Technology Category
Computer Vision: SegmentationMachine Learning: Evaluation and AnalysisKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
🤖 AI Summary
Current assessment of alveolar bone loss in periodontitis suffers from subjectivity and low efficiency. To address this, we propose an AI-driven automated X-ray analysis framework. Our method integrates YOLOv8 for tooth detection, Keypoint R-CNN for precise localization of the alveolar crest and cementoenamel junction, and YOLOv8x-seg for bone-level segmentation, coupled with a novel geometric analysis module that quantitatively differentiates horizontal from angular bone defects. Evaluated on 1,000 high-quality annotated radiographs, the framework achieves an intraclass correlation coefficient (ICC) of 0.80 for severity grading and 87% accuracy in defect pattern classification. To our knowledge, this is the first approach to enable dual-dimensional, fully automated quantification—simultaneously assessing both severity and morphological pattern—of alveolar bone defects. The framework significantly enhances objectivity, reproducibility, and clinical applicability in periodontal diagnosis.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Automated detection of alveolar bone-loss severity using AI
Classification of bone-loss patterns via geometric analysis
Improving periodontitis diagnosis with deep learning models
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-based deep learning for bone loss detection
YOLOv8 and Keypoint R-CNN for landmark identification
Geometric analysis for bone loss pattern classification
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