Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. Misdiagnosis might lead to several systemic conditions, including hypertension, vascular damage, and cognitive impairment. The gold-standard diagnostic tool for OSA is polysomnography, which is expensive, time-consuming, and not accessible everywhere. Artificial intelligence (AI) algorithms can facilitate diagnosis by detecting patients' signs and symptoms. In this systematic review, we evaluated the diagnostic accuracy of AI models in detecting sleep apnea.

Authors

  • Sara Haghighat
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany.
  • Muhammed Joghatayi
    Psychiatry and Behavioral Sciences Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. muhammedjyi@gmail.com.
  • Julien Issa
    Department of Biomaterials and Experimental Dentistry, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland.
  • Sarina Azimian
    Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Janet Brinz
    ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany.
  • Ali Ashkan
    Department of Orthodontics, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Akhilanand Chaurasia
    Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India.
  • Zahra Rahimian
    School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Linda Sangalli
    College of Dental Medicine-Illinois, Midwestern University, Downers Grove, Illinois, USA.