An Explainable Two Stage Deep Learning Framework for Pericoronitis Assessment in Panoramic Radiographs Using YOLOv8 and ResNet-50
PositiveArtificial Intelligence
- A new study has introduced an explainable two-stage deep learning framework for assessing pericoronitis in panoramic radiographs, utilizing YOLOv8 for anatomical localization and a modified ResNet-50 for pathological classification. The system achieved high precision and alignment with radiologists' diagnostic impressions, enhancing interpretability through Grad-CAM visualizations.
- This development is significant as it addresses the diagnostic challenges associated with pericoronitis, potentially improving clinical outcomes and trust in AI-assisted assessments among healthcare professionals.
- The integration of explainable AI in medical imaging reflects a growing trend towards enhancing diagnostic accuracy and interpretability, paralleling advancements in other areas such as pneumonia localization and skin disease classification, where similar methodologies are being employed to bolster the reliability of AI systems in clinical settings.
— via World Pulse Now AI Editorial System
