the spatial position change path of the marker point during the entire facial expression change process. The feature point tracking process needs to solve problems such as target recognition, position prediction, and trajectory smoothing.

[0086] The system uses a multi-feature fusion tracking algorithm to achieve landmark tracking. First, detect the positions of all marker points in the initial frame through color and shape features; then use the Kalman filter to predict the approximate positions of the marker points in the next frame; then use the template matching method to accurately locate the marker points within the predicted area; finally, use the B-spline curve to smooth the obtained trajectory. The system integrates SIFT feature matching and optical flow method, and can still maintain a stable tracking effect under the conditions of light change and fast movement, and the tracking accuracy reaches the sub-pixel level.

[0087] The tracking module of the system first uses the FasterR-CNN network trained with marker point data to detect the positions of all marker points in the first frame. For each detected marker point, extract a 16×16 pixel area centered on it as a feature template. In the processing of subsequent frames, the system uses the Kalman filter to predict the motion state of the marker points, and the prediction search range is a 32×32 pixel area. Use the normalized cross-correlation algorithm for template matching within the predicted area, and set the matching threshold to 0.85 to ensure accuracy. The matching position is accurately located at the sub-pixel level through the centroid calculation method, and the positioning accuracy reaches 0.1 pixel. The system dynamically updates the Kalman filter state and template features, and the feature update adopts the exponential sliding average method, with the historical feature weight of 0.9. When the marker point is occluded, use the trained LSTM network to predict its position until the marker point is redetected. Finally, use the Savitzky-Golay filter to smooth the trajectory, with a filter window length of 21 frames and a polynomial order of 3.

[0088] S209. Perform three-dimensional reconstruction on the motion trajectory to obtain the three-dimensional spatial coordinates of each of these bony landmark points at different times.

[0089] It refers to the position of the landmark point in the world coordinate system, including the position information in the X, Y, and Z directions. The coordinate sequences at different times constitute the complete spatial motion trajectory.

[0090] The system uses the binocular stereo vision method for three-dimensional reconstruction. First, calibrate the binocular cameras to obtain the internal and external parameters of the cameras; then use the epipolar constraint to determine the corresponding relationship of the landmark points in the left and right views; then use the triangulation principle to calculate the three-dimensional coordinates of the landmark points; finally, perform temporal filtering on the reconstruction results to eliminate the reconstruction noise. The system uses the Zhang Zhengyou calibration method for camera calibration, and the reconstruction accuracy is better than 0.1 mm. For the occlusion situation, the system performs trajectory compensation through multi-view geometric constraints and motion continuity constraints to ensure the integrity of the reconstruction results.

[0091] In the reconstruction process, the system first uses a standard 9×12 checkerboard calibration board to collect 20 groups of calibration images with different poses, and calculates the camera internal parameter matrix and lens distortion coefficients through the Zhang Zhengyou calibration algorithm. Subsequently, calculate the rotation matrix and translation vector between the two cameras according to the principle of epipolar geometry, and establish the geometric relationship model of the binocular system. Perform distortion correction and epipolar correction on each pair of collected images to generate a pair of corrected standard images. The binocular matching process uses a region matching algorithm based on normalized cross-correlation, with a matching window size of 7×7 pixels, and combines the dynamic programming algorithm to ensure the global consistency of the matching process. After obtaining the disparity data, calculate the three-dimensional coordinates of the landmark points through the triangulation principle, and transform the coordinates to the world coordinate system. The system performs temporal filtering on the reconstruction results using a Butterworth low-pass filter with a cut-off frequency of 30 Hz to eliminate high-frequency noise. For the data missing caused by occlusion, the cubic spline interpolation method is used to complete the trajectory. Finally, output three-dimensional trajectory data at 120 Hz, including the temporal and spatial coordinate information of all landmark points.

[0092] S210. Based on the three-dimensional spatial coordinates, calculate the displacement of each bony landmark point relative to the initial position to obtain the dynamic displacement data, which includes the three-dimensional spatial coordinate change information of the landmark points in different facial expression states.

