Title:Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications
Volume: 23
Issue: 14
Author(s): Zahra Batool, ShanShan Hu, Mohammad Amjad Kamal*, Nigel H. Greig and Bairong Shen*
Affiliation:
- Center for High Altitude Medicine, Institutes for Systems Genetics, Frontiers Science Center for Disease-related
Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- King Fahd Medical
Research Center, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Department of Pharmacy, Faculty of Health
and Life Sciences, Daffodil International University, Birulia, Savar, Dhaka -1216, Bangladesh
- Centre for Global
Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences,
Chennai, Tamil Nadu, India
- Enzymoics, Hebersham, NSW 2770, Novel Global Community Educational Foundation,
Australia
- Center for High Altitude Medicine, Institutes for Systems Genetics, Frontiers Science Center for Disease-related
Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
Keywords:
Artificial Intelligence, Alzheimer's disease, neurodegeneration, magnetic resonance imaging, deep learning models, convolution neural networks (CNNs), impaired cognition.
Abstract: Neurological disorders are marked by neurodegeneration, leading to impaired cognition,
psychosis, and mood alterations. These symptoms are typically associated with functional changes in
both emotional and cognitive processes, which are often correlated with anatomical variations in the
brain. Hence, brain structural magnetic resonance imaging (MRI) data have become a critical focus
in research, particularly for predictive modeling. The involvement of large MRI data consortia, such
as the Alzheimer's Disease Neuroimaging Initiative (ADNI), has facilitated numerous MRI-based
classification studies utilizing advanced artificial intelligence models. Among these, convolutional
neural networks (CNNs) and non-convolutional artificial neural networks (NC-ANNs) have been
prominently employed for brain image processing tasks. These deep learning models have shown
significant promise in enhancing the predictive performance for the diagnosis of neurological disorders,
with a particular emphasis on Alzheimer's disease (AD). This review aimed to provide a comprehensive
summary of these deep learning studies, critically evaluating their methodologies and
outcomes. By categorizing the studies into various sub-fields, we aimed to highlight the strengths
and limitations of using MRI-based deep learning approaches for diagnosing brain disorders. Furthermore,
we discussed the potential implications of these advancements in clinical practice, considering
the challenges and future directions for improving diagnostic accuracy and patient outcomes.
Through this detailed analysis, we seek to contribute to the ongoing efforts in harnessing AI for better
understanding and management of AD.
