In Journal of Alzheimer's disease : JAD
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 across 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 cohort.
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.
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
2022-Dec-19
Alzheimer’s disease, biomarker, clinical trial, machine learning, mild cognitive impairment, screening
