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.
Original language | English (US) |
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Pages (from-to) | 14-21 |
Number of pages | 8 |
Journal | Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration |
Volume | 22 |
Issue number | S1 |
DOIs | |
State | Published - 2021 |
Keywords
- Clinical trial
- biomarker
- respiratory function
- speech analysis
- ventilation
ASJC Scopus subject areas
- Neurology
- Clinical Neurology