Alveolar Bone Loss Detection and Localization in Dental X-Ray Images using YOLOv5

Periodontal disease, characterized by alveolar bone loss, is a prevalent oral health condition that requires early detection and management to prevent further progression. This paper proposes novel approach for loss localization in dental X-ray images using the YOLOv5 object algorithm. We annotated dataset of radiographs with regions fine-tuned model on this dataset. Our achieved high accuracy robustness detecting localizing regions, precision, recall, F1 score exceeding 90%. The real-time processing capabilities make it suitable clinical implementation, providing an efficient accurate solution periodontal disease management. automated can significantly assist dentists diagnosis treatment planning diseases, leading improved patient outcomes reduced risks tooth loss. proposed method has potential be integrated into practice, valuable tool practitioners field periodontics.