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Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning.

Dong-Min Son1, Yeong-Ah Yoon2, Hyuk-Ju Kwon1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea.

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Summary

This study introduces a deep learning system for diagnosing mandibular fractures from panoramic radiographs, improving accuracy and aiding radiologists in fracture detection.

Keywords:
YOLOYOLO v4deep learningimage processingmandibular fracturemulti-scale luminance adaptation transform (MLAT)object detectionpanoramic radiographysingle-scale luminance adaptation transform (SLAT)

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Area of Science:

  • Oral and Maxillofacial Surgery
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Mandibular fractures are common in oral and maxillo-facial surgery.
  • Diagnosis typically relies on panoramic radiography or cone-beam computed tomography (CBCT).
  • Panoramic radiography is a simpler imaging technique compared to CBCT.

Purpose of the Study:

  • To develop a deep learning system for automated mandibular fracture diagnosis using panoramic radiographs.
  • To evaluate the performance of a YOLO-based deep learning model for this task.
  • To enhance the diagnostic accuracy of mandibular fracture detection.

Main Methods:

  • Implementation of a YOLO (You Only Look Once) deep learning model for fracture detection.
  • Augmentation of panoramic radiograph images using gamma modulation, multi-bounding boxes, and luminance adaptation transforms.
  • Testing the deep learning system's performance on panoramic radiographs.

Main Results:

  • The YOLO-based deep learning system demonstrated superior detection performance compared to conventional methods.
  • Image augmentation techniques improved the accuracy of the deep learning model.
  • The proposed method offers a reliable tool for identifying mandibular fractures.

Conclusions:

  • A deep learning system based on YOLO can effectively diagnose mandibular fractures from panoramic radiographs.
  • The developed system shows potential to assist radiologists in double-checking diagnoses.
  • This AI-driven approach enhances the efficiency and accuracy of mandibular fracture detection.