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Updated: Aug 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Recent Advances in Screening for Mild Cognitive Impairment and Alzheimer Disease
Allan Anderson1, Matthew Malone1
1Banner Alzheimer's Institute, Tucson, Arizona.
Abstract:
In recent years, scientific understanding of the pathophysiology underlying Alzheimer disease (AD) has advanced substantially. Among the most transformative of discoveries is the existence of biomarkers, such as Aβ42, which can manifest in the central nervous system decades before the onset of disease-associated dementia. By detecting these biological entities early, clinicians can close diagnostic delays and substantially improve outcomes for patients with AD. With prompt news of a diagnosis, patients can initiate long-term planning and devise goals for treatment while their cognition is relatively intact. To differentiate among different forms of dementia, neurologists and supporting clinicians should additionally capitalize on the availability of validated screening tools. Increasingly adopted, tests such as the Montreal Cognitive Assessment yield highly sensitive, specific findings that can improve the standard of care. These results, when paired with insights gleaned from patient histories and clinical examinations, can further inform treatment-decision making and help ensure that patients receive care tailored to their unique circumstances and needs.
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