Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres.

Journal: Orthodontics & craniofacial research
Published Date:

Abstract

OBJECTIVE: To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres.

Authors

  • Jaerong Kim
    Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Inhwan Kim
    Department of computer science, Sangmyung University, Seoul, South Korea.
  • Yoon-Ji Kim
    Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Kangwon-do, 26493, South Korea.
  • Minji Kim
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
  • Jin-Hyoung Cho
    Department of Orthodontics, Chonnam National University School of Dentistry, Gwangju, Korea.
  • Mihee Hong
    Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Korea.
  • Kyung-Hwa Kang
    Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan, Korea.
  • Sung-Hoon Lim
    Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, Korea.
  • Su-Jung Kim
    Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea.
  • Young Ho Kim
    Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon, Korea.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Sang-Jin Sung
    Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Seung-Hak Baek
    Department of Orthodontics.