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Deep Learning Approaches for Early Prediction of Conversion From Mci to Ad Using Mri and Clinical Data: A Systematic Review Publisher



Valizadeh G1 ; Elahi R1, 2 ; Hasankhani Z3 ; Rad HS1 ; Shalbaf A4
Authors
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Authors Affiliations
  1. 1. Quantitative MR Imaging and Spectroscopy Group, Advanced Medical Technologies and Equipment Institute, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
  3. 3. Department of Para-Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran
  4. 4. Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Source: Archives of Computational Methods in Engineering Published:2025


Abstract

Due to the absence of definitive treatment for Alzheimer’s disease (AD), slowing its development is essential. Accurately predicting the conversion of mild cognitive impairment (MCI) -a potential early stage of AD- to AD is challenging due to the subtle distinctions between individuals who will develop AD and those who will not. As an increasing body of evidence in the literature suggests, advanced magnetic resonance imaging (MRI) scans, coupled with high-performance computing techniques and novel deep learning techniques, have revolutionized the ability to predict MCI to AD conversion. This study systematically reviewed the publications from 2013 to 2023 (July) to investigate the contribution of deep learning in predicting the MCI conversion to AD, concentrating on the MRI data (structural or functional) and clinical information. The search conducted across seven different databases yielded a total of 2273 studies. Out of these, 78 relevant studies were included, which were thoroughly reviewed, and their essential details and findings were extracted. Furthermore, this study comprehensively explores the challenges associated with predicting the conversion from MCI to AD using deep learning methods with MRI data. Also, it identifies potential solutions to address these challenges. The research field of predicting MCI to AD conversion from MRI data using deep learning techniques is constantly evolving. There is an increasing focus on employing explainable approaches to improve transparency in the analysis process. The paper concludes with an overview of future perspectives and recommends conducting further studies in MCI to AD conversion prediction using deep learning methods. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024.
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