3D Replacement Tooth Modeling Using Neural Network Tooth Context
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Solution Overview
Problem
Challenging to accurately model a replacement tooth that matches the subject's existing teeth, considering geometric and aesthetic features, for dental restorations like crowns.
Innovation Solution
Utilizing a tooth model generating neural network with an encoder and decoder portion, trained on three-dimensional digital tooth models, to generate a subject-specific three-dimensional digital model of a replacement tooth, incorporating features from adjacent, antagonist, and contralateral teeth, and applying surface modifications to achieve a realistic appearance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a skilled dental technician manually sculpts the crown to match existing teeth, then the geometric and aesthetic matching is improved, but the time consumption and labor complexity increase
Solution Approach 1:
The patent replaces the manual mechanical sculpting process with an automated neural network system. The neural network receives three-dimensional digital tooth models as input and generates replacement tooth models automatically, eliminating the need for manual sculpting while maintaining high geometric and aesthetic matching precision.
Solution Approach 2:
The neural network is trained on extensive datasets of three-dimensional digital tooth models to learn and replicate the geometric and aesthetic features of natural teeth. The system copies and adapts these learned patterns to generate accurate replacement tooth models that match the subject's existing teeth.
2Manufacturing precision
If three-dimensional digital tooth models are used as input for neural network processing, then the manufacturing precision of replacement teeth is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The neural network is divided into distinct functional components: an encoder portion that processes the input three-dimensional digital tooth models and extracts features, and a decoder portion that generates the replacement tooth model. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The encoder acts as an intermediary between the input tooth models and the output replacement models. It processes the complex three-dimensional data and transforms it into a simplified representation that the decoder can efficiently process to generate the final output, reducing the computational burden.
3Manufacturing precision
If features from multiple teeth (adjacent, antagonist, contralateral) are incorporated into the model generation, then the realism and accuracy of the replacement tooth is improved, but the data processing complexity increases
Solution Approach 1:
The neural network merges and integrates features from multiple tooth models (adjacent, antagonist, and contralateral teeth) into a unified representation. The encoder combines these multiple input sources and synthesizes their characteristics to generate a realistic replacement tooth model that incorporates the contextual information from surrounding teeth.
Data Source
AI summary
Disclosed herein is a dental method that comprises receiving a selection of a replacement tooth for a subject. The method further comprises receiving one or more three-dimensional digital tooth models descriptive of one or more teeth of the subject. The method further comprises receiving a generated three-dimensional digital model of the replacement tooth in response to inputting the one or more three-dimensional digital tooth models descriptive of one or more teeth of the subject into a tooth model generating neural network. The tooth model generating neural network comprises an encoder portion and a decoder portion. The encoder portion is configured for outputting a collective feature vector descriptive of the one or more digital tooth models. The tooth model generating neural network further comprises at least one fully connected layer configured to output a latent space vector into the decoder portion in response to receiving the collective feature vector.