AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns

TL;DR

This study introduces an AI-based deep learning framework that automatically detects and quantifies alveolar bone loss and its patterns in dental radiographs, aiming to improve diagnosis and treatment planning for periodontitis.

Contribution

The paper presents a novel AI system combining YOLOv8 and Keypoint R-CNN models for precise detection and classification of alveolar bone loss in radiographs.

Findings

Achieved high accuracy in detecting bone loss severity with ICC up to 0.80.

Classified bone loss patterns with 87% accuracy.

Demonstrated rapid, objective, and reproducible periodontal assessment.

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.