AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Periodontitis, a chronic inflammatory disease causing alveolar bone loss,
significantly affects oral health and quality of life. Accurate assessment of
bone loss severity and pattern is critical for diagnosis and treatment
planning. In this study, we propose a novel AI-based deep learning framework to
automatically detect and quantify alveolar bone loss and its patterns using
intraoral periapical (IOPA) radiographs. Our method combines YOLOv8 for tooth
detection with Keypoint R-CNN models to identify anatomical landmarks, enabling
precise calculation of bone loss severity. Additionally, YOLOv8x-seg models
segment bone levels and tooth masks to determine bone loss patterns (horizontal
vs. angular) via geometric analysis. Evaluated on a large, expertly annotated
dataset of 1000 radiographs, our approach achieved high accuracy in detecting
bone loss severity (intra-class correlation coefficient up to 0.80) and bone
loss pattern classification (accuracy 87%). This automated system offers a
rapid, objective, and reproducible tool for periodontal assessment, reducing
reliance on subjective manual evaluation. By integrating AI into dental
radiographic analysis, our framework has the potential to improve early
diagnosis and personalized treatment planning for periodontitis, ultimately
enhancing patient care and clinical outcomes.
