Magnetic Resonance Imaging Analysis for Alzheimer's Disease Diagnosis using Artificial Intelligence: Methods, Challenges, and Opportunities.
Authors
Affiliations (3)
Affiliations (3)
- Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China.
- Department of CT & MR, the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China.
- Department of CT & MR, the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China. Electronic address: [email protected].
Abstract
Alzheimer's disease(AD) is the most common cause of dementia and affects millions of people worldwide. The early and accurate diagnosis of AD is crucial for timely intervention and disease management. Magnetic resonance imaging (MRI) is a widely used noninvasive technique for assessing brain structure and function in patients with AD. However, conventional MRI analysis methods are often subjective, time-consuming, and depend on expert knowledge. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for extracting meaningful information from large and complex MRI data and providing an automated and reliable diagnosis of AD. In this review, we summarize the recent advances and challenges in AI MRI for AD diagnosis, focusing on the following aspects: (1) types and characteristics of MRI data used for AD diagnosis; (2) the main AI models and architectures applied to MRI data analysis; (3) performance and evaluation metrics of AI models for AD diagnosis; (4) potential applications and limitations of AI models for AD diagnosis in clinical practice; and (5) future research directions for AI MRI for AD diagnosis. This review aims to provide a comprehensive and updated overview of the field and stimulate further research and advancements in AI-aided MRI for the diagnosis of AD.