3D Dental Model Segmentation Quality Assessment With Prior Treatment Data
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Solution Overview
Problem
Existing orthodontic treatment plans face challenges in accurately and efficiently segmenting 3D dental models, particularly for patients who have undergone previous dental or orthodontic treatments, leading to aligner fit issues and customer complaints.
Innovation Solution
Implementing automated systems that generate and segment 3D dental models by comparing them with prior treatment data, using engines for initial segmentation, feature extraction, comparison, and model updating to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated segmentation is used for 3D dental models, then processing time is reduced, but segmentation accuracy deteriorates
Solution Approach 1:
The system uses feedback from prior treatment plans to automatically correct and refine segmentation results. The segmentation quality assessment system compares automated segmentation against prior treatment data and automatically adjusts the segmentation to improve accuracy while maintaining processing efficiency.
Solution Approach 2:
The system performs preliminary segmentation automatically, then uses prior treatment information to pre-correct common segmentation errors before final review. This preliminary automated action reduces processing time while the feedback loop ensures accuracy is maintained.
2Measurement precision
If manual review and correction of segmented models is performed, then segmentation accuracy is improved, but treatment time increases
Solution Approach 1:
The segmentation system performs self-correction by automatically comparing its output against prior treatment plans and adjusting segmentation errors without requiring manual intervention. This self-service capability maintains high segmentation accuracy while eliminating the time-consuming manual review process.
Solution Approach 2:
The system continuously receives feedback from prior treatment data and automatically adjusts segmentation results accordingly. This feedback mechanism enables the system to self-correct errors and maintain accuracy without requiring manual review, thereby reducing treatment time.
3Adaptability or versatility
If segmentation is performed without prior treatment data, then adaptability is improved, but manufacturing precision deteriorates
Solution Approach 1:
The segmentation system is designed to handle both new patients and treatment continuation cases universally. It automatically detects whether prior treatment data is available and applies appropriate segmentation strategies, maintaining high precision for treatment continuation while remaining adaptable to new patients through the same unified system.
Data Source
AI summary
Provided herein are apparatuses (e.g., systems) and methods for assisting in generating and segmenting a 3D dental model of a subject's dentition. A 3D dental model may be generated from a dental scan. The apparatuses described herein can determine if the subject has previously undergone a dental or orthodontic treatment, and the 3D dental model can be compared to prior 3D dental models from the previous treatment(s). In some examples, the 3D dental model can be updated or supplemented with data from the prior 3D dental models.