Neural Network Margin Proposal for Dental 3D Models
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Solution Overview
Problem
In modern dental workflows that omit the plaster cast stage, accurately determining the margin line for dental prostheses from full jaw digital surfaces is challenging, especially in subgingival cases where the margin line is obscured by gum and saliva, requiring manual and time-consuming point-by-point localization.
Innovation Solution
A computer-implemented method using two trained neural networks to propose an automatic margin line in a 3D digital model of a jaw, where the first neural network determines an inner representation of the model and the second neural network generates a margin line proposal based on this representation and a base margin line.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual point-by-point margin localization is used in modern digital workflows, then measurement precision can be maintained, but productivity decreases due to time-consuming manual work
Solution Approach 1:
The patent replaces manual mechanical point-by-point localization with an automated neural network system. The neural network processes 3D digital models and automatically generates margin line proposals, eliminating the need for manual intervention while maintaining detection accuracy through learned patterns from training data.
Solution Approach 2:
The system performs self-service by automatically detecting margin lines without requiring user input or intervention. The neural network independently processes the 3D digital model, determines the preparation tooth, identifies the margin line, and generates proposals autonomously based on the input data.
2Productivity
If fully automatic margin detection is implemented, then productivity improves, but measurement precision deteriorates due to difficulty in handling complex cases like subgingival margins
Solution Approach 1:
The patent implements feedback mechanisms where the system provides margin line proposals that can be reviewed and adjusted. The neural network continues to refine its predictions based on the 3D digital model characteristics, and the feedback loop allows for verification against ground truth data during training to improve precision in challenging cases.
Solution Approach 2:
The system handles different margin types by changing its detection parameters and approach based on the specific case characteristics. For subgingival margins, the neural network adjusts its feature extraction and detection parameters to account for obscured areas, while maintaining automated processing for visible margins.
3Ease of operation
If curvature-based geometrical tools are used for margin localization, then ease of operation improves with simple tools, but adaptability deteriorates for complex tooth shapes and subgingival cases
Solution Approach 1:
The neural network system provides universal functionality by handling multiple margin types (supragingival, subgingival, open, closed) and various tooth shapes within a single unified system. Unlike specialized curvature tools, the neural network can adapt to different geometries through its learned representations, making it universally applicable across diverse dental cases.
Data Source
AI summary
A computer-implemented method/system/instructions of automatic margin line proposal includes receiving a 3D digital model of at least a portion of a jaw, the 3D digital model including a digital preparation tooth; determining, using a first trained neural network, an inner representation of the 3D digital model; and determining, using a second trained neural network, a margin line proposal from a base margin line and the inner representation of the 3D digital model.