Ultrasound Predicts Drug-Induced Sleep Endoscopy Findings Using Machine Learning Models.

Abstract

Objectives: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug-induced sleep endoscopy (DISE). This study was devised to identify ultrasound-derived anatomical measurements that could accurately predict collapse pattern and location based on the VOTE criteria (VOTE: Velum, Oropharynx, Tongue, and Epiglottis).

Methods: Ultrasonography was performed on 20 adult patients of various airway subsites while awake and sedated with concurrent endoscopy performed during drug-induced sleep. Measurements were obtained from the ultrasonographic images, and percent collapse (P_c) was estimated then graded using a standard VOTE score. Generalized Least Squares regression (GLS) was used to establish models predictive of P_c on a continuous scale, while multiple machine learning (ML) models were trained to predict each VOTE score (binary, >50% collapse) from ultrasound measurements.

Results: Measurements of multiple ultrasonographic airway subsites demonstrated associations with endoscopic collapse using Kendall Tau correlation. The GLS models showed moderate to strong correlation between multiple ultrasound features and P_c (R² _adj 0.53-0.82) across all VOTE subsites. ML models accurately predicted binarized VOTE scores from ultrasound measurements in four out of five VOTE subsites (F1 score >0.65), while the VOTE subsite with the most accurately predicted collapse was lateral velum collapse with an F1 score of 0.93 averaged across all models.

Conclusions: Ultrasound is a reliable imaging modality and can identify features of airway collapse during drug-induced sleep. Regression (GLS) and ML models show promise in predicting severity of collapse during DISE with analysis of airway ultrasonographic measurements.

Level of evidence: 3 Laryngoscope, 2025.