Akademik Veri Yönetim Sistemi
Journal of Medical and Biological Engineering, cilt.45, sa.4, ss.436-446, 2025 (SCI-Expanded, Scopus)
Aim: This study aims to employ the YOLO algorithm for the automatic classification of mandibular condylar morphology in panoramic radiographs. Materials and Methods: A total of 1,056 panoramic radiographs, containing 2,112 healthy mandibular condyles, were used in the study. The dataset was split into training (~80%), validation (~10%), and test (~10%) sets. Two experienced dentomaxillofacial radiologists annotated the training images and classified the condyles into four morphological categories: Round, Angled, Diamond, and Crooked Finger-shaped. The YOLOv8 deep learning model was trained using transfer learning, hyperparameter tuning, and fine-tuning techniques. Performance was assessed using metrics including precision, recall (sensitivity), F1-score, mean Average Precision (mAP), and training time. True positives, false positives, and false negatives were evaluated based on bounding box localization and class assignments. Results: The model demonstrated balanced performance across classes in the training dataset. On the test dataset, the model achieved an overall F1-score of 0.769 and mAP@0.5 of 0.786. The highest performance was observed for the Crooked Finger class (0.795 precision, 0.870 recall, 0.831 F1-score, 0.837 mAP@0.5) and the Angled class (0.723 precision, 0.860 recall, 0.786 F1-score, 0.808 mAP@0.5). The Round class showed moderate results with 0.677 precision, 0.870 recall, 0.761 F1-score, and 0.798 mAP@0.5. The Diamond class had the lowest performance, with 0.528 precision, 0.696 recall, 0.600 F1-score, and 0.661 mAP@0.5. Conclusion: The model effectively distinguishes the Angled and Crooked Finger classes but faces challenges with the Diamond and Round classes. Despite varied performance, the model demonstrates balanced performance overall, providing a foundation for further refinement and optimization.