3D Fixture Model Validation for Digital Aligner Labeling
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Solution Overview
Problem
Existing dental and orthodontic appliance fabrication systems face challenges in accuracy and efficiency due to the loss of information during 2D to 3D conversion, leading to conflicting labels and increased complexity, which can be addressed by direct labeling of 3D mesh elements without intermediate 2D images.
Innovation Solution
The system employs direct labeling of 3D mesh elements using machine learning techniques, such as MeshCNN and U-Net, to improve accuracy and efficiency by avoiding the computational overhead of 2D mapping, and incorporates federated learning and contrastive learning to enhance model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D to 3D conversion is performed using projection operations, then the system can process dental images and generate 3D models, but loss of information occurs and mapping accuracy deteriorates
Solution Approach 1:
The patent extracts and processes only the essential 3D mesh elements directly, eliminating the unnecessary intermediate 2D projection step. By working directly with 3D mesh elements and their connectivity, the system preserves complete geometric information while maintaining processing efficiency.
Solution Approach 2:
The patent transitions from 2D image processing to direct 3D mesh processing. Instead of projecting 2D images onto 3D models, the system operates natively in 3D space on mesh elements, vertices, and faces, thereby avoiding information loss inherent in 2D-to-3D conversion.
2Manufacturing precision
If projection operations are used to label 3D mesh elements, then the system can annotate dental structures, but conflicting labels are generated and system complexity increases
Solution Approach 1:
The patent extracts and processes only the essential 3D mesh elements directly, eliminating the unnecessary intermediate 2D projection step. By working directly with 3D mesh elements and their connectivity, the system preserves complete geometric information while maintaining processing efficiency.
Solution Approach 2:
The patent segments the 3D mesh into discrete elements (vertices, edges, faces) and processes each segment independently through machine learning models. This segmentation allows each element to be labeled without interference from other elements, eliminating conflicting labels while maintaining system simplicity.
3Productivity
If 2D image projection is used for data processing, then the system can handle dental images, but computational efficiency deteriorates
Solution Approach 1:
The patent transitions from 2D image processing to direct 3D mesh processing. Instead of projecting 2D images onto 3D models, the system operates natively in 3D space on mesh elements, thereby avoiding information loss inherent in 2D-to-3D conversion.
Solution Approach 2:
The patent extracts and processes only the essential 3D mesh elements directly, eliminating the unnecessary intermediate 2D projection step. By working directly with 3D mesh elements and their connectivity, the system preserves complete geometric information while maintaining processing efficiency.
Data Source
AI summary
Systems and techniques for training one or more machine learning models to automatically validate models of fixtures used in orthodontic alignment treatment are disclosed including assigning one or more labels to the first digital representation of the fixture, wherein the one or more labels specify whether the fixture model is correctly formed, wherein the training is performed based on an automatic comparison between a first digital representation of a fixture and a second digital representation of a fixture.