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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
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A novel deep learning-based perspective for tooth numbering and caries detection.
Baturalp Ayhan1, Enes Ayan2, Yusuf Bayraktar3
1Department of Restorative Dentistry, Faculty of Dentistry, Kırıkkale University, Kırıkkale, Turkey. baturalpayhan@kku.edu.tr.
Clinical Oral Investigations
|February 27, 2024
Summary
This study demonstrates that deep learning algorithms, specifically convolutional neural networks (CNNs), can accurately detect, number, and identify cavities in dental radiographs. This automated approach shows promise for improving diagnostic efficiency in real-time clinical settings.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Imaging Analysis
- Deep Learning Applications
Background:
- Accurate detection and numbering of teeth are crucial for dental diagnostics and treatment planning.
- Identifying dental caries in early stages is essential for effective intervention.
- Current methods for analyzing bitewing radiographs can be time-consuming and may benefit from automation.
Purpose of the Study:
- To develop and evaluate a deep learning-based system for automated tooth detection and numbering on digital bitewing radiographs.
- To assess the real-time diagnostic efficiency of this system in identifying decayed teeth (caries).
- To enhance clinical workflows and treatment outcomes through AI-driven dental image analysis.
Main Methods:
- A dataset of 1170 anonymized digital bitewing radiographs was utilized, split into training and testing sets.
- A three-stage pipeline was developed, employing a pre-trained convolutional neural network (CNN) for image orientation and YOLOv7 for tooth detection, numbering, and caries identification.
- The intersection over union (IoU) metric was used to match numbered teeth with detected caries.
Main Results:
- The system achieved high performance in teeth detection (recall 0.994, precision 0.987, F1-score 0.99) and teeth numbering (recall 0.974, precision 0.985, F1-score 0.979).
- Caries detection yielded a recall of 0.833, precision of 0.866, and F1-score of 0.822.
- The overall matching performance for teeth numbering and caries detection demonstrated an accuracy of 0.934, recall of 0.834, specificity of 0.961, precision of 0.851, and F1-Score of 0.842.
Conclusions:
- The proposed CNN-based model effectively performs concurrent tooth detection, numbering, and caries detection on bitewing radiographs.
- This AI system offers significant potential to support clinicians by automating key aspects of radiographic analysis.
- The automation capabilities can lead to enhanced diagnostic performance, time savings, and improved efficiency in dental assessments.
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