ArticleJournal of Alzheimer's disease : JAD2023
Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts.
Article in Journal of Alzheimer's disease : JAD, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis.Age and ageing · 2025Pooled it
- Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023.Frontiers in neuroscience · 2024Pooled it
- Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Evaluating digital cognitive tests for clinical use in Alzheimer's disease: A novel framework and scoping review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Speech-based digital cognitive assessment for clinical trials: Detecting cognitive impairment stages and AD biomarker relations across European cohorts.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Early cognitive screening for individuals on the dementia continuum: A novel approach amid current trends.Journal of Alzheimer's disease : JAD · 2026Review
- Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment.medRxiv : the preprint server for health sciences · 2025Article
- Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores.Journal of Alzheimer's disease : JAD · 2025Article
- Clinical Application of Machine Learning in Biomedical Engineering for the Early Detection of Neurological Disorders.Annals of biomedical engineering · 2025Article
- From "invisible" to "audible": Features extracted during simple speech tasks classify patient-reported fatigue in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- Detection of Alzheimer's Disease Using Logistic Regression and Clock Drawing Errors.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModern 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.
objectiveDevelop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations.
methodsTwo 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.
resultsThe 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.
conclusionThe 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.
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