AI自动分析牙片，精准识别牙槽骨流失程度与类型
AI-assisted radiographic analysis in detecting alveolar bone-loss severity and patterns
- 用YOLOv8和关键点检测定位牙齿与解剖标志点
- 骨流失程度评估一致性达0.80，分类准确率87%
- 适合牙科医生快速辅助诊断，提升诊疗客观性
牙周炎是一种导致牙槽骨丧失的慢性炎症性疾病，严重影响口腔健康与生活质量。准确评估骨流失程度与模式对诊断与治疗规划至关重要。本研究提出一种基于深度学习的新型AI框架，利用口内牙片（IOPA）自动检测并量化牙槽骨流失及其模式。方法结合YOLOv8进行牙齿检测与关键点R-CNN识别解剖标志点，实现骨流失程度的精确计算；同时采用YOLOv8x-seg模型分割骨水平与牙体掩码，通过几何分析判断骨流失模式（水平型或角形）。在1000张经专家标注的大规模数据集上评估，该方法在骨流失程度检测中达到最高0.80的组内相关系数，在骨流失模式分类中准确率达87%。该自动化系统提供快速、客观且可重复的牙周评估工具，减少对主观人工评估的依赖。通过将AI融入牙科影像分析，本框架有望提升牙周炎的早期诊断与个性化治疗规划，改善患者护理与临床结局。
原文摘要 · Abstract (English)
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.
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