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Updated: Jan 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Neuropsychological Diagnostic Tools in Mild Cognitive Impairment: A Systematic Review and Meta-Analysis
Nuria Jerez-Garrido1, Ana M Jiménez-García1, David Sánchez-Borda1
1BRABE Group, Department of Psychology, Faculty of Life and Natural Sciences, University of Nebrija, C/del Hostal, 28248 Madrid, Spain.
None:
Due to baseline differences in cognitive test performance and their variable capacity to predict progression from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD), this systematic review and meta-analysis aimed to identify the most frequently used neuropsychological tests and their predictive value. Given the high prevalence of MCI, identifying accurate diagnostic tools is crucial. This study examined cognitive test differences between MCI patients and healthy individuals in clinical studies. A random-effects meta-analysis was conducted to account for variability. To this end, a comprehensive search was conducted across PubMed, Scopus, ScienceDirect, and Cochrane databases. Of the 103 studies that met the inclusion criteria and were reviewed, 34 were excluded from the meta-analysis due to the absence of control groups, and 7 articles were excluded because there were not enough comparable studies to perform a meaningful analysis. The meta-analysis revealed significant cognitive impairment in global cognition, language, and emotional well-being. Some studies reported hippocampal atrophy and amyloid-β/tau deposition in MCI patients progressing to AD, while others found benefits from physical and cognitive training. These findings highlight the need for standardized cognitive assessments to identify MCI patients at risk of AD conversion and implement effective treatments. Nevertheless, this meta-analysis faced limitations due to missing patient data, including the MCI subtype and disease duration.
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