Automatic scoring of drug-induced sleep endoscopy for obstructive sleep apnea using deep learning.

Journal: Sleep medicine
PMID: 36587544

Abstract

BACKGROUND: Treatment of obstructive sleep apnea is crucial for long term health and reduced economic burden. For those considered for surgery, drug-induced sleep endoscopy (DISE) is a method to characterize location and pattern of sleep-related upper airway collapse. According to the VOTE classification system, four upper airway sites of collapse are characterized: velum (V), oropharynx (O), tongue (T), and epiglottis (E). The degree of obstruction per site is classified as 0 (no obstruction), 1 (partial obstruction), or 2 (complete obstruction). Here we propose a deep learning approach for automatic scoring of VOTE obstruction degrees from DISE videos.

Authors

  • Umaer Hanif
    Stanford Center for Sleep Sciences and Medicine, Stanford University, 3165 Porter Drive, MC 5480, Palo Alto, CA 94304-5480, United States of America. Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, University of Copenhagen, Glostrup, Denmark. Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Eva Kirkegaard Kiaer
    Danish Center for Sleep Surgery, Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital (Rigshospitalet), Inge Lehmanns Vej 8, 2100, Copenhagen, Denmark. Electronic address: eva.kirkegaard.kiaer.01@regionh.dk.
  • Robson Capasso
    Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
  • Stanley Y Liu
    Department of Otolaryngology/Head & Neck Surgery, Stanford University School of Medicine, 801 Welch Road, Palo Alto, CA, 94304, USA. Electronic address: ycliu@stanford.edu.
  • Emmanuel J M Mignot
  • Helge B D Sorensen
  • Poul Jennum
    Danish Center for Sleep Medicine, Rigshospitalet, Glostrup, 2600, Denmark.

Keywords

External Resources

Popular Topics
Recent Journals