Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern.

Journal: American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Published Date: May 31, 2022

Abstract

INTRODUCTION: This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare the accuracy of the model performance across different skeletal patterns presenting with various pharyngeal dimensions.

Authors

  • Ha-Nul Cho
    Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea.
  • Eunseo Gwon
    Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Kyung-A Kim
    Department of Orthodontics, School of Dentistry, Kyung Hee University, Seoul, Republic of Korea.
  • Seung-Hak Baek
    Department of Orthodontics.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Su-Jung Kim
    Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea.

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