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Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs.
André Ferreira Leite1,2, Adriaan Van Gerven3, Holger Willems3
1OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 33, 3000, Leuven, Belgium. andreleite@unb.br.
A new artificial intelligence (AI) tool accurately detects and segments teeth on panoramic radiographs, significantly reducing analysis time compared to manual methods. This AI system offers a faster and more precise approach for dental imaging analysis.
Area of Science:
- Dentistry
- Medical Imaging
- Artificial Intelligence
Background:
- Accurate tooth detection and segmentation are crucial for diagnosing dental conditions.
- Manual analysis of panoramic radiographs is time-consuming and prone to variability.
Purpose of the Study:
- To evaluate the performance of a novel artificial intelligence (AI)-driven tool for automated tooth detection and segmentation on panoramic radiographs.
- To compare the AI tool's accuracy and efficiency against manual segmentation by a dentomaxillofacial radiologist.
Main Methods:
- A dataset of 153 panoramic radiographs was used, with manual segmentation serving as the ground truth.
- The AI tool employed a combination of two deep convolutional neural networks and expert refinement.
- Performance metrics included sensitivity, precision, intersection over union, and time analysis.
Main Results:
- The AI system achieved high accuracy for tooth detection (98.9% sensitivity, 99.6% precision).
- Tooth segmentation performance was excellent, particularly for lower canines (97.5% F1-score).
- The AI tool reduced analysis time by 67% compared to manual segmentation.
Conclusions:
- The AI-driven tool demonstrates highly accurate and efficient performance for tooth detection and segmentation on panoramic radiographs.
- This innovative tool offers a faster and more precise alternative to manual segmentation in clinical practice.
