Periodontitis bone loss detection in panoramic radiographs using modified YOLOv7

Periodontitis is a common dental disease that results in tooth loss, if not diagnosed and treated in time. However, diagnosing bone loss due to periodontitis from panoramic radiographs is a time-consuming and error-prone process, requiring extensive training and expertise. This work addresses the research gap in automated periodontitis bone loss diagnosis using deep learning techniques. We have proposed a modified version of You Only Look Once (YOLO)v2, called YOLOv7-M, that includes a focus module and a feature fusion module for rapid inference and improved feature extraction ability. The proposed YOLOv7-M model was evaluated on a tooth detection dataset and demonstrated superior performance, achieving an F1-score, precision, recall, and mean average precision (mAP) of 92.5, 91.7, 87.1, and 91.0, respectively. Experimental results indicate that YOLOv7-M outperformed other state-of-the-art object detectors, including YOLOv5 and YOLOv7, in terms of both accuracy and speed. In addition, our comprehensive performance tests show that YOLOv7-M outperforms robust object detectors in terms of various statistical evaluation measures. The proposed method has potential applications in automated periodontitis diagnosis and can assist in the detection and treatment of the disease, eventually enhancing patient outcomes.