TR
EN
Detection of Periapical Lesions on Panoramic Radiographs Using Artificial Intelligence
Abstract
Objective: This study aimed to compare the performance of two deep learning models, YOLOv8 and Cascade Mask R-CNN, for the artificial intelligence-assisted automatic detection of periapical lesions in dental panoramic radiographs.
Material and Methods: A total of 1861 panoramic radiographs were reviewed, and 602 images containing periapical lesions were included in the study. In these radiographs, a total of 1059 lesions were identified. Observer agreement for lesion detection was evaluated using Kappa analysis. The included radiographs were divided into three datasets: training (70%), validation (20%), and testing (10%). The YOLOv8 and Cascade Mask R-CNN models were trained using the PyTorch library. The trained models were then applied to the test dataset, and true positive, false positive, true negative, and false negative outcomes were recorded for periapical lesion detection. Based on these outcomes, accuracy, sensitivity, specificity, precision, and F1 score were calculated for each model.
Results: On the test dataset, the YOLOv8 model achieved 96.9% accuracy, 53.5% sensitivity, 90% precision, and an F1 score of 0.671, whereas the Cascade Mask R-CNN model achieved 97.6% accuracy, 76.2% sensitivity, 81.9% precision, and an F1 score of 0.790. Although YOLOv8 produced fewer false positives, it failed to detect a considerable number of lesions. In contrast, Cascade Mask R-CNN was able to detect more lesions but generated relatively more false positives.
Conclusion: Artificial intelligence models demonstrated potential as supportive tools in clinical decision support systems based on their accuracy rates. However, the current sensitivity levels were insufficient to reliably undertake diagnostic responsibilities alone, suggesting that these models should not yet be used independently for clinical decision-making.
Keywords
Supporting Institution
This study was supported by the KOSGEB (Approval no: 7AHNN).
Ethical Statement
This study was conducted with the approval of the Ethics Committee of Karamanoğlu Mehmetbey University Faculty of Medicine (Approval No: 09-2024/01)
References
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Details
Primary Language
English
Subjects
Oral and Maxillofacial Radiology , Endodontics
Journal Section
Research Article
Publication Date
December 29, 2025
Submission Date
May 28, 2025
Acceptance Date
July 11, 2025
Published in Issue
Year 2025 Volume: 28 Number: 4