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Updated: May 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild cognitive impairment in older adults
1Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA. geda.yonas@mayo.edu
Abstract:
Mild cognitive impairment (MCI) is the intermediate stage between the cognitive changes of normal aging and dementia. MCI is important because it constitutes a high risk group for dementia. Ideally, prevention strategies should target individuals who are not even symptomatic. Indeed, the field is now moving towards identification of asymptomatic individuals who have underlying Alzheimer's disease (AD) pathology that can be detected using biomarkers and neuroimaging technologies. To this effect, the Alzheimer's Association and the National Institute on Aging have developed a new classification scheme that has categorized AD into a preclinical phase (research category), MCI due to AD, and dementia of Alzheimer's type. However, there are also ongoing research studies to understand high-risk groups for non-Alzheimer's dementia.
Insights
Mild cognitive impairment (MCI) is a high-risk stage for dementia. Identifying preclinical Alzheimer's disease (AD) in asymptomatic individuals using biomarkers and neuroimaging is crucial for future prevention strategies.
Area of Science:
- Neurology
- Gerontology
- Biomarker Research
Background:
- Mild cognitive impairment (MCI) represents a critical intermediate stage between normal aging and dementia.
- Individuals with MCI are at a significantly elevated risk of progressing to dementia.
- Early identification of at-risk individuals is paramount for developing effective prevention strategies.
Purpose of the Study:
- To discuss the significance of MCI as a high-risk group for dementia.
- To highlight the shift towards identifying asymptomatic individuals with underlying Alzheimer's disease (AD) pathology.
- To introduce the new classification scheme for AD by the Alzheimer's Association and NIA.
Main Methods:
- Review of current research trends in cognitive decline and dementia.
- Discussion of biomarker and neuroimaging technologies for detecting AD pathology.
- Explanation of the new NIA-AA research framework for classifying AD stages.
Main Results:
- MCI is a key indicator of increased dementia risk.
- Biomarkers and neuroimaging enable detection of preclinical AD in asymptomatic individuals.
- A new classification system categorizes AD into preclinical, MCI due to AD, and dementia stages.
Conclusions:
- The field is advancing towards early detection of AD pathology before symptomatic stages.
- The new classification scheme provides a framework for research into AD progression.
- Research continues to explore risk factors for non-Alzheimer's dementias.
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