[0093] The three-dimensional spatial coordinates refer to the absolute position of the bony landmark point in the world coordinate system, represented by a rectangular coordinate system (X, Y, Z). The initial position refers to the spatial position of the landmark point when the patient maintains a natural state, serving as a reference point for calculating the displacement. The dynamic displacement data records the relative movement amount of the landmark point relative to the initial position, including the displacement components in three directions and the composite displacement.

[0094] The system first synchronizes the time of the reconstructed three-dimensional trajectory data, aligning the timestamps of all frames to a unified benchmark. For each marker point, the position of the first frame is taken as the initial reference point P0(x0, y0, z0). The difference between the position of the marker point Pi(xi, yi, zi) in each subsequent frame and the initial position is calculated to obtain the displacement vector D(dx, dy, dz). The system stores the displacement data using 32-bit floating-point numbers, with a precision of 0.001 mm. The calculated displacement data is subjected to least squares fitting to obtain the displacement-time curves in each direction. The curve fitting uses a fifth-order polynomial model, and the fitting residual is controlled within 0.01 mm. The finally output dynamic displacement data includes a displacement time series with a sampling rate of 120 Hz, recording the complete process of facial expression changes.

[0095] S211. Based on this dynamic displacement data, calculate the Euclidean distance between any two of these osseous landmark points in different expression states.

[0096] The Euclidean distance refers to the straight-line distance between two points in three-dimensional space, reflecting the relative positional relationship between two marker points in space. For any two marker points, their Euclidean distance changes with facial expressions, and this change reflects the deformation characteristics of facial soft tissues.

[0097] The system establishes an N×N distance matrix (N is the total number of marker points), and calculates the Euclidean distance between all pairs of marker points in each frame. For the marker points Pi(xi, yi, zi) and Pj(xj, yj, zj), calculate their Euclidean distance dij = √[(xi - xj)² + (yi - yj)² + (zi - zj)²]. The calculation uses a CUDA parallel processing architecture to simultaneously process the distance calculations for all pairs of points. The system generates a distance-time curve for each pair of points, with the sampling rate remaining the same as the original data (120 Hz). To improve the calculation accuracy, the system uses the Kahan summation algorithm to eliminate the cumulative error of floating-point operations, ensuring that the distance calculation accuracy is better than 0.005 mm.

[0098] S212. Obtain the maximum change amount of the relative distance between each pair of osseous landmark points during the entire movement process.

[0099] The relative distance refers to the Euclidean distance between two marker points, and the maximum change amount refers to the difference between the maximum and minimum values of the distance between two points during the entire movement process. This index reflects the stability of the relative positional relationship between two marker points. The smaller the change amount, the more stable the relative position between the two points.

[0100] The system analyzes the distance-time curve of each pair of markers. First, the local maximum and minimum values ​​of the distance are found through the peak detection algorithm, and the detection threshold is set to 0.05mm to filter out noise fluctuations. The detected extreme values ​​are sorted, the maximum value dmax and the minimum value dmin are taken, and the maximum change Δd=dmax-dmin is calculated. The system generates an N×N change matrix, where N is the total number of markers, and the matrix element (i, j) stores the maximum distance change between the i-th and j-th markers. At the same time, the time points when the maximum and minimum distances occur are recorded for subsequent analysis of the timing characteristics of deformation. The calculation process uses double-precision floating point numbers to ensure that the calculation accuracy of the change reaches 0.001mm.

[0101] S213, sorting the relative distance changes from small to large, and selecting the first N bony landmarks with the smallest changes as the guide plate positioning reference points, where N is equal to the preset number.

[0102] The relative distance change refers to the maximum change in the Euclidean distance between two marking points, in millimeters. The guide plate positioning reference point refers to the reference point used to determine the spatial position of the implant guide. The preset number N is the number of reference points determined based on the geometric dimensions and support requirements of the guide plate, usually set to 4-6.

