Abstract
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.
Abstract (translated)
目标：克服在全景片上诊断牙周冠炎（pericoronitis）的挑战，开发了一种结合解剖定位、病理分类和解释能力的人工智能辅助评估系统。 方法：实施了一个两阶段的深度学习管道。第一阶段使用YOLOv8来检测第三磨牙，并根据Winter's分类法对其解剖位置和倾斜角度进行分类。然后将检测到的区域输入第二阶段分类器，即修改后的ResNet-50架构，以识别提示牙周冠炎的放射学特征。为了增强临床信任度，在该系统中使用了Grad-CAM来突出显示全景片上的关键诊断区域。 结果：YOLOv8组件实现了92%的精度和92.5%的平均精度（mean average precision）。ResNet-50分类器在正常病例中的F1得分为88%，牙周冠炎病例中为86%。放射科医生报告称，Grad-CAM与他们的诊断印象之间的一致性达到了84%，这支持了该系统的影像学相关性和解释能力的输出。 结论：该系统展示了人工智能辅助全景片评估的强大潜力，并且其可解释的人工智能特性有助于提高临床信心。
URL
https://arxiv.org/abs/2601.08401
