ArticleFrontiers in digital health2026
Listening to MS: AI-assisted speech analysis for diagnosis and fatigue prediction (COMMITMENT).
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Outside the healthy bounds: reference intervals for population and individual-level medical speech analysis.Frontiers in digital health · 2026Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Identification of fatigue in people with Multiple Sclerosis (pwMS) is still mainly based on subjective assessments due to the lack of objective diagnostic tools. We aimed to identify vocal biomarkers to differentiate between pwMS with and without fatigue. Methods: This COMMITMENT trial was a prospective, observational study recruiting healthy controls (HCs, Findings: Participants had a mean age of 36.0 years, and 73% were female, with no significant group differences. Median EDSS in pwMS was 1.0 (range 0-3.0). Motor fatigue affected 50% and cognitive fatigue 40% of pwMS. Five acoustic features were associated with general fatigue, independently of depression and sleepiness. Further, five features were associated with motor-, and 12 with cognitive fatigue. The best-performing classification and regression models [Leave-One-Speaker-Out (LOSO) paradigm] achieved specificities of 0.68-0.94, whereas sensitivity remained lower (0.38-0.90). Speech biomarkers distinguished pwMS from HCs with a specificity of 0.90 but only a sensitivity of 0.3. Interpretation: Speech in pwMS may serve as a potential biomarker for MS-associated fatigue and might help to differentiate between pwMS and HC. Our findings suggest that AI-assisted speech analysis could complement existing fatigue assessments.
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