Abstract
This research primarily focuses on identifying implant brands, specifically Bicon, Bego and ITI, and detecting their locations on the periapical and panoramic X-ray images using YOLO (You Only Look Once) object detection algorithm. A dataset of 2290 annotated images sourced from Roboflow.com was utilized. The dataset was augmented by applying 50% probability of horizontal flip, 50% probability of vertical flip, equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise and salt and pepper noise by the Roboflow. Prior to training, the dataset was splitted into three subsets: train of 1893 samples, test of 218 samples and validation of 179 samples. The detection algorithm was trained using multiple YOLOv8, YOLOv9, YOLOv10 and YOLOv11 models and their sub-versions medium and large variants. In order to evaluate the effectiveness of the models, the performance metrics including precision, recall, F1 score, and mAP@50 (mean Average Precision at Intersection over Union (IoU) threshold 0.50) were analyzed. The highest precision values are 98% for ITI with YOLOv8m, 91.9% for Bicon with YOLOv10m and YOLOv11l, 91.9% for Bego with YOLOv10m and 93.1% for all classes with YOLOv10m. The top-performing recall values were achieved at 94.1% for Bego using YOLOv11l and YOLOv9m, 94.9% for Bicon using YOLOv9m and YOLOv8m, 94.7% for ITI using YOLOv10m and 92.9% for all classes using YOLOv8l. The optimal F1 scores were acquired through 88% for Bego by YOLOv11l, 92.1% for Bicon by YOLOv10m, 96% for ITI by YOLOv11m and %91.4 for all classes by YOLOv10m. YOLO models are effective tools for dental imaging, providing a reliable and efficient approach to automating implant identification. Future studies could improve detection accuracy by utilizing larger and more diverse datasets and exploring other potential applications in dental imaging.