[0103] The system first constructs an N×N change matrix (N is the number of all marked points), and each matrix element represents the change in the relative distance between the corresponding two points. Use the quick sort algorithm to sort the changes of all non-repeating point pairs in ascending order. Select the point pairs with the smallest change from the sorting results in turn, and check whether the points in the newly selected point pairs are already included in the selected point set. Stop selecting when the cumulative number of non-repeating points selected reaches the preset number. The system evaluates the spatial distribution of the selected reference points, requiring that any three points are not collinear and any four points are not coplanar, to ensure that the spatial configuration of the reference points has sufficient constraint capabilities. If the spatial distribution does not meet the requirements, continue to select the next qualifying point from the sorted list until a set of reference points that meets the geometric constraints is obtained.

[0104] S214. According to the position correspondence, the guide plate positioning reference point is projected onto the surface of the mandibular three-dimensional model to obtain a reference projection point.

[0105] Position correspondence refers to the coordinate mapping relationship between facial landmarks and the jaw model established after spatial registration. Reference projection points refer to the spatial points obtained by projecting the guide plate positioning reference points along the normal direction onto the jaw surface. These points are the actual points of support for the guide plate support structure.

[0106] The system first reads the surface mesh data of the jawbone three-dimensional model, divides the model space using an octree structure to accelerate the nearest point search. For each selected guide plate positioning reference point, the system calculates the direction of the shortest distance from it to the jawbone surface, and this direction is the projection direction. Search for the intersection point on the jawbone surface along the projection direction as the reference projection point. To ensure the local smoothness of the projection point, the system extracts the surface mesh within a range of 10 mm around the projection point and uses a bicubic B-spline function to perform local surface reconstruction. The final position of the projection point is located on the reconstructed surface. The system also records the surface normal vector and the principal curvature direction at each projection point for the subsequent design of the support structure.

[0107] S215. Calculate the offset area of the reference projection point within the corresponding range of the dynamic displacement data, and construct positioning struts at the boundary of this area.

[0108] The offset area refers to the displacement range of the reference projection point during facial movement, which is determined by the dynamic displacement data. The positioning strut is a support structure connecting the guide plate base and the jawbone surface, used to ensure the precise positioning of the guide plate.

[0109] The system first analyzes the dynamic displacement data corresponding to each reference projection point and calculates its spatial envelope surface during the entire movement cycle. Project the displacement data into the local coordinate system of the projection point, where the Z-axis of the coordinate system is along the surface normal and the X-axis is along the direction of the maximum principal curvature. Calculate the extreme value range of the displacement in the local coordinate system to determine the boundary of the offset area. The system designs the geometric parameters of the strut according to the size of the offset area: the diameter of the strut base is taken as 1.2 times the diameter of the circumscribed circle of the offset area; the height of the strut is determined according to the local anatomical clearance, generally 5 - 8 mm; the cross-section of the strut adopts a variable cross-section design, with a circular bottom and a square top for easy connection with the guide plate base. The material of the strut is selected as medical-grade photosensitive resin, and the surface is specially treated to increase biocompatibility. The system uses a parametric modeling method to generate the three-dimensional model of the strut and adds a fillet (radius 0.5 mm) on the surface of the strut to reduce stress concentration.

[0110] S216. Generate a support structure connecting each of the positioning struts according to the jawbone three-dimensional model and the spatial distribution of the positioning struts.

[0111] The support structure is the framework part connecting adjacent positioning struts, used to enhance the overall stiffness and stability of the guide plate. The spatial distribution of the positioning struts refers to the relative position relationship of each strut on the jawbone surface, and these positions determine the geometric shape of the support structure. The jawbone three-dimensional model provides the anatomical boundary conditions for the support structure design.

[0112] The system adopts a parametric design method to generate the support structure. First, a Delaunay triangulation network is constructed, and the tops of all positioning struts are connected into a closed polygon. For each connecting edge, the system calculates the distance and relative height difference between the two struts at both ends to generate a variable cross-section connecting beam. The connecting beam adopts an I-shaped cross-section, and the cross-section parameters adaptively change with the span: the flange width is 4-6 mm, the web thickness is 2-3 mm, and the total height is 1 / 10 of the span. The path of the connecting beam takes into account the undulation of the jaw surface and is transitioned using a cubic Bezier curve to ensure that the minimum gap with the jaw surface is not less than 2 mm. The system performs finite element analysis on the support structure to ensure that the maximum deformation under a 100 N vertical load is less than 0.1 mm. The material of the support structure is medical-grade PEEK material, which has good mechanical properties and biocompatibility.

