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Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts

BACKGROUND: Modern prodromal Alzheimer’s disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. OBJE...

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Detalles Bibliográficos
Autores principales: Schäfer, Simona, Mallick, Elisa, Schwed, Louisa, König, Alexandra, Zhao, Jian, Linz, Nicklas, Bodin, Timothy Hadarsson, Skoog, Johan, Possemis, Nina, ter Huurne, Daphne, Zettergren, Anna, Kern, Silke, Sacuiu, Simona, Ramakers, Inez, Skoog, Ingmar, Tröger, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912722/
https://www.ncbi.nlm.nih.gov/pubmed/36565116
http://dx.doi.org/10.3233/JAD-220762
Descripción
Sumario:BACKGROUND: Modern prodromal Alzheimer’s disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. OBJECTIVE: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. METHODS: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. RESULTS: The algorithms achieved a performance of AUC  0.73 and AUC  0.77 in the respective training cohorts and AUC  0.81 in the unseen validation cohorts. CONCLUSION: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.