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Automatic detection of mandibular fractures on CT scan using deep learning.

Yuanyuan Liu1, Xuechun Wang2, Yeting Tu1

  • 1Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.

Dento Maxillo Facial Radiology
|April 16, 2025
PubMed
Summary

Deep learning models show high accuracy in detecting and classifying mandibular fractures from CT scans. The nn-Net framework improves fracture identification, aiding clinical diagnosis.

Keywords:
CT scansResNet-101deep learningdiagnostic accuracymandibular fracturesmedical image segmentationnnU-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oral and Maxillofacial Surgery

Background:

  • Mandibular fractures are common injuries requiring accurate diagnosis.
  • Computed Tomography (CT) scans are crucial for evaluating these fractures.
  • AI offers potential for improving diagnostic accuracy in medical imaging.

Purpose of the Study:

  • To evaluate the efficacy of deep learning (DL) models for detecting and classifying mandibular fractures using CT scans.
  • To assess the performance of the nnU-Net segmentation framework and a 3D-ResNet model.

Main Methods:

  • Retrospective analysis of 459 patient CT scans (2020-2023).
  • Utilized the nnU-Net framework for pixel-level fracture detection.
  • Employed a 3D-ResNet model for fracture classification based on severity.
  • Evaluated performance using sensitivity, precision, specificity, and AUC.

Main Results:

  • High diagnostic accuracy for mandibular fracture detection (sensitivity >0.93, precision >0.79, specificity >0.80).
  • Mandibular fracture classification achieved accuracies above 0.718.
  • Mean Area Under the Curve (AUC) for classification was 0.86.

Conclusions:

  • Deep learning, particularly the nnU-Net framework, significantly enhances the detection and classification of mandibular fractures in CT images.
  • AI-driven analysis can improve the accuracy and efficiency of clinical diagnosis for these injuries.