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. 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.
Originalsprog | Engelsk |
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Tidsskrift | Sleep Medicine |
Vol/bind | 102 |
Sider (fra-til) | 19-29 |
Antal sider | 11 |
ISSN | 1389-9457 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:This study was supported by a grant from the Klarman Family Foundation. Additional funding was from the Technical University of Denmark, and the Danish Center for Sleep Medicine. Mr. Hanif's stay at Stanford University was funded by Danmark-Amerika Fondet, Vera og Carl Johan Michaelsens Legat, Reinholdt W. Jorck og Hustrus Fond, Torben og Alice Frimodts Fond, Christian og Ottilia Brorsons Rejselegat, Marie og M.B. Richters Fond, Oberstløjtnant Max Nørgaard og hustru Magda Nørgaards Legat, William Demant Fonden, Augustinus Fonden, Rudolph Als Fondet, Knud Højgaards Fond, Otto Mønsteds Fond, Julie von Müllens Fond, and Direktør Einar Hansen og hustru fru Vera Hansens Fond. The funding institutes played no role in the design and conduct of the study; no role in the collection, management, analysis, or interpretation of the data; and no role in the preparation, review, or approval of the manuscript.
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© 2022 The Authors