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Updated: Oct 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data.

Jafar Zamani1, Ali Sadr2, Amir-Homayoun Javadi3,4

  • 1School of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran.

Scientific Reports
|January 20, 2022
PubMed
Summary

This study introduces an evolutionary algorithm (EA) optimization method for diagnosing early mild cognitive impairment (EMCI) using structural MRI. The novel approach achieved over 93% accuracy, outperforming previous methods for early Alzheimer's disease detection.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Neurology

Background:

  • Alzheimer's disease (AD) is the leading cause of dementia, necessitating early diagnosis for effective intervention.
  • Structural magnetic resonance imaging (sMRI) measures brain atrophy, a potential biomarker for neurodegeneration.
  • Current classification methods struggle with diagnosing early stages of mild cognitive impairment (EMCI).

Purpose of the Study:

  • To evaluate the efficacy of evolutionary algorithms (EA) in diagnosing EMCI compared to cognitively normal (CN) individuals.
  • To determine if optimized single-modality biomarkers can achieve high classification accuracy for EMCI detection.

Main Methods:

  • Utilized structural MRI data from EMCI (n=54) and CN (n=56) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
  • Employed three automatic brain segmentation methods to extract volumetric parameters.
  • Applied optimization based on evolutionary algorithms (EA) for classification.

Main Results:

  • Achieved a classification accuracy exceeding 93% for distinguishing EMCI from CN participants.
  • This accuracy surpasses previously reported results using single or multiple imaging modalities.
  • Demonstrated that optimized single biomarkers can yield high diagnostic accuracy.

Conclusions:

  • Evolutionary algorithm optimization is an effective tool for diagnosing early mild cognitive impairment (EMCI).
  • High classification accuracy for EMCI can be achieved using a single imaging biomarker with appropriate optimization.
  • This approach holds promise for improving early detection of Alzheimer's disease.