Fully automated identification of cephalometric landmarks for upper airway assessment using cascaded convolutional neural networks.

Journal: European journal of orthodontics
Published Date: Jan 25, 2022

Abstract

OBJECTIVES: The aim of the study was to evaluate the accuracy of a cascaded two-stage convolutional neural network (CNN) model in detecting upper airway (UA) soft tissue landmarks in comparison with the skeletal landmarks on the lateral cephalometric images.

Authors

  • Hyun-Joo Yoon
    Department of Dentistry, Graduate School, Kyung Hee University, Seoul, Republic of Korea.
  • Dong-Ryul Kim
    Department of Dentistry, Graduate School, Kyung Hee University, Seoul, Republic of 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.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Seung-Hak Baek
    Department of Orthodontics.
  • Hyo-Won Ahn
    Department of Orthodontics, Graduate School, Kyung Hee University, Seoul 02447, Korea.
  • Kyung-A Kim
    Department of Orthodontics, School of Dentistry, Kyung Hee University, Seoul, Republic of Korea.
  • Su-Jung Kim
    Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea.

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