Machine Learning of Acoustic Voice Outcomes in Idiopathic Subglottic Stenosis

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OBJECTIVE: There are no objective reliable non-invasive screening tools to identify disease severity in patients with idiopathic subglottic stenosis (iSGS), a debilitating and life-threatening disease where scar tissue narrows the airway. This study aims to identify objective voice measures that characterize iSGS disease severity. METHODS: Voice recordings of the Rainbow Passage (RP) were obtained in patients with severe iSGS (n = 10) immediately prior to endoscopic balloon dilation (ED), 2 weeks post-treatment (n = 6), and in healthy age-matched volunteers (n = 10). CT neck was obtained before and after ED, and the luminal area was measured relative to cricoid as a percent stenosis. Quantitative voice analysis was performed using VoiceLab, an automated voice analysis program. Unsupervised machine learning was performed using principal component analysis (PCA), and a simple linear regression compared composite voice outcome to luminal airway stenosis. RESULTS: Patients with severe iSGS had increased RP duration (p = 0.013) and decreased speech rate (p = 0.012), articulation rate (p = 0.017), and average syllable duration (p = 0.0096) compared to healthy controls. Isolated recorded breathing samples during RP in severe iSGS patients showed increased cepstral peak prominence (p < 0.0001), mean breath count (p = 0.0039), mean breath duration (p < 0.0001), and time spent breathing (p = 0.0002) compared to controls. PCA analysis showed complete separation between normal and severe iSGS, with post-ED iSGS intervening the two groups. Linear regression of the composite principal component 1 strongly correlated with luminal airway caliber. CONCLUSIONS: Objective voice analysis using machine learning provides a novel, non-invasive biomarker which can be used to quantify iSGS disease severity in advance of surgical intervention.

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