Digital intelligent false tooth manufacturing method

Abstract

The invention discloses a digital intelligent false tooth manufacturing method, relates to the technical field of artificial intelligence, and solves the problems that the production timeliness of false teeth cannot be controlled, the production precision is not high, the production efficiency is low and the product improvement is difficult due to inconsistent artificial production quality standards. The false tooth production method comprises the following steps: taking a mold; performing digital importing; aI model generation; aI, arranging teeth; digitally producing a try-on false tooth; trying on the false tooth; designing a guide plate; copying tooth arrangement by a digital guide plate; obtaining finished products; through an improved PointNet + + algorithm and an improved denture production process, a digital technology is utilized to realize standardized process denture production, and through establishing a large database for storing and analyzing denture form data of a patient, the denture production efficiency and the denture delivery efficiency are improved, the denture processing time is shortened, and the denture comfort is improved.

Claims

1. A digital denture production method, characterized in that: The following steps are involved: S1: Acquire and store the anatomical structure data of the patient's oral cavity; The anatomical structure data includes two-dimensional data and point cloud data; the point cloud data includes alveolar ridge point cloud data, hard palate point cloud data, edentulous jaw point cloud data, chewing feature point cloud data and alveolar arch point cloud data; the two-dimensional data includes condylar canal inclination, maximum occlusal gap, normal occlusal gap and inter-jaw distance; S2: Clean the anatomical structure data and import it into the AI ​​design software; S3: The AI ​​design software generates an intraoral alveolar bone model based on the anatomical structure data that has been cleaned, and performs finite element analysis on the intraoral alveolar bone model; S4: Arrange the dentures according to the anatomical data using computer-assisted tooth arrangement or improved PointNet++ algorithm in the intraoral alveolar bone model, extract the arranged digital dentures, and then obtain the denture model. Add brackets and / or steel brackets to the denture model with one click according to the patient's needs; S5: Use 3D printing and cutting equipment to digitally produce trial dentures for the denture models, and provide them to patients for trial dentures; denture models include full denture models and fixed denture models; S6: The designer modifies the three-dimensional data of the denture model according to the patient's denture trial-fitting feedback and obtains the design data of the final denture model; S7: The AI ​​design software generates the final denture guide plate data according to the design data of the final denture model, and uses the 3D printing cutting equipment to produce the final denture guide plate according to the final denture guide plate data; S8: The production staff completes the tooth arrangement, shape fixation and detail adjustment according to the final denture guide plate and determines the final denture model; detail adjustment includes grinding, polishing and cleaning; S9: Use digital technology and traditional technology to make finished dentures based on the final denture model.

2. The method for making digital dentures according to claim 1, characterized in that: In step S1, the doctor uses an intraoral scanning device to accurately scan the inside of the patient's mouth, or uses a traditional molding process to obtain a model of the patient's mouth, and then uses a scanner to perform an extraoral scan of the model to obtain the anatomical structure data of the patient's mouth; the anatomical structure data of the patient's mouth is stored and analyzed in a large database.

3. The method for making digital dentures according to claim 1, characterized in that: The step S2 includes the following steps: S2-1: Adaptively divide the point cloud data in the anatomical structure data into voxels to form a number of three-dimensional voxel grids; denoise the two-dimensional data in the anatomical structure data using a data cleaning algorithm to obtain denoised two-dimensional data; S2-2: Calculate the centroid and center of the three-dimensional voxel grid to obtain the centroid voxel block and the center voxel block respectively; S2-3: Use the Gaussian function to calculate the spatial domain kernel of the centroid voxel block according to the Euclidean distance of the point cloud position, perform Fourier transform on the spatial domain kernel, and obtain the spatial domain matrix; S2-4: Calculate the range kernel of the central voxel block according to the grayscale similarity using a multiple logarithmic function, perform fast Fourier transform on the range kernel, and obtain the range matrix; S2-5: Multiply the spatial domain matrix and the range matrix to obtain denoised point cloud data; S2-6: The denoised point cloud data and the denoised two-dimensional data are transmitted to the AI ​​design software using a parallel transmission algorithm.

4. The method for making digital dentures according to claim 1, characterized in that: In step S3, the AI ​​design software first uses a data association algorithm to associate the anatomical structure data according to the oral structure to generate an oral structure data table; then uses a three-dimensional reconstruction algorithm to generate a virtual oral model according to the oral structure data table; then uses a Gaussian Laplace algorithm to enhance the edge of the virtual oral model, thereby enhancing the contour traces of the alveolar ridge, hard palate, edentulous jaw and alveolar arch in the virtual oral model, and constructs an intraoral alveolar bone model. Finally, a finite element analysis algorithm is used to perform an opening simulation and a closing simulation of the intraoral alveolar bone model according to the maximum occlusal gap and the normal occlusal gap.

