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Updated: Mar 29, 2026

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Early detection of cognitive disorders: Follow-up study.
Summary
A new Alzheimer's disease (AD) screening test shows 50% sensitivity and 90.9% specificity in primary care. Further validation in larger cohorts is recommended for early cognitive disorder detection.
Area of Science:
- Neurology
- Geriatrics
- Primary Health Care Research
Background:
- A novel Cuetos-Vega test has been developed for early detection of Alzheimer's disease (AD).
- This rapid and simple test aims to diagnose cognitive disorders in early phases.
- The current study evaluates its performance in primary health care (PHC) settings.
Purpose of the Study:
- To assess the sensitivity, specificity, and predictive values of the Cuetos-Vega test.
- To validate the test's utility in a primary health care context.
- To evaluate the test's diagnostic accuracy for early Alzheimer's disease symptoms.
Main Methods:
- A follow-up study involving asymptomatic individuals aged 66-75.
- Random selection and administration of the Cuetos-Vega test at a PHC center.
- Repeat testing after 32 months, including the Pfeiffer test for cognitive assessment.
Main Results:
- The study included 20 participants (50% male), average age 71.5 years.
- After 32 months, the test demonstrated 50% sensitivity and 90.9% specificity.
- Positive predictive value was 75%, negative predictive value 76.9%, and overall accuracy 76.4%.
Conclusions:
- The Cuetos-Vega test shows potential for detecting mild cognitive impairment and early AD symptoms.
- The findings suggest the test is viable for primary care screening.
- A larger study cohort is necessary to enhance the internal validity and confirm these results.

