3D Dental Model Segmentation Quality Assessment
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Solution Overview
Problem
Current orthodontic treatments face challenges in accurately and efficiently reviewing segmented 3D dental models, leading to potential errors in treatment plans due to manual review requirements, which increase time and costs.
Innovation Solution
Implementing machine learning classifier engines trained on trimming plane errors, scan artifacts, extra teeth, and missing teeth to automatically classify and correct segmented 3D dental models, enabling rapid and accurate review for treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and correction of segmented 3D dental models is performed by dedicated personnel, then segmentation accuracy is improved, but time consumption and expense increase
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated machine learning-based review system. The ML model automatically detects segmentation errors and generates corrections, substituting human reviewers with an automated computational system that maintains high accuracy while dramatically reducing review time and costs.
Solution Approach 2:
The segmented 3D dental model performs self-review through the automated ML-based system. The model independently identifies its own segmentation errors and generates corrections without requiring external human intervention, enabling the system to self-validate and self-correct segmentation results.
2Measurement precision
If manual review and correction of segmented 3D dental models is performed by dedicated personnel, then segmentation quality is improved, but treatment plan development expense increases
Solution Approach 1:
The patent replaces expensive manual review processes with an automated machine learning system. This substitution eliminates the need to pay dedicated personnel for review and correction work, significantly reducing treatment plan development expenses while maintaining or improving segmentation quality through consistent automated evaluation.
Solution Approach 2:
The patent changes the operational parameters of the review process by transitioning from human-based review to automated ML-based review. This parameter change fundamentally alters the cost structure, replacing labor-intensive processes with computationally-efficient automated systems that reduce overall expense while maintaining quality standards.
3Productivity
If automated segmentation is performed without manual review, then time and expense are reduced, but segmentation accuracy deteriorates
Solution Approach 1:
The patent replaces manual review mechanics with automated ML-based review mechanics. The ML model automatically detects segmentation errors and generates corrections, providing a computational review system that maintains high accuracy without requiring human intervention, thus preserving both speed and precision.
Solution Approach 2:
The patent implements feedback mechanisms where the ML model continuously learns from segmentation results and corrections. The system uses feedback from detected errors and generated corrections to improve its segmentation and review capabilities over time, maintaining high accuracy through iterative learning while preserving automated processing speed.
Data Source
AI summary
Provided herein are apparatuses (e.g., systems) and methods for automatically assessing a segmented 3D dental model. The segmented 3D dental model may be classified by a trained machine learning classifier (a trained neural network). In some cases, the segmented 3D dental model may be classified as a passing (acceptable) or failing (unacceptable) model. The machine learning classifier may be trained with labeled 3D dental models that illustrate passing and failing segmented 3D dental models.