001     281859
005     20251218103439.0
024 7 _ |a 10.1177/13872877251343296
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024 7 _ |a pmid:40415342
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024 7 _ |a pmc:PMC12583645
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024 7 _ |a 1387-2877
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024 7 _ |a 1875-8908
|2 ISSN
037 _ _ |a DZNE-2025-01232
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Alexopoulou, Zampeta-Sofia
|0 0000-0002-7304-0834
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245 _ _ |a Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores.
260 _ _ |a Amsterdam
|c 2025
|b IOS Press
336 7 _ |a article
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336 7 _ |a ARTICLE
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520 _ _ |a 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.
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650 _ 7 |a Alzheimer's disease
|2 Other
650 _ 7 |a dementia
|2 Other
650 _ 7 |a digital cognitive assessment
|2 Other
650 _ 7 |a mild cognitive impairment
|2 Other
650 _ 7 |a speech analysis
|2 Other
650 _ 7 |a speech-based digital cognitive assessment
|2 Other
650 _ 7 |a speech-based markers
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Cognitive Dysfunction: diagnosis
|2 MeSH
650 _ 2 |a Cognitive Dysfunction: psychology
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Neuropsychological Tests
|2 MeSH
650 _ 2 |a Speech: physiology
|2 MeSH
650 _ 2 |a Aged, 80 and over
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Cohort Studies
|2 MeSH
700 1 _ |a Köhler, Stefanie
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700 1 _ |a Mallick, Elisa
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700 1 _ |a Tröger, Johannes
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700 1 _ |a Glanz, Wenzel
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700 1 _ |a Munk, Matthias Hj
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700 1 _ |a Osterrath, Antje
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700 1 _ |a Esser, Anna
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700 1 _ |a Roeske, Sandra
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700 1 _ |a Frommann, Ingo
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700 1 _ |a Stark, Melina
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700 1 _ |a Kleineidam, Luca
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700 1 _ |a Spottke, Annika
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700 1 _ |a Priller, Josef
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700 1 _ |a Schneider, Anja
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700 1 _ |a Wiltfang, Jens
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700 1 _ |a Jessen, Frank
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700 1 _ |a Düzel, Emrah
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700 1 _ |a Falkenburger, Bjoern
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700 1 _ |a Wagner, Michael
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700 1 _ |a Laske, Christoph
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700 1 _ |a Manera, Valeria
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700 1 _ |a Teipel, Stefan
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700 1 _ |a König, Alexandra
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773 _ _ |a 10.1177/13872877251343296
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