Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.

Journal: Sleep & breathing = Schlaf & Atmung
PMID: 37278870

Abstract

PURPOSE: The diagnosis of obstructive sleep apnea (OSA) relies on time-consuming and complicated procedures which are not always readily available and may delay diagnosis. With the widespread use of artificial intelligence, we presumed that the combination of simple clinical information and imaging recognition based on facial photos may be a useful tool to screen for OSA.

Authors

  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Zhe Liang
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Qing Wang
    School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, China. qwang@163.com.
  • Chenyao Ma
    Sleep Medical Center, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Yi Lei
    Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, 3002 SunGangXi Road, Shenzhen, 518035, China.
  • John E Sanderson
    Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Xu Hu
    School of Mechanical Engineering, Baoji University of Arts and Sciences, Baoji, China.
  • Weihao Lin
    Automation School, Beijing University of Posts and Telecommunications, Beijing, China.
  • Hu Liu
    School of Instrument Science and Opto-electronic Engineering, Beihang University, Beijing, 10091, China.
  • Fei Xie
    Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.
  • Hongfeng Jiang
    Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. jhf@pku.edu.cn.
  • Fang Fang
    Department of Cardiology, Central War Zone General Hospital of the Chinese People's Liberation Army, Wuhan 430061, China.

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