[0113] S217. Construct a guide plate base on the basis of the surface contour of the three-dimensional jaw model and the positions of the positioning struts.

[0114] The guide plate base is the main structure covering the jaw surface, used to support the implant guiding device and fix the entire guide plate system. The surface contour refers to the geometric features on the surface of the three-dimensional jaw model, including concavity and convexity changes and anatomical landmarks. The positions of the positioning struts determine the boundary range and the distribution of fixed points of the base.

[0115] The system first marks the surgical area on the jaw surface and expands it by 5 mm outside this area as the coverage range of the guide plate base. Extract the surface mesh within this range and optimize the mesh quality using an adaptive resampling algorithm, with the side length of the triangular patches controlled between 0.5-1 mm. Offset 2 mm outward along the surface normal to generate the inner surface of the guide plate base, and the outer surface is uniformly thickened by 3 mm according to the inner surface. At the positions of the positioning struts, the system automatically generates a locking structure, which adopts a dovetail groove design with a groove width of 4 mm, a depth of 3 mm, and an inclination angle of 15 degrees to ensure the reliable connection between the struts and the base. The system arranges drainage and ventilation holes on the surface of the base, with a hole diameter of 1 mm and a spacing of 5 mm, avoiding the positions of the struts and ribs. The material of the guide plate base is selected as a photocurable transparent resin, with a hardness reaching above Shore D85.

[0116] S218. Construct the digital design plan of the guide plate according to the guide plate base, the positioning struts, and the support structure.

[0117] The digital design plan of the guide plate is a complete three-dimensional digital model, containing all the geometric information and material parameters of the guide plate base, positioning struts, and support structure. This design plan will be used for subsequent 3D printing manufacturing and clinical applications.

[0118] The system uses parametric modeling software to integrate all components. First, the guide plate base is taken as the main body, and the reference planes for the installation positions of the struts are arranged on its surface. The positioning struts are precisely docked with the base through dovetail grooves, and the contact surface adopts an interference fit of 0.05 mm to ensure the stability after assembly. The support structure is connected to the struts and the base by chemical bonding, and the contact surface is designed with positioning bosses. The system automatically generates engineering drawings and assembly drawings of all parts, including key dimensions and tolerance requirements. At the same time, an STL file for 3D printing is generated, and the model accuracy is set to 0.01 mm to ensure the dimensional accuracy of the printed parts. The system conducts finite element analysis verification on the complete guide plate, including static analysis and fatigue analysis, to ensure its safety in use in the oral environment. The final design scheme is output as a 3D model file in a standard format, containing a complete parametric history record for subsequent modification and optimization.

[0119] In some embodiments, after the step, the following steps are further included:

[0120] Based on the static three-dimensional facial model, a patient facial feature database is established.

[0121] Specifically, the facial feature database refers to a structured data set for storing the patient's facial anatomical features and morphological parameters. The static three-dimensional facial model represents the digital expression of the patient's facial surface morphology. The features include quantitative parameters such as facial contour curves, positions of anatomical landmark points, and surface curvature distributions. The database structure includes multiple data tables such as the patient's basic information, feature data, and index relationships.

[0122] This step is executed after the reconstruction of the three-dimensional facial model and is used to establish a standardized feature storage system. Specifically, the system first extracts key anatomical features from the three-dimensional model, including feature curves such as facial contour lines, nasolabial folds, and orbital margins. Calculate the principal curvature distribution and shape index of the facial surface to generate a surface morphology feature descriptor. Establish a relational database based on PostgreSQL, design patient information tables, feature parameter tables, and index tables. Store the extracted feature data in the database in a standard format and establish the association relationships between the feature data.

