AI models are essential for dental implant clinical accuracy to eliminate human diagnostic variability, ensure consistent brand identification across diverse implant systems, and enable real-time surgical planning with precision exceeding specialist performance in resource-constrained clinical settings so the aim of our study was to develop AI model and compare its accuracy by evaluated YOLO architectures (v7, v10m, v11, v12x) for detecting Adin, Osstem, Straumann, i-Fix implants using 3,500 periapical radiographs (1,500 in vitro cadaveric models + 2,000 in vivo clinical cases). The Images were manually annotated by 2 prosthodontists (ICC=0.95) in YOLO format, split 70/15/15, trained on RTX 4060 GPU with 300 epochs, AdamW optimizer, and 5-fold cross-validation. We have found that, YOLOv12x achieved 97% in vitro and 93% in vivo accuracy (0.94/0.89 mAP@50-95) for Adin/Osstem/Straumann/i-Fix implant detection, significantly outperforming 2 prosthodontists (85.2%) and 2 general dentists (78.4%) with 5-7ms inference speed (p<0.001). Thus, YOLOv12x can establishes superior clinical accuracy.
Keywords: YOLOv12x, Adin, Osstem, Straumann, i-Fix, cadaveric models.
How to cite this article: Dordi J, Tuvar A, Siddiqui A, Ahmed G, Agrawal S, Karkera R, Develop & Compare AI Model For Dental Implant Clinical Accuracy: An In Vitro-Invivo Study. Int J Drug Deliv Technol. 2026;16(2s): 266-270; DOI: 10.25258/ijddt.16.266-270