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Updated: May 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A systematic review of screening tools for predicting the development of dementia
Andrea R Lischka1, Marissa Mendelsohn, Tom Overend
1The University of Western Ontario, London, Ontario. alischka@alumni.uwo.ca
Abstract:
Early detection of dementia is essential to guide front-line health care practitioners in further clinical evaluations and treatments. There is a paucity of literature assessing the effectiveness of screening tools to predict the development of dementia, thus we conducted a systematic review to fill this gap. The purpose of the systematic review was to make recommendations to health care practitioners on which screening tool best predicts the development of dementia and is most feasible in the primary care setting. Ten databases were searched for relevant articles, yielding 751 papers. Of these, 12 met relevance criteria for inclusion. Screening tools were assessed for test accuracy, cognitive domain coverage, predictive ability, and feasibility. Four screening tools were recommended. Addenbrooke's Cognitive Examination (ACE) was considered to be the ideal tool. A revised version of this tool is now used in clinical practice but the psychometric properties of the ACE-R remain to be established.
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