Explainable Deep Learning Framework for Classifying Mandibular Fractures on Panoramic Radiographs

Abstract

This study aimed to develop a deep-learning model for the automatic classification of mandibular fractures using panoramic radiographs. A pretrained convolutional neural network (CNN) was used to classify fractures based on a novel, clinically relevant classification system. The dataset comprised 800 panoramic radiographs obtained from patients with facial trauma. The model demonstrated robust classification performance across 8 fracture categories, achieving consistently high accuracy and F1 scores. Performance was evaluated using standard metrics, including accuracy, precision, recall, and F1-score. To enhance interpretability and clinical applicability, explainable AI techniques-Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME)-were used to visualize the model's decision-making process. These findings suggest that the proposed deep learning framework is a reliable and efficient tool for classifying mandibular fractures on panoramic radiographs. Its application may help reduce diagnostic time and improve decision-making in maxillofacial trauma care. Further validation using larger, multi-institutional datasets is recommended to ensure generalizability.

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