Estimation of forced vital capacity using speech acoustics in patients with ALS
Gabriela Stegmann, Shira Hahn, Cayla Jessica Duncan, Seward B. Rutkove, Julie Liss, Jeremy M. Shefner, Visar Berisha
Abstract
In this study, we present and provide validation data for a tool that predicts forced vital capacity (FVC) from speech acoustics collected remotely via a mobile app without the need for any additional equipment (e.g. a spirometer). We trained a machine learning model on a sample of healthy participants and participants with amyotrophic lateral sclerosis (ALS) to learn a mapping from speech acoustics to FVC and used this model to predict FVC values in a new sample from a different study of participants with ALS. We further evaluated the cross-sectional accuracy of the model and its sensitivity to within-subject change in FVC. We found that the predicted and observed FVC values in the test sample had a correlation coefficient of .80 and mean absolute error between .54 L and .58 L (18.5% to 19.5%). In addition, we found that the model was able to detect longitudinal decline in FVC in the test sample, although to a lesser extent than the observed FVC values measured using a spirometer, and was highly repeatable (ICC = 0.92-0.94), although to a lesser extent than the actual FVC (ICC = .97). These results suggest that sustained phonation may be a useful surrogate for VC in both research and clinical environments.