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

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.