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Published on: February 23, 2024
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Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study.
Ali Altındağ1, Serkan Bahrilli1, Özer Çelik2
1Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey.
Summary
This study shows deep learning AI accurately identifies and counts permanent teeth in dental X-rays. This technology can improve dental practice efficiency and effectiveness.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Imaging Analysis
- Deep Learning Applications
Background:
- Automated tooth identification in dental radiographs is crucial for accurate diagnosis and treatment planning.
- Traditional methods can be time-consuming and prone to human error.
- Deep learning offers a promising approach for enhancing the accuracy and efficiency of dental image analysis.
Purpose of the Study:
- To evaluate the effectiveness of a deep learning model for automated identification and enumeration of permanent teeth in bitewing radiographs.
- To assess the performance of the Yolov5 architecture in this specific dental imaging task.
Main Methods:
- A dataset of 1248 bitewing radiographs was curated and annotated using the CranioCatch labeling program.
- The dataset was divided into training (80%), validation (10%), and testing (10%) sets.
- A 3x3 clash operation was applied to enhance labeled regions, and the Yolov5 architecture was employed for model development.
Main Results:
- The artificial intelligence model achieved high performance metrics on the test dataset.
- F1 score: 0.9913
- Sensitivity: 0.9954
- Precision: 0.9873
Conclusions:
- Deep learning-based AI demonstrates significant efficacy in the numerical identification of teeth from bitewing radiographs.
- AI-augmented clinical decision support systems have the potential to improve the efficiency and effectiveness of dental practitioners.
- This technology can streamline dental workflows and support diagnostic accuracy.
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