Local Coordinate System for Tooth 3D Models via Deep Learning
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Solution Overview
Problem
Current methods for setting local coordinate systems for tooth 3D digital models are inconsistent and labor-intensive, requiring manual effort and resulting in high time and labor costs, with limited optimization capabilities.
Innovation Solution
A computer-implemented method using trained deep learning artificial neural networks, such as Multi-Layer Perceptrons and Octree-Based Convolutional Neural Networks, to automatically determine the local coordinate system for tooth 3D digital models by predicting coordinate axes and employing Principal Component Analysis for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setting method is used, then flexibility in understanding is allowed, but consistency and efficiency deteriorate
Solution Approach 1:
The patent replaces the manual mechanical setting process with an automated computational system using Principal Component Analysis (PCA) and deep learning artificial neural networks. The system automatically determines coordinate axes by analyzing 3D digital models, substituting human manual operations with algorithmic processing that ensures consistent results across different teeth while maintaining the ability to handle various tooth types and configurations.
2Adaptability or versatility
If manual setting method is used, then adaptability to different cases is maintained, but time and labor consumption increase
Solution Approach 1:
The system enables self-service automation where the coordinate system setting process performs itself without manual intervention. The deep learning neural network automatically processes each tooth's 3D digital model, extracts relevant features, and determines the local coordinate system parameters autonomously. This eliminates the need for manual setting while maintaining adaptability to different tooth types through the network's learning capability from training data.
Solution Approach 2:
The patent utilizes parameter changes in the form of deep learning model parameters that are trained on diverse tooth data. By adjusting and optimizing the neural network parameters during training, the system adapts to different tooth morphologies and automatically applies the learned patterns to new cases, achieving both efficiency and adaptability simultaneously.
3Manufacturing precision
If optimization is performed manually, then local optimization is possible, but overall process time increases
Solution Approach 1:
The patent implements continuous automated optimization where the deep learning system continuously processes and optimizes coordinate systems for multiple teeth without interruption. The neural network performs optimization in a continuous pipeline, processing each tooth model through feature extraction, parameter determination, and coordinate system establishment without manual reconfiguration, thereby maintaining optimization quality while dramatically reducing total process time.
Data Source
AI summary
In one aspect of the present application, a computer-implemented method for setting a local coordinate system of a tooth 3D digital model is provided, the method comprises: obtaining a first 3D digital model representing a first tooth, wherein the first 3D digital model is based on a world coordinate system; and setting a local coordinate system for the first 3D digital model using a first artificial neural network based on the first 3D digital model, where the first artificial neural network is a trained deep learning artificial neural network.