Web-based fully automated cephalometric analysis by deep learning.

Journal: Computer methods and programs in biomedicine
Published Date: May 6, 2020

Abstract

BACKGROUND AND OBJECTIVE: An accurate lateral cephalometric analysis is vital in orthodontic diagnosis. Identification of anatomic landmarks on lateral cephalograms is tedious, and errors may occur depending on the doctor's experience. Several attempts have been made to reduce this time-consuming process by automating the process through machine learning; however, they only dealt with a small amount of data from one institute. This study aims to develop a fully automated cephalometric analysis method using deep learning and a corresponding web-based application that can be used without high-specification hardware.

Authors

  • Hannah Kim
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
  • Eungjune Shim
    Center for Bionics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea. Electronic address: ejshim@kist.re.kr.
  • Jungeun Park
    Department of Orthodontics, Graduate School, Yonsei University College of Dentistry, 50-1, Yonseiro, Seodaemun-gu, Seoul, 03722, Republic of Korea. Electronic address: cong7135@naver.com.
  • Yoon-Ji Kim
    Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Kangwon-do, 26493, South Korea.
  • Uilyong Lee
    Department of Oral and Maxillofacial Surgery, Chungang University Hospital, 102, Heukseok-ro, Dongjak-gu, Seoul, 06973, Republic of Korea; Tooth Bioengineering National Research Laboratory, BK21, School of Dentistry, Seoul National University, Daehak-ro 101, Jongno-gu, Seoul, 03080, Republic of Korea. Electronic address: davidjoy@caumc.or.kr.
  • Youngjun Kim

Keywords

External Resources

Popular Topics
Recent Journals