Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network.

Journal: Journal of dental sciences
Published Date: Nov 11, 2020

Abstract

BACKGROUND/PURPOSE: Facial asymmetry is relatively common in the general population. Here, we propose a fully automated annotation system that supports analysis of mandibular deviation and detection of facial asymmetry in posteroanterior (PA) cephalograms by means of a deep learning-based convolutional neural network (CNN) algorithm.

Authors

  • Saori Takeda
    Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Yuichi Mine
    Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Yuki Yoshimi
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Shota Ito
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Kotaro Tanimoto
    Department of Orthodontics and Craniofacial Developmental Biology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Takeshi Murayama
    Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

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