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Related Experiment Video

Updated: Jun 9, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

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Machine learning models for diagnosing Alzheimer's disease using brain cortical complexity.

Shaofan Jiang1,2, Siyu Yang3,4,5, Kaiji Deng1

  • 1Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China.

Frontiers in Aging Neuroscience
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models using fractal dimension (FD) show promise for diagnosing Alzheimer's disease (AD). The MoCA + FD model demonstrated the highest predictive efficiency, suggesting a potential non-invasive diagnostic tool for AD.

Keywords:
Alzheimer’s diseaseMontreal Cognitive Assessmentapolipoprotein Emachine learningmagnetic resonance imaging

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) diagnosis relies on complex assessments.
  • Cortical complexity, measured by fractal dimension (FD), is a potential biomarker for AD.
  • Machine learning models (MLMs) offer novel approaches for disease diagnosis.

Purpose of the Study:

  • To develop and validate MLMs for diagnosing AD using cortical complexity (FD).
  • To evaluate the diagnostic performance of FD-based MLMs compared to other clinical and biological markers.
  • To assess the clinical utility of MLMs for AD diagnosis.

Main Methods:

  • Developed MLMs using FD from 30 significantly altered cortical regions in 296 normal cognition (NC) and 182 AD participants from ADNI.
  • Validated models internally and externally using institutional cohorts (n=66).
  • Evaluated model performance using receiver operating characteristic curves (AUC) and decision curve analysis.

Main Results:

  • FD models demonstrated good accuracy in predicting AD across three cohorts (AUCs: 0.842, 0.808, 0.803).
  • The MoCA + FD model achieved the highest predictive efficiency (AUCs: 0.951, 0.931, 0.955) across all cohorts.
  • The MoCA + FD model exhibited the greatest clinical net benefit.

Conclusions:

  • FD-based MLMs show favorable diagnostic performance for AD.
  • The MoCA + FD model is a highly efficient predictor of AD.
  • This approach offers a potential non-invasive method for AD diagnosis.