Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores
Year: 2025
Type: article
Abstract: Background Cognitive 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. Objective This 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. Methods Within 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). Results Global 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). Conclusions Our findings support speech-based cognitive assessments as a promising avenue towards remote MCI screening, with implications for scalable screening in clinical trials and healthcare.
Source: Journal of Alzheimer s Disease
Authors: Zampeta-Sofia Alexopoulou, Stefanie Köhler, Elisa Mallick, Johannes Tröger, Nicklas Linz, Eike Spruth, Klaus Fliessbach, Claudia Bartels, Ayda Rostamzadeh, Wenzel Glanz, Enise I Incesoy, Michaela Butryn, Ingo Kilimann, Sebastian Sodenkamp, Matthias HJ Munk, Antje Osterrath, Anna Esser, Sandra Roeske, Ingo Frommann, Melina Stark, Luca Kleineidam, Annika Spottke, Josef Priller, Anja Schneider, Jens Wiltfang, Frank Jessen, Emrah Düzel, Bjoern Falkenburger, Michael Wagner, Christoph Laske, Valeria Manera, Stefan Teipel, Alexandra König
Institutions: Université Côte d'Azur, Cognition Behaviour Technology, German Center for Neurodegenerative Diseases, Stiftung Charité, Charité - Universitätsmedizin Berlin, University of Bonn, University Hospital Bonn, Universitätsmedizin Göttingen, University of Göttingen, University of Cologne, University Hospital Magdeburg, Otto-von-Guericke-Universität Magdeburg, Universitätsmedizin Rostock, University of Tübingen, University Hospital Carl Gustav Carus, German Center for Neurodegenerative Diseases (DZNE) Dresden, Technische Universität Dresden, UK Dementia Research Institute, Technical University of Munich, University of Edinburgh, University of Aveiro, Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases, Hertie Institute for Clinical Brain Research
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4410729748