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Explainable Deep Learning Framework for Classifying Mandibular Fractures on Panoramic Radiographs.

Hyejun Seo¹, Jae-Il Lee¹, Jeong-Uk Park²

¹Department of Dentistry, University of Ulsan Hospital, University of Ulsan College of Medicine.

The Journal of Craniofacial Surgery

|September 10, 2025

Summary

A new deep learning model accurately classifies mandibular fractures from panoramic radiographs. This AI tool aids in faster diagnosis and better treatment decisions for maxillofacial trauma patients.

Keywords:

Convolutional neural networks deep learning explainable AI mandibular fractures panoramic radiograph

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Mandibular fractures are common injuries in maxillofacial trauma.
Accurate and timely classification is crucial for effective treatment.
Current diagnostic methods can be time-consuming.

Purpose of the Study:

To develop and validate a deep learning model for automatic mandibular fracture classification.
To utilize a novel, clinically relevant classification system for fractures.
To enhance model interpretability using explainable AI techniques.

Main Methods:

A pretrained convolutional neural network (CNN) was employed.
The model was trained on 800 panoramic radiographs.
Fracture classification was performed across 8 distinct categories.
Explainable AI methods (Grad-CAM, LIME) were used for visualization.

Main Results:

The deep learning model achieved high accuracy and F1 scores.
Robust classification performance was observed across all 8 fracture categories.
Explainable AI techniques provided insights into the model's decision-making process.

Conclusions:

The developed deep learning framework is a reliable tool for classifying mandibular fractures on panoramic radiographs.
This AI approach can potentially reduce diagnostic time and improve clinical decision-making in maxillofacial trauma.
Further validation on larger, multi-institutional datasets is recommended for generalizability.