Frontiers in digital health · 2026
Listening to MS: AI-assisted speech analysis for diagnosis and fatigue prediction (COMMITMENT).
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Hammer Helly, Gonzalez-Machorro Monica, Hecker Pascal, Reichel Uwe, Zmutt Alisha, Pedrotti Lisa, Chan Andrew, Eyben Florian, Sagha Hesam, Kahlau Matthias, Arnrich Bert, Schuller Björn W, Hoepner Robert
Аннотация
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, n = 20) and relapsing MS (RMS) patients (n = 50, EDSS <4.0) at University Hospital of Bern. The primary objective was to predict fatigue in pwMS by means of automated artificial intelligence (AI)-based speech analysis. An exploratory objective was to differentiate between pwMS and 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.
METHODS: This COMMITMENT trial was a prospective, observational study recruiting healthy controls (HCs, n = 20) and relapsing MS (RMS) patients (n = 50, EDSS <4.0) at University Hospital of Bern. The primary objective was to predict fatigue in pwMS by means of automated artificial intelligence (AI)-based speech analysis. An exploratory objective was to differentiate between pwMS and 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.