Neural Network Generation of 3D Replacement Tooth Models
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Solution Overview
Problem
Challenging to accurately model a replacement tooth that matches the subject's existing teeth in terms of shape and aesthetics.
Innovation Solution
Utilizing a tooth model generating neural network with an encoder and decoder portion, combined with fully connected layers, to generate a subject-specific three-dimensional digital model of a replacement tooth based on input tooth models from the subject.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a skilled dental technician sculpts the crown to match existing teeth, then the shape and aesthetics of the replacement tooth are improved, but the time and complexity of the process increase
Solution Approach 1:
The system performs preliminary actions by capturing three-dimensional digital models of existing teeth before the restoration process begins. These digital models are stored and used as reference data to automatically generate the replacement tooth geometry, eliminating the need for time-consuming manual sculpting while maintaining shape accuracy.
Solution Approach 2:
The patent replaces the mechanical sculpting process performed by dental technicians with an automated computer-based system. The system uses digital model processing and algorithmic generation to create the replacement tooth geometry, substituting manual mechanical work with automated computational methods that are both faster and more precise.
2Manufacturing precision
If manual sculpting is used to match existing teeth, then the aesthetic quality is improved, but the reliability and consistency of results decrease due to human variability
Solution Approach 1:
The system replaces the variable human sculpting process with a standardized computer-based algorithm. The same digital model processing and geometric generation methods are applied consistently across all cases, eliminating human variability and ensuring reliable, repeatable results while maintaining high aesthetic quality through precise digital control.
Solution Approach 2:
The system transforms the physical sculpting process into digital parameter-based generation. By working with three-dimensional digital models and controlling tooth geometry through digital parameters and algorithms, the system achieves consistent, reliable results that are not subject to human variability while maintaining or improving aesthetic quality.
3Device complexity
If traditional modeling methods are used, then the process is simpler in terms of technology, but the measurement precision and detail accuracy decrease
Solution Approach 1:
The system replaces traditional physical modeling methods with three-dimensional digital scanning and modeling. This substitution enables high-precision capture of existing tooth geometry and accurate generation of replacement tooth models, achieving superior measurement precision despite the increased technological complexity of the digital system.
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.