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.

翻译：目的：为克服全景片上诊断牙周炎的挑战，开发一种融合解剖定位、病理分类与可解释性的人工智能辅助评估系统。方法：实现了一种双阶段深度学习流程。第一阶段采用YOLOv8检测第三磨牙，并依据Winter分类法对其解剖位置与倾斜角度进行分类。检测区域随后输入第二阶段分类器——一种改进的ResNet-50架构，用于识别提示牙周炎的影像学特征。为增强临床可信度，采用Grad-CAM在全景片上突出显示关键诊断区域。结果：YOLOv8模块实现了92%的精确率与92.5%的平均平均精度。ResNet-50分类器对正常病例与牙周炎病例的F1分数分别达到88%与86%。放射科医师报告Grad-CAM结果与其诊断印象的一致性达84%，证实了可解释性输出的影像学相关性。结论：本系统展现了人工智能辅助全景片评估的强大潜力，其可解释人工智能特征有助于提升临床诊断信心。