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Updated: May 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild cognitive impairment. Part 1: clinical characteristics and predictors of dementia
Orestes V Forlenza1, Breno S Diniz, Florindo Stella
1Laboratory of Neuroscience (LIM-27), Department and Institute of Psychiatry, Faculty of Medicine, Universidade de São Paulo (USP), São Paulo, SP, Brazil. forlenza@usp.br
Objective:
To critically review and evaluate existing knowledge on the conceptual limits and clinical usefulness of the diagnosis of mild cognitive impairment (MCI) and the neuropsychological assessment and short- and long-term prognosis thereof.
Methods:
We conducted a systematic search of the PubMed and Web of Science electronic databases, limited to articles published in English between 1999 and 2012. Based on the search terms mild cognitive impairment or MCI and epidemiology or diagnosis, we retrieved 1,698 articles, of which 248 were critically eligible (cross-sectional and longitudinal studies); the abstracts of the remaining 1,450 articles were also reviewed.
Results:
A critical review on the MCI construct is provided, including conceptual and diagnostic aspects; epidemiological relevance; clinical assessment; prognosis; and outcome. The distinct definitions of cognitive impairment, MCI included, yield clinically heterogeneous groups of individuals. Those who will eventually progress to dementia may present with symptoms consistent with the definition of MCI; conversely, individuals with MCI may remain stable or return to normal cognitive function.
Conclusion:
On clinical grounds, the cross-sectional diagnosis of MCI has limited prognostic relevance. The characterization of persistent and/or progressive cognitive deficits over time is a better approach for identification of cases at the pre-dementia stages, particularly if these cognitive abnormalities are consistent with the natural history of incipient Alzheimer's disease.
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