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Tooth segmentation and dental crowding diagnosis using two-stage dual-dilated graph convolution
Zongsong Han1, Ning Dai2, Zhilei Wu1
1The College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China.
International Journal of Computer Assisted Radiology and Surgery
|October 6, 2025
Summary
This study introduces a two-stage intelligent workflow using dual-dilated graph convolutional networks for automated tooth segmentation and dental crowding diagnosis from 3D intraoral scans, improving efficiency and accuracy in orthodontic analysis.
Area of Science:
- Computer-aided dentistry
- Orthodontic diagnostics
- Artificial intelligence in healthcare
Background:
- Conventional orthodontic model analysis is time-consuming and subjective.
- Automated tooth segmentation and crowding diagnosis are crucial for efficient computer-aided analysis.
- There is a need for intelligent and efficient approaches in orthodontics.
Purpose of the Study:
- To propose a two-stage intelligent workflow for tooth segmentation and dental crowding severity diagnosis.
- To develop an automated system for analyzing 3D intraoral scan models.
- To overcome the limitations of conventional manual methods in orthodontics.
Main Methods:
- A two-stage workflow utilizing innovative dual-dilated graph convolutional networks (DDGCNet1 and DDGCNet2).
- Stage 1: Tooth segmentation using DDGCNet1.
- Stage 2: Point cloud conversion and processing by DDGCNet2 for arch length discrepancy (ALD) measurement, incorporating a novel dual-dilated EdgeConv module.
Main Results:
- The proposed network demonstrated outstanding performance in tooth segmentation.
- Accurate diagnosis of dental crowding severity was achieved.
- Mean absolute error (MAE) for ALD measurement was 1.553 mm (maxilla) and 1.434 mm (mandible).
Conclusions:
- The developed system assists orthodontists in diagnosis and treatment planning.
- It alleviates workload and expedites the creation of reliable orthodontic treatment plans.
- The study meets the growing demands for computer-aided orthodontic diagnosis.
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