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Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically
Abdul Rehman El Bsat1, Elie Shammas1, Daniel Asmar1
1Department of Mechanical Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut P.O. Box 11-0236, Lebanon.
Diagnostics (Basel, Switzerland)
|June 13, 2025
Summary
This study introduces a novel 3D segmentation method for dental models using Convolutional Neural Networks (CNNs). It accurately measures tooth movement after orthodontic treatment by using palatal rugae as stable references.
Area of Science:
- Computer-aided design
- Medical imaging
- Orthodontics
Background:
- 3D dental model segmentation is crucial for orthodontic computer-aided design.
- Traditional methods struggle with missing or misaligned teeth.
- A robust segmentation method is needed for accurate orthodontic analysis.
Purpose of the Study:
- To semantically segment maxillary teeth and palatal rugae in 3D scans using Convolutional Neural Networks (CNNs).
- To assess tooth movement post-orthodontic treatment using stable palatal rugae references.
Main Methods:
- Developed a 3D segmentation method by converting 3D scans to 2D images for segmentation, then back-projecting to 3D.
- Utilized a dataset of 100 patient scans manually segmented by experts.
- Aligned pre- and post-treatment scans using palatal rugae as a stable reference frame.
Main Results:
- Achieved 98.69% accuracy and 84.5% Intersection over Union (IoU) for 3D segmentation.
- Enabled computation of 3D translational and rotational tooth movements.
- No pre- or post-processing of data was required.
Conclusions:
- The proposed method successfully measures tooth motion using rugae as a stable reference.
- Provides accurate rotational and translational measurements of maxillary teeth.
- Offers a robust solution for analyzing orthodontic treatment outcomes.
