Deep Learning-Based Diagnosis of Alzheimer's Disease

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Imaging

Background:

• Alzheimer's disease (AD) is a leading cause of dementia and mortality, characterized by irreversible cognitive decline.
• Current diagnostic methods for AD face limitations, necessitating advanced approaches.
• Deep learning (DL) has emerged as a powerful tool in various AI domains, including medical research.

Purpose of the Study:

• To review the state-of-the-art in Alzheimer's disease diagnosis using deep learning techniques.
• To summarize recent trends, findings, biomarkers, and datasets relevant to DL-based AD diagnosis.
• To identify current challenges and future directions in the field.

Main Methods:

• Comprehensive literature review of studies published since 2014 focusing on deep learning for AD diagnosis.
• Analysis of various deep learning architectures and their performance metrics.
• Exploration of different neuroimaging and non-imaging biomarkers utilized in DL models.

Main Results:

• Deep learning models demonstrate superior accuracy in AD diagnosis compared to traditional machine learning methods.
• Significant progress has been made in utilizing DL for early and accurate detection of Alzheimer's disease.
• A growing body of research highlights the potential of DL across diverse datasets and biomarkers.

Conclusions:

• Deep learning holds significant promise for improving Alzheimer's disease diagnosis.
• Further research is needed to address challenges related to data heterogeneity, model interpretability, and clinical validation.
• Continued development in DL techniques could revolutionize the diagnostic landscape for AD.