Artificial Intelligence and Dental Professionals’ Performance in the Identification of Dental Implant Systems: The Impact of Deep Learning Algorithms – A Diagnostic Accuracy Study

Background : This study aimed to compare the diagnostic performance of two deep learning models (YOLOv8 and YOLOv10) in identifying dental implant systems (DISs) on panoramic radiographs and to assess the impact of AI assistance on the diagnostic accuracy of dental professionals. Methods: Panoramic radiographs of patients who underwent implant treatment at Aydın Adnan Menderes University Faculty of Dentistry between January 2014 and April 2024 were retrospectively screened. A total of 380 radiographs, containing 1,143 implant images representing five DISs (NucleOSS T4, NucleOSS T6, Dentium Superline, Nobel Replace Tapered, and NTA Regular) were included. Images were annotated using Roboflow Annotate and underwent preprocessing and data augmentation. The final dataset comprised 810 images split into 80% training, 10% validation, and 10% test. YOLOv8 and YOLOv10 object detection algorithms were fine-tuned for DIS identification. Performance was assessed using confusion matrices, precision, recall, F1 score, and mean Average Precision (mAP). Twelve dental professionals with at least one year of implantology experience were included. Multiple-choice forms, with and without AI assistance, were completed by participants. Precision, recall, F1 score, and true positive (TP) rates were calculated to compare performance. Statistical analyses included the Shapiro–Wilk and Wilcoxon signed-rank tests. Results : Precision, recall, F1 score, and mAP for YOLOv8 were 0.94, 0.94, 0.94, and 0.96, respectively, while YOLOv10 achieved 0.94, 0.95, 0.95, and 0.97. Although overall accuracy was similar, YOLOv10 showed a narrower range of variation. Except for the recall of NucleOSS T4, AI significantly improved all metrics across all DIS groups (p<0.05). The overall diagnostic performance of participants was lower than that of the AI models. Conclusions: Deep learning-based AI models showed high accuracy in classifying dental implant systems on panoramic radiographs, with YOLOv10 outperforming YOLOv8 in consistency. AI assistance improved participants’ diagnostic performance across all implant categories but did not exceed the standalone AI model, underscoring the impact of human–AI interaction and automation bias. Future work should focus on dataset diversity, multi-modal imaging, and clinician training to optimize AI integration in clinical practice. Trial registration: not applicable.