[0123] In some embodiments, the establishment of the feature database can be achieved in various ways: Optionally, use differential geometry methods to extract surface features; establish a B-tree-based feature index structure; store feature data in JSON format; design data integrity constraints; implement database backup and recovery mechanisms. Optionally, use deep learning methods to extract semantic features; establish a hash index to accelerate queries; compress and store in binary format; design triggers to maintain data consistency; implement distributed data synchronization. It can be understood that other database technologies and feature storage methods can also be used to manage facial features, which are not limited here.

[0124] The distribution characteristics of the guide plate positioning reference points are stored in the feature database.

[0125] Specifically, the distribution characteristics of the guide plate positioning reference points refer to the spatial arrangement pattern of the reference points, including the relative position relationship between the points and the geometric configuration characteristics. The distribution characteristics include the three-dimensional coordinates of the reference points, the distance matrix between the point pairs, the geometric invariants of the configuration, and other information. The feature database is used to store and manage these distribution feature data.

[0126] This step is performed after the guide plate positioning reference points are determined, and is used to record the typical pattern of reference point selection. Specifically, the system calculates the spatial distribution characteristics of the reference point set, including geometric invariants such as point-to-point distance matrix, triangle area ratio, and dihedral angle. Generate feature descriptors of the reference point distribution, including position information and configuration parameters. The feature descriptors are stored in a special table structure in the database to establish an association with the patient information.

[0127] In some embodiments, the storage of distribution features can be implemented in a variety of ways: optionally, calculating the Delaunay triangulation of the reference points; extracting the geometric invariants of the configuration; generating standardized feature vectors; designing feature similarity metrics; and implementing feature retrieval functions. Optionally, calculating the convex hull features of the reference points; extracting the main directions of the spatial configuration; generating a compact representation of the features; designing feature clustering analysis; and implementing pattern recognition functions. It is understandable that other feature representation and storage methods can also be used to implement the management of the reference point distribution features, which are not limited here.

[0128] When the solution acquisition instruction is received, the guide plate digital design solution is sent to the target client.

[0129] Specifically, the scheme acquisition instruction refers to the command information sent by the client to request the guide design data. The target client refers to the authorized terminal device used to receive and display the guide design scheme. The guide digital design scheme includes the guide 3D model and related parameter information, using a standard data exchange format.

[0130] This step is executed after receiving the client request and is used to achieve the secure transmission of the design plan. Specifically, the system first verifies the legitimacy of the request instruction and checks the client's access rights. The guide plate design plan is packaged into a standard format, including a 3D model file, a parameter configuration file, and a description document. The data packet is encrypted using an encryption algorithm to ensure the security of the transmission process. The encrypted data is sent to the target client through the network protocol and the receipt confirmation information is verified.

[0131] The following describes the planting data processing system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of a physical device structure of a planting data processing system in an embodiment of the present application.

[0132] It should be noted that Figure 3 the structure of the shown planting data processing system is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

[0133] As Figure 3 shown, the planting data processing system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0134] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0135] Specifically, according to the embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments of the present invention include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309 and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0136] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0138] Specifically, the implant data processing system of this embodiment includes a processor and a memory. A computer program is stored on the memory, and when the computer program is executed by the processor, it implements the implant data processing method of the edentulous jaw guide plate provided in the above embodiment.

[0139] On the other hand, the present invention also provides a computer-readable storage medium. This storage medium can be included in the implant data processing system described in the above embodiment; or it can exist independently and not be assembled into the implant data processing system. The above storage medium carries one or more computer programs, and when the one or more computer programs are executed by a processor of the implant data processing system, the implant data processing system is enabled to implement the implant data processing method of the edentulous jaw guide plate provided in the above embodiment.

[0140] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0141] As used in the foregoing embodiments, depending on the context, the term "when" may be construed to mean "if" or "after" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "upon determining" or "if (the stated condition or event) is detected" may be construed to mean "if determined" or "in response to determining" or "when (the stated condition or event) is detected" or "in response to detecting (the stated condition or event)".

[0142] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by a computer program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the foregoing method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disk that can store program code.

