Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study.

Journal: Sleep & breathing = Schlaf & Atmung
Published Date: Feb 8, 2021

Abstract

PURPOSE: In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images.

Authors

  • Satoru Tsuiki
    Institute of Neuropsychiatry, 91, Bentencho, Shinjuku-ku, Tokyo, 162-0851, Japan. strtsuiki@gmail.com.
  • Takuya Nagaoka
    Rist Inc., Kyoto, Japan.
  • Tatsuya Fukuda
    Institute of Neuropsychiatry, 91, Bentencho, Shinjuku-ku, Tokyo, 162-0851, Japan.
  • Yuki Sakamoto
    Rist Inc., Kyoto, Japan.
  • Fernanda R Almeida
    Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, Canada.
  • Hideaki Nakayama
    Institute of Neuropsychiatry, 91, Bentencho, Shinjuku-ku, Tokyo, 162-0851, Japan.
  • Yuichi Inoue
    Institute of Neuropsychiatry, 91, Bentencho, Shinjuku-ku, Tokyo, 162-0851, Japan.
  • Hiroki Enno
    Rist Inc., Tokyo, Japan.

Keywords

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