TY - JOUR

T1 - Automatic scoring of drug-induced sleep endoscopy for obstructive sleep apnea using deep learning

AU - Hanif, Umaer

AU - Kiaer, Eva Kirkegaard

AU - Capasso, Robson

AU - Liu, Stanley Y.

AU - Mignot, Emmanuel J. M.

AU - Sorensen, Helge B. D.

AU - Jennum, Poul

N1 - Publisher Copyright: © 2022 The Authors

PY - 2023

Y1 - 2023

N2 - 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. Methods: We included 281 DISE videos with varying durations (6 s–16 min) from two sleep clinics: Copenhagen University Hospital and Stanford University Hospital. Examinations were split into 5-s clips, each receiving annotations of 0, 1, 2, or X (site not visible) for each site (V, O, T, and E), which was used to train a deep learning model. Predicted VOTE obstruction degrees per examination was obtained by taking the highest predicted degree per site across 5-s clips, which was evaluated against VOTE degrees annotated by surgeons. Results: Mean F1 score of 70% was obtained across all DISE examinations (V: 85%, O: 72%, T: 57%, E: 65%). For each site, sensitivity was highest for degree 2 and lowest for degree 0. No bias in performance was observed between videos from different clinicians/hospitals. Conclusions: This study demonstrates that automating scoring of DISE examinations show high validity and feasibility in degree of upper airway collapse.

AB - 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. Methods: We included 281 DISE videos with varying durations (6 s–16 min) from two sleep clinics: Copenhagen University Hospital and Stanford University Hospital. Examinations were split into 5-s clips, each receiving annotations of 0, 1, 2, or X (site not visible) for each site (V, O, T, and E), which was used to train a deep learning model. Predicted VOTE obstruction degrees per examination was obtained by taking the highest predicted degree per site across 5-s clips, which was evaluated against VOTE degrees annotated by surgeons. Results: Mean F1 score of 70% was obtained across all DISE examinations (V: 85%, O: 72%, T: 57%, E: 65%). For each site, sensitivity was highest for degree 2 and lowest for degree 0. No bias in performance was observed between videos from different clinicians/hospitals. Conclusions: This study demonstrates that automating scoring of DISE examinations show high validity and feasibility in degree of upper airway collapse.

KW - Deep learning

KW - Drug-induced sleep endoscopy

KW - Obstructive sleep apnea

KW - vote classification

U2 - 10.1016/j.sleep.2022.12.015

DO - 10.1016/j.sleep.2022.12.015

M3 - Journal article

C2 - 36587544

AN - SCOPUS:85144947815

VL - 102

SP - 19

EP - 29

JO - Sleep Medicine

JF - Sleep Medicine

SN - 1389-9457

ER -