An Explainable Two Stage Deep Learning Framework for Pericoronitis Assessment in Panoramic Radiographs Using YOLOv8 and ResNet-50

TL;DR

This paper presents an explainable two-stage deep learning system combining YOLOv8 and ResNet-50 for accurate, interpretable diagnosis of pericoronitis in panoramic radiographs, enhancing clinical trust and decision-making.

Contribution

It introduces a novel AI pipeline integrating anatomical localization, pathology classification, and interpretability for dental radiograph analysis.

Findings

YOLOv8 achieved 92% precision and 92.5% mAP in molar detection.

ResNet-50 classifier obtained F1-scores of 88% and 86% for normal and pericoronitis cases.

Grad-CAM highlighted diagnostic regions aligned with radiologists' impressions at 84%.

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.