Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.

Journal: Alzheimer's research & therapy
Published Date: Aug 7, 2025

Abstract

BACKGROUND: Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical management and selecting proper therapeutic interventions. Structural magnetic resonance imaging (MRI) markers have been widely investigated for predicting the conversion of MCI to AD, and recent advances in deep learning (DL) methods offer enhanced capabilities for identifying subtle neurodegenerative changes over time.

Authors

  • Sepehr Aghajanian
    Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Fateme Mohammadifard
    Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Ida Mohammadi
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
  • Shahryar Rajai Firouzabadi
    Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), PO box 14665-354, Tehran, Iran.
  • Ali Baradaran Bagheri
    Department of Neurosurgery, Alborz University of Medical Sciences, Karaj, Iran.
  • Elham Moases Ghaffary
    School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri-Kansas City, Kansas City, MO, USA.
  • Omid Mirmosayyeb
    Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

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