ChatIOS: Improving automatic 3-dimensional tooth segmentation via GPT-4V and multimodal pre-training
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Summary
This summary is machine-generated.The ChatIOS framework enhances 3D tooth segmentation using GPT-4V and multimodal pre-training, improving accuracy and efficiency for dental treatments. This approach pioneers multimodal pre-training for digital dentistry applications.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Digital Dentistry
Background
- Accurate 3D tooth segmentation from intraoral scans (IOSs) is crucial for dental treatments like orthodontics and prosthetics.
- Current deep learning methods require enhancement for precise and efficient segmentation of 3D IOS data.
Purpose Of The Study
- To propose and evaluate the ChatIOS framework, integrating GPT-4V and multimodal pre-training for improved 3D tooth segmentation.
- To enhance deep learning algorithms for 3D tooth segmentation in IOS data.
Main Methods
- Developed the ChatIOS framework using 1800 3D IOS scans from the Teeth3DS dataset.
- Pre-processed 3D IOS data into point clouds and utilized GPT-4V for 2D image descriptions.
- Employed multimodal pre-training with point clouds, 2D images, and text descriptions as input triplets.
Main Results
- ChatIOS significantly outperformed existing benchmarks like PointNet++ on segmentation quality metrics (mIoU, accuracy, DSC).
- Achieved high segmentation accuracy (e.g., 98.0% for maxillary, 97.9% for mandible).
- Demonstrated efficient processing (approx. 2s per scan) and clinical applicability.
Conclusions
- The ChatIOS framework enhances the effectiveness and efficiency of 3D tooth segmentation for clinical dental procedures.
- This study pioneers multimodal pre-training for 3D tooth segmentation and explores GPT-4V applications in digital dentistry.

