Deep learning technique to detect craniofacial anatomical abnormalities concentrated on middle and anterior of face in patients with sleep apnea.

Journal: Sleep medicine
PMID: 37801860

Abstract

OBJECTIVES: The aim of this study is to propose a deep learning-based model using craniofacial photographs for automatic obstructive sleep apnea (OSA) detection and to perform design explainability tests to investigate important craniofacial regions as well as the reliability of the method.

Authors

  • Shuai He
    Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, 1 Dongjiaominxiang, Dongcheng District, Beijing, 100730, People's Republic of China.
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Chong Zhang
    Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, Beijing 100048, China.
  • Zufei Li
    Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, People's Republic of China.
  • Yuanyuan Ren
    Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China.
  • Tiancheng Li
    School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
  • Jianting Wang
    Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China. Electronic address: ENT_wjt@163.com.

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