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.

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.