Orthodontic Bracket Placement Validation on Direct 3D Meshes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dental and orthodontic systems face challenges in accurately converting 2D images to 3D mesh geometry due to loss of accuracy in mapping and inefficient data processing, often requiring additional machine learning models to disambiguate conflicting labels, leading to increased complexity and errors.
Innovation Solution
Direct labeling of 3D mesh elements without intermediate 2D images, utilizing neural networks like MeshCNN, U-Net, and ResNet for segmentation and cleanup, and employing representation learning and federated learning to enhance model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If 2D images are converted to 3D mesh geometry through projection operations, then the process can be automated, but accuracy is lost in the mapping
Solution Approach 1:
The patent transitions from 2D image processing to direct 3D mesh element labeling, operating natively in three-dimensional space. This dimensional change eliminates the projection step that caused accuracy loss, allowing the system to work directly with 3D coordinates and spatial relationships without intermediate 2D representations.
2Device complexity
If projection operations are performed to convert 2D images to 3D mesh, then data processing can be simplified, but conflicting labels are generated requiring additional machine learning models
Solution Approach 1:
The patent extracts and removes the problematic projection operation from the processing pipeline. By eliminating the intermediate 2D representation step, the system avoids generating conflicting labels entirely. The solution takes out the source of the problem (projection) rather than adding more complex models to resolve its consequences.
3Ease of manufacture
If intermediate 2D images are used in the conversion process, then the workflow can be standardized, but computational overhead increases
Solution Approach 1:
The patent establishes a continuous direct processing path from 3D mesh input to labeled 3D mesh output, eliminating the interruptive 2D projection step. This continuous action in 3D space maintains workflow standardization while removing the computational overhead associated with generating and processing intermediate 2D representations.
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 described including generating a prediction of one or more transformations that position the first digital representation of the orthodontic appliance within the first digital representation of the patients teeth, generating a predicted representation for the placement of the orthodontic appliance, generating a loss value that specifies a difference between the one or more predicted representations for the placement of the orthodontic appliance and the one or more reference representations of a placement of the orthodontic appliance that is generated from one or more reference transformations that have been applied to the orthodontic appliance, and modifying the neural network based on the loss value.