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Updated: Jun 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Identifying amnestic mild cognitive impairment in primary care: a feasibility study
Rachelle S Doody1, Steven H Ferris, Stephen Salloway
1Alzheimers Disease and Memory Disorders Center, Baylor College of Medicine, Houston, TX 77030, USA. rdoody@bcm.edu
Background And Objective:
Amnestic mild cognitive impairment (aMCI), characterized by episodic memory impairment in the absence of clinical dementia, often represents a transitional stage between normal aging and Alzheimer's disease (AD). It is not known if non-expert primary-care physicians (PCPs) can differentiate individuals with no cognitive impairment (NCI), aMCI and mild AD in a primary-care practice setting. This study develops an approach to this question, which is necessary for aMCI to become a treatment target.
Methods:
Fourteen experts assessed subjects with memory complaints in terms of their laboratory test results, magnetic resonance imaging findings and scores on the Mini-Mental State Examination, adapted Clinical Dementia Rating Scale and Alzheimer's Disease Assessment Scale-cognitive subscale Delayed Word Recall before designating each subject as having NCI, aMCI or AD. Subjects agreed upon by a consensus committee were assigned to non-expert PCPs who, following brief training, assessed them using the same clinical information and utilizing the same assessment instruments. The chance-corrected inter-rater reliability (expert versus non-expert) measure κ, based on binary outcome (aMCI/not-aMCI), was estimated.
Results:
The study recruited 119 evaluable subjects (50 aMCI, 27 mild AD and 42 NCI) and demonstrated fair to moderate agreement (κ = 0.423) between experts and non-experts in designation of aMCI. The percent agreement was 72.3%, sensitivity 62.0% and specificity 79.7%. Overall, non-experts under-rated the level of impairment compared with experts.
Conclusion:
This study established the feasibility of making the aMCI designation in the community and identified some likely sources of error. The results suggest that when drugs with clear benefit for aMCI patients are developed, community-based PCPs, with additional, more optimized training, will be able to accurately identify those patients who should receive treatment.
Insights
Primary care physicians can differentiate between no cognitive impairment, mild cognitive impairment, and Alzheimer's disease. With training, PCPs can identify patients for future treatments.
Area of Science:
- Neurology
- Geriatrics
- Cognitive Science
Background:
- Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease (AD).
- Accurate differentiation of aMCI is crucial for developing targeted treatments.
Purpose of the Study:
- To determine if non-expert primary care physicians (PCPs) can differentiate between no cognitive impairment (NCI), aMCI, and mild AD.
- To develop an approach for PCP-based aMCI identification.
Main Methods:
- Experts diagnosed 119 subjects (NCI, aMCI, mild AD) using clinical data and cognitive tests.
- Non-expert PCPs, after brief training, assessed the same subjects.
- Inter-rater reliability (expert vs. non-expert) for aMCI designation was calculated using Cohen's kappa (κ).
Main Results:
- Fair to moderate agreement (κ = 0.423) was found between experts and non-expert PCPs in diagnosing aMCI.
- Percent agreement was 72.3%, with 62.0% sensitivity and 79.7% specificity for aMCI detection by non-experts.
- Non-expert PCPs tended to under-rate the level of cognitive impairment compared to experts.
Conclusions:
- It is feasible for community-based PCPs to identify individuals with aMCI.
- Optimized training can enhance PCP accuracy in diagnosing aMCI.
- This capability is essential for future therapeutic interventions targeting aMCI.
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