Claims

1. A method for processing implant data of an edentulous jaw guide, characterized in that: Applied to a planting data processing system, the method comprises: Acquire facial static feature data, facial motion video data, and jaw tomography data of a target patient, wherein the facial static feature data includes three-dimensional spatial position information of bony landmarks; Extracting features from the facial static feature data to obtain a facial static three-dimensional model; Extracting features from the jaw tomography data to obtain a three-dimensional model of the jaw; Performing spatial registration of the static three-dimensional facial model with the three-dimensional model of the jaw to obtain a positional correspondence between bony landmarks and jaw positions; Extracting the motion trajectory data of the marker points in the facial motion video data to obtain dynamic displacement data of each marker point, wherein the dynamic displacement data includes three-dimensional space coordinate change information of the marker point under different expression states; Calculate the relative distance change between each of the marking points according to the dynamic displacement data, and select a preset number of marking points with the smallest relative distance change as guide plate positioning reference points; According to the position correspondence, the guide plate positioning reference point is projected onto the surface of the jaw three-dimensional model to obtain a reference projection point; Calculate the offset area of ​​the reference projection point within the corresponding range of the dynamic displacement data, and construct a positioning pillar at the boundary of the area; Generating a support structure connecting each of the positioning pillars according to the three-dimensional model of the jaw and the spatial distribution of the positioning pillars; Constructing a guide plate base based on the surface contour of the three-dimensional jaw model and the position of the positioning pillar; A digital design scheme for the guide plate is constructed based on the guide plate base, the positioning pillars and the supporting structure.

2. The method according to claim 1, characterized in that The step of spatially registering the static facial three-dimensional model with the jaw three-dimensional model to obtain the position correspondence between the bony landmarks and the jaw positions specifically includes: Using the bony landmarks in the static three-dimensional facial model as reference points, establishing a coordinate system for the static three-dimensional facial model; Extracting jaw feature points based on the anatomical features of the jaw three-dimensional model and establishing a jaw coordinate system; The coordinate system of the static three-dimensional facial model is registered and transformed with the jaw coordinate system, and the three-dimensional coordinates of the registered bony landmarks in the jaw coordinate system are calculated to obtain the positional correspondence between the bony landmarks and the jaw positions.

3. The method according to claim 1, characterized in that The step of extracting the motion trajectory data of the marker points in the facial motion video data to obtain the dynamic displacement data of each marker point specifically includes: Performing frame decomposition on the facial motion video data to obtain a continuous video frame sequence; Using a feature point tracking algorithm to track the motion trajectory of each bony landmark point in the video frame sequence; Performing three-dimensional reconstruction on the motion trajectory to obtain the three-dimensional spatial coordinates of each of the bony landmarks at different times; Based on the three-dimensional spatial coordinates, the displacement of each bony landmark point relative to the initial position is calculated to obtain the dynamic displacement data.

4. The method according to claim 1, characterized in that: The step of calculating the relative distance change between each of the marking points according to the dynamic displacement data, and selecting a preset number of marking points with the smallest relative distance change as the guide plate positioning reference points specifically includes: Based on the dynamic displacement data, calculating the Euclidean distance between any two of the bony landmarks in different expression states; Obtain the maximum change in the relative distance between each pair of bony landmarks during the entire movement process; The relative distance changes are sorted from small to large, and the first N bony landmarks with the smallest changes are selected as the guide plate positioning reference points, where N is equal to the preset number.

5. The method according to claim 1, characterized in that After the step of generating a digital design scheme of the guide plate according to the three-dimensional coordinate information of the guide plate positioning reference point and the three-dimensional model of the jaw, the method further comprises: Based on the static three-dimensional facial model, establishing a patient facial feature database; The distribution characteristics of the guide plate positioning reference points are stored in the feature database.

6. The method according to claim 1, characterized in that After the step of generating a digital design plan for the guide plate based on the three-dimensional coordinate information of the guide plate positioning reference point and the three-dimensional model of the jaw, the method further includes: upon receiving a plan acquisition instruction, sending the digital design plan for the guide plate to a target client.

7. A planting data processing system, characterized in that: The planting data processing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the planting data processing system to execute the method described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a planting data processing system, the planting data processing system is caused to execute the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product is run on a planting data processing system, the planting data processing system is caused to execute the method according to any one of claims 1 to 6.

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