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Updated: Dec 20, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Early Detection of Mild Cognitive Impairment (MCI) in Primary Care
M N Sabbagh1, M Boada, S Borson
1Marwan N. Sabbagh, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA, sabbagm@ccf.org; Tel.: (702) 483-6029; Fax: (702) 722-6584.
Abstract:
Mild cognitive impairment (MCI) is significantly misdiagnosed in the primary care setting due to multi-dimensional frictions and barriers associated with evaluating individuals' cognitive performance. To move toward large-scale cognitive screening, a global panel of clinicians and cognitive neuroscientists convened to elaborate on current challenges that hamper widespread cognitive performance assessment. This report summarizes a conceptual framework and provides guidance to clinical researchers and test developers and suppliers to inform ongoing refinement of cognitive evaluation. This perspective builds upon a previous article in this series, which outlined the rationale for and potentially against efforts to promote widespread detection of MCI. This working group acknowledges that cognitive screening by default is not recommended and proposes large-scale evaluation of individuals with a concern or interest in their cognitive performance. Such a strategy can increase the likelihood to timely and effective identification and management of MCI. The rising global incidence of AD demands innovation that will help alleviate the burden to healthcare systems when coupled with the potentially near-term approval of disease-modifying therapies. Additionally, we argue that adequate infrastructure, equipment, and resources urgently should be integrated in the primary care setting to optimize the patient journey and accommodate widespread cognitive evaluation.
Insights
Mild cognitive impairment (MCI) screening faces challenges in primary care. A new framework suggests evaluating individuals with concerns to improve early detection and management of cognitive decline.
Area of Science:
- Neurology
- Gerontology
- Public Health
Background:
- Mild cognitive impairment (MCI) is frequently misdiagnosed in primary care settings.
- Barriers in cognitive performance evaluation hinder widespread screening for MCI.
Purpose of the Study:
- To address challenges in large-scale cognitive screening.
- To propose a conceptual framework and guidance for refining cognitive evaluations.
- To inform clinical researchers, test developers, and suppliers.
Main Methods:
- Convening a global panel of clinicians and cognitive neuroscientists.
- Elaborating on current challenges in cognitive performance assessment.
- Building upon previous work on the rationale for MCI detection.
Main Results:
- A conceptual framework for cognitive evaluation was developed.
- Guidance for improving cognitive assessment in primary care was provided.
- The importance of evaluating individuals with cognitive concerns was emphasized.
Conclusions:
- Large-scale cognitive evaluation for individuals with concerns can enhance MCI identification and management.
- Integrating infrastructure, equipment, and resources in primary care is crucial.
- Innovation in cognitive assessment is needed, especially with rising Alzheimer's disease incidence and new therapies.
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