Efficacy of deep learning models and dental professionals in identifying dental implants
- Author:
Veena BENAKATTI
1
;
Ramesh P. NAYAKAR
;
Mallikarjun ANANDHALLI
;
Rohit C. SUKHASARE
Author Information
- Publication Type:Original Articles
- From:Imaging Science in Dentistry 2025;55(4):351-360
- CountryRepublic of Korea
- Language:English
-
Abstract:
Purpose:Implant identification is a pressing concern in dental implantology, and artificial intelligence (AI) has been evaluated for this purpose. YOLO, a state-of-the-art object detection model, is suitable for medical imaging; therefore, this study assessed YOLOv11-the latest iteration-for identifying 10 implant types in Indian clinical settings and compared its accuracy to that of dental professionals.
Materials and Methods:A dataset of 3,161 radiographs, comprising both periapical and panoramic images of 10implant types, was annotated and used to train and test YOLOv11. Training was performed on Google Colab using an NVIDIA Tesla T4 GPU (16 GB VRAM). A random sample of 200 radiographs was selected from the test dataset andpresented to 50 dental practitioners for implant identification. Their responses were analysed and compared, using the chi-square test for statistical significance.
Results:YOLOv11 achieved precision of 0.87, recall of 0.85, an F1-score of 0.86, and an mAP50 of 0.899. The model achieved excellent classification accuracy for Adin (95%), MIS (94%), Bego (92%), ITI (96%), and Bicon (97%).Moderate accuracy was noted for Noris (82%), Osstem (85%), AlphaBio (88%), Dentium (77%), and Bioline (75%).YOLOv11 demonstrated higher overall accuracy and consistency than dental professionals. Dentists’ accuracy ranged from 27% to 49%, whereas that of YOLOv11 ranged from 92% to 100%.
Conclusion:YOLOv11 recognised most implant classes with over 90% accuracy, surpassing traditional manualtechniques in implant detection. Although the model is dependable and efficient, certain aspects require improvement.The study also emphasises the significance of a region-specific approach for clinical relevance.
