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| Home > Publications Database > Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores. |
| Home > Publications Database > Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores. |
| Journal Article | DZNE-2025-01232 |
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2025
IOS Press
Amsterdam
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Please use a persistent id in citations: doi:10.1177/13872877251343296
Abstract: BackgroundCognitive decline in Alzheimer's disease (AD) often includes speech impairments, where subtle changes may precede clinical dementia onset. As clinical trials focus on early identification of patients for disease-modifying treatments, digital speech-based assessments for scalable screening have become crucial.ObjectiveThis study aimed to validate a remote, speech-based digital cognitive assessment for mild cognitive impairment (MCI) detection through the comparison with gold-standard paper-based neurocognitive assessments.MethodsWithin the PROSPECT-AD project, speech and clinical data were obtained from the German DELCODE and DESCRIBE cohorts, including 21 healthy controls (HC), 110 participants with subjective cognitive decline (SCD), and 59 with MCI. Spearman rank and partial correlations were computed between speech-based scores and clinical measures. Kruskal-Wallis tests assessed group differences. We trained machine learning models to classify diagnostic groups comparing classification accuracies between gold-standard assessment scores and a speech-based digital cognitive assessment composite score (SB-C).ResultsGlobal cognition, as measured by SB-C, significantly differed between diagnostic groups (H(2) = 30.93, p < 0.001). Speech-based scores were significantly correlated with global anchor scores (MMSE, CDR, PACC5). Speech-based composites for memory, executive function and processing speed were also correlated with respective domain-specific paper-based assessments. In logistic regression classification, the model combining SB-C and neuropsychological tests at baseline achieved a high discriminatory power in differentiating HC/SCD from MCI patients (Area Under the Curve = 0.86).ConclusionsOur findings support speech-based cognitive assessments as a promising avenue towards remote MCI screening, with implications for scalable screening in clinical trials and healthcare.
Keyword(s): Humans (MeSH) ; Cognitive Dysfunction: diagnosis (MeSH) ; Cognitive Dysfunction: psychology (MeSH) ; Male (MeSH) ; Female (MeSH) ; Aged (MeSH) ; Neuropsychological Tests (MeSH) ; Speech: physiology (MeSH) ; Aged, 80 and over (MeSH) ; Middle Aged (MeSH) ; Cohort Studies (MeSH) ; Alzheimer's disease ; dementia ; digital cognitive assessment ; mild cognitive impairment ; speech analysis ; speech-based digital cognitive assessment ; speech-based markers
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