Prediction of Aspiration Risk by Using Vocal Biomarkers: Machine Learning Development and Validation Study

Background: Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor interrater and intrarater reliability, while gold standard diagnostic tests for aspiration, such as video fluoroscopic swallow study and fiberoptic endoscopic evaluation of swallowing, are cumbersome or invasive and health care resource-intensive. Objective: This study aims to develop and validate a novel machine learning (ML) algorithm that can analyze simple vowel phonations to aid in predicting aspiration risk. Methods: Recorded [i] phonations during routine nasal endoscopy from 163 unique patients were retrospectively analyzed for acoustic features, including pitch, jitter, shimmer, harmonic to noise ratio, and others. Supervised ML was performed on the vowel phonations of those at high-risk for aspiration versus those at low-risk for aspiration. Ground truth of aspiration risk classification for model development was established using a video fluoroscopic swallow study. The performance of the ML model was tested on an independent, external cohort of patient voice samples. The performance of trained speech language pathologists to categorize high versus low-risk aspirators by listening to phonations was compared against the ML model. Results: Mean ML risk score for those with the ground truth of high versus low aspiration risk was 0.530 (SD 0.310) vs 0.243 (SD 0.249), which was a significant difference (0.287, 95% CI 0.192-0.381; P<.001). In the development cohort, the model showed an area under the curve for the receiver operator characteristic of 0.76 (0.67-0.84) with specificity of 0.76 and F₁-score of 0.63. The performance of the model in an external testing cohort was comparable, with an area under the curve of 0.70 (0.52-0.88), a specificity of 0.81, and an F₁-score of 0.67. The ML model had comparable accuracy, sensitivity, specificity, negative, and positive predictive values compared to trained speech language pathologists in classifying aspiration risk by evaluating vowel phonations. Conclusions: Otolaryngology (ear, nose, and throat) patients at high risk for aspiration have quantifiable voice characteristics that significantly differ from those who are at a low risk for aspiration, as detected by an ML model trained to analyze sustained phonation and tested on an independent cohort.