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Updated: Mar 21, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Evaluation of Early Dementia (Mild Cognitive Impairment)
R S Osorio1, V Berti2, L Mosconi3
1Department of Psychiatry, Center for Brain Health, Silberstein Alzheimer's Institute, Center of Excellence on Brain Aging, NYU Langone Medical Center, 145 East 32nd Street, 5th Floor, New York, NY 10016, USA; Department of Pathology and Psychiatry, Alzheimer's Disease Center, Silberstein Alzheimer's Institute, Center of Excellence on Brain Aging, NYU Langone Medical Center, 145 East 32nd Street, 2nd Floor, New York, NY 10016, USA; Alzheimer's Disease Research Unit, CIEN Foundation-Reina Sofia Foundation, Carlos III Institute of Health, Valderrebollo 5, (Complejo Alzheimer), PAU de Vallecas, Madrid 28031, Spain.
Abstract:
Early diagnosis of Alzheimer disease (AD) is one of the major challenges for the prevention of this dementia. The pathologic lesions associated with AD develop many years before the clinical manifestations of the disease become evident, during a likely transitional period between normal aging and the appearance of first cognitive symptoms. AD biomarkers are needed not only to reveal these early pathologic changes but also to monitor progression in cognitive and behavioral decline and brain lesions. PET neuroimaging can reliably assess indirect and direct aspects of the molecular biology and neuropathology of AD. This article reviews the use of [18F] 2-fluoro-2-deoxy-D-glucose-PET and amyloid PET imaging in the early detection of AD.
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