Orthodontic Appliance Validation via Direct 3D Mesh Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dental and orthodontic appliance fabrication systems face accuracy issues due to the loss of precision in converting 2D images to 3D meshes, leading to conflicting labels and inefficient processing, which complicates the mapping and requires additional machine learning models to resolve.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D images, using neural networks like MeshCNN, ResNet, and U-Net for segmentation and cleanup, and employing representation learning to improve accuracy and efficiency in dental appliance fabrication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If projection operations are performed to map 2D images to 3D mesh elements, then the system can process dental images, but the mapping accuracy is lost and conflicting labels are generated
Solution Approach 1:
The patent extracts and eliminates the intermediate projection step from the traditional 2D-to-3D mapping pipeline. Instead of projecting 2D image labels onto 3D mesh elements, the system directly labels 3D mesh elements using a 3D convolutional neural network that processes 3D point cloud data, thereby removing the source of mapping inaccuracies and conflicting labels
Solution Approach 2:
The patent transitions from 2D image processing to 3D direct labeling by using a 3D convolutional neural network that operates on 3D point cloud representations of dental structures. This dimensional shift allows the system to process spatial information directly in 3D space, eliminating the need for problematic 2D-to-3D projection operations
2Measurement precision
If additional machine learning models are added to disambiguate conflicting labels, then label accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The patent removes the need for additional disambiguation models by eliminating the projection operation that creates conflicting labels in the first place. The direct 3D labeling approach produces unambiguous labels directly, avoiding the need for subsequent resolution steps
Solution Approach 2:
The patent segments the labeling task into direct 3D mesh element labeling using a specialized 3D convolutional neural network, rather than using a multi-step process involving 2D projection and subsequent disambiguation. This segmentation approach simplifies the overall system architecture while maintaining accuracy
3Loss of information
If 2D to 3D conversion is performed through projection, then the system can utilize existing image data, but processing efficiency decreases
Solution Approach 1:
The patent processes dental data directly in 3D space using a 3D convolutional neural network that operates on point cloud representations, eliminating the need for time-consuming 2D-to-3D projection operations and significantly improving processing efficiency while maintaining data utilization
Data Source
AI summary
Systems and techniques for training one or more neural networks to automatically determine placement of a digital representation of an orthodontic appliance are disclosed including comparing one or more aspects of the second representation of a 3D printed part with one or more respective aspects of a first representation of the 3D printed part, generating a reconstruction error based on the comparing, and when the reconstruction error is greater than a predetermined threshold, assigning one or more result labels that specify that the respective aspects of the 3D printed part were not correctly fabricated and when the reconstruction error is less than the predetermined threshold, assigning one or more result labels that specify that the respective aspects of the 3D printed part were correctly fabricated.