Automated detection of marginal bone loss levels in implant brands using deep learning on periapical radiographs.

BACKGROUND: In contemporary dental practice, implants are the standard solution for edentulism. However, the wide variety of implant brands and the prevalence of peri-implantitis present significant diagnostic hurdles for clinicians. This study evaluated an automated hybrid AI framework designed to simultaneously identify implant brands, determine clinical treatment stages, and classify peri-implant bone loss severity using periapical radiographs, aiming to address the efficiency limitations of existing single-function AI models. METHODS: A dataset comprising 708 periapical radiographs with 3i and Xive implants was utilized. We employed a YOLOv8 model to localize implants and exclude background noise precisely. Subsequently, a custom implant segmentation algorithm and an automated alveolar crest detection method based on two-stage clustering were applied. EfficientNet-B3 served as the backbone for a multi-task classification of 12 composite classes, integrating implant brand, exposure status, and bone loss status. RESULTS: The YOLOv8 model demonstrated exceptional performance with 99.39% precision and 98.63% sensitivity. In the complex 12-class classification, the system achieved an overall accuracy of 97.42%, with specific categories such as Xive/Prothesis/Diseased achieving 98.28%. Clinical feasibility tests revealed the framework significantly outperformed manual expert evaluation, drastically reducing average assessment time from 15.5 to 0.16 s while elevating diagnostic accuracy from 90.73% to 97.38%. CONCLUSION: The proposed hybrid AI framework successfully consolidates brand identification, staging, and bone loss assessment into a unified, efficient workflow. By offering superior accuracy and speed, it serves as a reliable second opinion to support clinical decision-making and improve diagnostic consistency in dentistry.