用AI自动分析全景牙片，识别智齿位置与发炎迹象，还能解释判断依据。
An Explainable Two Stage Deep Learning Framework for Pericoronitis Assessment in Panoramic Radiographs Using YOLOv8 and ResNet-50
- 两阶段模型：先定位智齿，再判断是否发炎。
- 发炎诊断准确率86%，正常情况识别率达88%。
- 可视化热图帮助医生理解AI判断，提升临床信任度。
目的：为克服全景牙片中智齿冠周炎诊断的挑战，构建一个融合解剖定位、病理分类与可解释性的AI辅助评估系统。方法：采用两阶段深度学习流程。第一阶段使用YOLOv8检测智齿并基于Winter分类法判别其解剖位置与角度；检测区域输入第二阶段分类器（改进的ResNet-50），识别提示冠周炎的放射影像特征。为增强临床可信度，引入Grad-CAM生成关键诊断区域热图。结果：YOLOv8组件实现92%精确率与92.5%平均精度（mAP）。ResNet-50分类器在正常病例上获得88% F1分数，在冠周炎病例上达86%。放射科医生评估显示，Grad-CAM热点与诊断意见一致率达84%，证实可解释性输出具有放射学相关性。结论：该系统在全景牙片的AI辅助评估中展现出良好潜力，可解释性设计有助于提升临床信心。
原文摘要 · Abstract (English)
Objectives: To overcome challenges in diagnosing pericoronitis on panoramic radiographs, an AI-assisted assessment system integrating anatomical localization, pathological classification, and interpretability. Methods: A two-stage deep learning pipeline was implemented. The first stage used YOLOv8 to detect third molars and classify their anatomical positions and angulations based on Winter's classification. Detected regions were then fed into a second-stage classifier, a modified ResNet-50 architecture, for detecting radiographic features suggestive of pericoronitis. To enhance clinical trust, Grad-CAM was used to highlight key diagnostic regions on the radiographs. Results: The YOLOv8 component achieved 92% precision and 92.5% mean average precision. The ResNet-50 classifier yielded F1-scores of 88% for normal cases and 86% for pericoronitis. Radiologists reported 84% alignment between Grad-CAM and their diagnostic impressions, supporting the radiographic relevance of the interpretability output. Conclusion: The system shows strong potential for AI-assisted panoramic assessment, with explainable AI features that support clinical confidence.
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