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Updated: Sep 28, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Detecting depression in persons living with mild cognitive impairment: a systematic review
Britney Wong1,2, Zahinoor Ismail2,3,4,5, Zahra Goodarzi1,2,3,6,7
1Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada.
Objective:
Depression is common in persons experiencing mild cognitive impairment (MCI), with 32% (95% Cl 27, 37) overall experiencing depression. Persons with MCI who have depression have more cognitive changes compared to those without depression. To understand how we can detect depressive symptoms in persons with MCI, we undertook a systematic review to identify tools that were validated compared with a reference standard.
Design:
We searched MEDLINE, EMBASE, PsycINFO, and Cochrane from inception to April 25, 2021, and conducted a gray literature search. Title/abstract and full-text screening were completed in duplicate. Demographic information, reference standards, prevalence, and diagnostic accuracy measures were then extracted from included articles (PROSPERO CRD: CRD42016052120).
Results:
Across databases, 8,748 abstracts were generated after removing duplicates. Six hundred and sixty-five records underwent full-text screening, with six articles included for data extraction. Nine tools were identified compared to a reference standard, with multiple demonstrating a sensitivity of 100% (Brief Assessment Schedule Depression Cards, Beck Depression Inventory-II, Cornell Scale for Depression in Dementia, Zung Self-Rated Depression Scale, and the Neuropsychiatric Inventory). The second highest sensitivity reported was 89% (Patient Health Questionnaire-9). Too few studies were available for a meta-analysis.
Conclusions:
Multiple depression detection tools have been examined amongst MCI outpatients, with several showing high sensitivity. However, this evidence is only present in single studies, with little demonstration of how differing MCI types affect accuracy. More research is needed to confirm the accuracy of these tools amongst persons with MCI. At this time, several tools could be suitable for use in cognitive clinics.
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