TY  - JOUR
AU  - Alexopoulou, Zampeta-Sofia
AU  - Köhler, Stefanie
AU  - Mallick, Elisa
AU  - Tröger, Johannes
AU  - Linz, Nicklas
AU  - Spruth, Eike
AU  - Fliessbach, Klaus
AU  - Bartels, Claudia
AU  - Rostamzadeh, Ayda
AU  - Glanz, Wenzel
AU  - Incesoy, Enise I
AU  - Butryn, Michaela
AU  - Kilimann, Ingo
AU  - Sodenkamp, Sebastian
AU  - Munk, Matthias Hj
AU  - Osterrath, Antje
AU  - Esser, Anna
AU  - Roeske, Sandra
AU  - Frommann, Ingo
AU  - Stark, Melina
AU  - Kleineidam, Luca
AU  - Spottke, Annika
AU  - Priller, Josef
AU  - Schneider, Anja
AU  - Wiltfang, Jens
AU  - Jessen, Frank
AU  - Düzel, Emrah
AU  - Falkenburger, Bjoern
AU  - Wagner, Michael
AU  - Laske, Christoph
AU  - Manera, Valeria
AU  - Teipel, Stefan
AU  - König, Alexandra
TI  - Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores.
JO  - Journal of Alzheimer's disease
VL  - 108
IS  - 1_suppl
SN  - 1387-2877
CY  - Amsterdam
PB  - IOS Press
M1  - DZNE-2025-01232
SP  - S118 - S131
PY  - 2025
AB  - 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.
KW  - Humans
KW  - Cognitive Dysfunction: diagnosis
KW  - Cognitive Dysfunction: psychology
KW  - Male
KW  - Female
KW  - Aged
KW  - Neuropsychological Tests
KW  - Speech: physiology
KW  - Aged, 80 and over
KW  - Middle Aged
KW  - Cohort Studies
KW  - Alzheimer's disease (Other)
KW  - dementia (Other)
KW  - digital cognitive assessment (Other)
KW  - mild cognitive impairment (Other)
KW  - speech analysis (Other)
KW  - speech-based digital cognitive assessment (Other)
KW  - speech-based markers (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40415342
C2  - pmc:PMC12583645
DO  - DOI:10.1177/13872877251343296
UR  - https://pub.dzne.de/record/281859
ER  -