5. The method for making digital dentures according to claim 1, characterized in that: In step S4, the improved PointNet++ algorithm includes a set sampling layer and a feature propagation layer; the set sampling layer uses the farthest point sampling algorithm to sample the patient's intraoral alveolar bone model as the centroid of the local area of ​​the oral cavity; and uses the ball query algorithm or the kNN algorithm to obtain a neighborhood point set in the intraoral alveolar bone model according to the centroid coordinates; then uses a multilayer perceptron to extract local features of the local area of ​​the oral cavity and performs maximum pooling; the feature propagation layer uses an interpolation algorithm to map high-level features in the local features to low-level features, thereby obtaining the global features of the intraoral alveolar bone model.

6. The method for making digital dentures according to claim 5, characterized in that: The set abstraction layer includes a sampling layer, a grouping layer and a PointNet layer; the sampling layer uses an iterative farthest point sampling algorithm to collect a number of sampling points in the alveolar bone model in the patient's mouth as the centroid of the local area of ​​the oral cavity; Among them, P new is a new sampling point, argmax represents the iterative farthest point sampling algorithm, p represents the point to be sampled, P represents the set of points to be sampled in the alveolar bone model in the mouth, q represents the sampled point, P cur is a set of sampled points, ||·|| represents the Euclidean distance function; The grouping layer uses the ball query algorithm to obtain several neighborhood points in the intraoral alveolar bone model according to the centroid coordinates and construct a neighborhood point set; B(p i ,r)={p j ∈P|||p i -p j ||≤r} (2) Among them, B() represents the ball query algorithm, p i is the i-th point to be sampled, i represents the sampling point sequence number, r is the radius of the sphere, B(p i ,r) is p i The neighborhood point set, p j represents the jth point to be sampled; The PointNet layer converts the original coordinates of the sampled point into local coordinates relative to the centroid coordinates: x' i =x i -x c ; where x' i Represents the local coordinates of the point to be sampled, x i Represents the original coordinates of the i-th point to be sampled, x c Represents the centroid coordinates of the local area; The PointNet layer uses a multi-layer perceptron to extract local features of the oral cavity and perform maximum pooling; h(x')=ReLU(W2ReLU(W1x')) (3) Wherein, h() represents the local feature, x' represents the coordinates of the sampling point in the local area of ​​the oral cavity, W1 and W2 are the first and second weight matrices of MLP respectively, and ReLU represents the activation function; f(B)=maxpool x’∈ Bh(x’) (4) Among them: f() represents the maximum pooling feature of the local area, maxpool represents the maximum pooling layer, and B represents the local area B.

7. The method for making digital dentures according to claim 5, characterized in that: The feature propagation layer uses an inverse distance weight function through an interpolation algorithm to map high-level features to low-level features, thereby obtaining global features of the intraoral alveolar bone model; Among them, w i () represents the inverse distance weight value of the i-th neighbor point, x represents the coordinates of the reference point, d() represents the distance function, s represents a constant, s is 2, f j () represents the interpolation value of the jth feature, k represents the total number of neighboring points, Represents the mapping coefficient of the jth feature of the i-th neighbor point.

8. The method for making digital dentures according to claim 1, characterized in that: In step S5, the complete denture includes a base and artificial teeth; the base fits tightly against the oral mucosa, is retained by the adsorption force generated by edge sealing and atmospheric pressure, and is then adsorbed on the upper and lower alveolar ridges in the oral cavity; the base and artificial teeth together restore the patient's facial morphology and function; the fixed denture includes a retainer, artificial teeth and a connector; the retainer is fixed on the natural teeth, the bridge restores the morphology and function of the missing teeth, and the connector connects the retainer and the artificial teeth.

9. The method for making digital dentures according to claim 8, characterized in that: The step S6 includes the following steps: S6-1: The producer uses 3D printing and cutting equipment to produce the denture model according to the patient's needs, and simultaneously cuts the hard wax tray to combine it into a full denture or fixed denture; S6-2: Quantify the occlusal feedback of base edge position, base fit, facial proportion, condylar position, temporalis muscle contraction, centric occlusion, lateral movement and protrusive movement after the patient wears complete denture or fixed denture; S6-3: The designer modifies the denture model based on the quantitative data, and then obtains the final denture version data of the denture model.

10. The method for making digital dentures according to claim 1, characterized in that: The step S8 includes the following steps: S8-1: The producer completes the denture arrangement based on the final denture guide using digital technology or traditional technology; S8-2: The producer uses the traditional boxing process and silicone rubber or plaster embedding process to fix the denture shape; S8-3: Boil wax and flush wax on the preliminarily denture model that has been boxed and embedded, and inject the mixed denture base polymer into the base model for pressurized filling to complete the base production; S8-4: Grind, polish and clean the denture after the base and denture installation, and adjust the comfort and aesthetics of the denture; S8-5: Casting, milling, and laser sintering of the combined denture were performed to obtain the final denture model.

Images