Artificial intelligence facial recognition of obstructive sleep apnea: a Bayesian meta-analysis.

Journal: Sleep & breathing = Schlaf & Atmung
PMID: 39614959

Abstract

PURPOSE: Conventional obstructive sleep apnea (OSA) diagnosis via polysomnography can be costly and inaccessible. Recent advances in artificial intelligence (AI) have enabled the use of craniofacial photographs to diagnose OSA. This meta-analysis aims to clarify the diagnostic accuracy of this innovative approach.

Authors

  • Esther Yanxin Gao
    Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.
  • Benjamin Kye Jyn Tan
    Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore. benjamintankyejyn@u.nus.edu.
  • Nicole Kye Wen Tan
    Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Adele Chin Wei Ng
    Department of Otorhinolaryngology-Head & Neck Surgery, Singapore General Hospital (SGH), Singapore, Singapore.
  • Zhou Hao Leong
    Department of Otorhinolaryngology - Head and Neck Surgery, Singapore General Hospital, Singapore.
  • Chu Qin Phua
    Department of Otorhinolaryngology, Sengkang General Hospital, Singapore, Singapore.
  • Shaun Ray Han Loh
    Department of Otorhinolaryngology - Head and Neck Surgery, Singapore General Hospital, Singapore.
  • Maythad Uataya
    Siriraj Piyamaharajkarun Hospital, Bangkok, Thailand.
  • Liang Chye Goh
    Department of Otorhinolaryngology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Thun How Ong
    Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore.
  • Leong Chai Leow
    Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore.
  • Guang-Bin Huang
  • Song Tar Toh
    Department of Otorhinolaryngology - Head and Neck Surgery, Singapore General Hospital, Singapore.

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