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Updated: Jun 11, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Current Advances in Computerized Cognitive Assessment for Mild Cognitive Impairment and Dementia in Older Adults: A
Cornelia Henkel1, Susan Seibert1,2, Catherine Nichols Widmann1,2
1University Hospital Bonn, Center for Neurology, Bonn, Germany.
Introduction:
Timely detection of cognitive impairment such as mild cognitive impairment (MCI) or dementia is pivotal in initiating early interventions to improve patients' quality of life. Conventional paper-pencil tests, though common, have limited sensitivity in detecting subtle cognitive changes. Computerized assessments offer promising alternatives, overcoming time and manual scoring constraints while potentially providing greater sensitivity.
Methods:
A literature search yielded 26 eligible articles (2020-2023). The articles were reviewed according to PRISMA guidelines, and the computerized tools were categorized by diagnostic outcome (MCI, dementia, combined).
Results:
The subjects included in the studies were aged 55-77 years. The overall gender distribution comprised 60% females and 40% males. The sample sizes varied considerably from 22 to 4,486. Convergent validity assessments in 20 studies demonstrated strong positive correlations with traditional tests. Overall classification accuracy in detecting MCI or dementia, distinguishing from normal cognition (NC), reached up to 91%. Impressively, 46% of the studies received high-quality ratings, underscoring the reliability and validity of the findings.
Conclusion:
The review highlights the advancements in computerized cognitive assessments for assessing MCI and dementia. This shift toward technology-based assessments could enhance detection capabilities and facilitate timely interventions for better patient outcomes.
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