Upper Airway Classification in Sleep Endoscopy Examinations using Convolutional Recurrent Neural Networks

Umaer Hanif, Eric Kezirian, Eva Kirkegaard Kiar, Emmanuel Mignot, Helge Bjarup Dissing Sørensen, Poul Jennum

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Assessing the upper airway (UA) of obstructive sleep apnea patients using drug-induced sleep endoscopy (DISE) before potential surgery is standard practice in clinics to determine the location of UA collapse. According to the VOTE classification system, UA collapse can occur at the velum (V), oropharynx (O), tongue (T), and/or epiglottis (E). Analyzing DISE videos is not trivial due to anatomical variation, simultaneous UA collapse in several locations, and video distortion caused by mucus or saliva. The first step towards automated analysis of DISE videos is to determine which UA region the endoscope is in at any time throughout the video: V (velum) or OTE (oropharynx, tongue, or epiglottis). An additional class denoted X is introduced for times when the video is distorted to an extent where it is impossible to determine the region. This paper is a proof of concept for classifying UA regions using 24 annotated DISE videos. We propose a convolutional recurrent neural network using a ResNet18 architecture combined with a two-layer bidirectional long short-term memory network. The classifications were performed on a sequence of 5 seconds of video at a time. The network achieved an overall accuracy of 82% and F1-score of 79% for the three-class problem, showing potential for recognition of regions across patients despite anatomical variation. Results indicate that large-scale training on videos can be used to further predict the location(s), type(s), and degree(s) of UA collapse, showing potential for derivation of automatic diagnoses from DISE videos eventually.
Original languageEnglish
Title of host publicationProceedings of 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PublisherIEEE
Publication date2021
Pages3957-3960
ISBN (Print)978-1-7281-1180-3
DOIs
Publication statusPublished - 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Virtual event
Duration: 1 Nov 20215 Nov 2021

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
LocationVirtual event
Period01/11/202105/11/2021
SeriesAnnual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference
ISSN2694-0604

Keywords

  • Endoscopy
  • Epiglottis
  • Humans
  • Neural Networks, Computer
  • Sleep
  • Sleep Apnea, Obstructive

Fingerprint

Dive into the research topics of 'Upper Airway Classification in Sleep Endoscopy Examinations using Convolutional Recurrent Neural Networks'. Together they form a unique fingerprint.

Cite this