Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review

Area of Science:

• Neurology
• Computer Science
• Artificial Intelligence

Background:

• Alzheimer's disease (AD) is a progressive neurological disorder causing cognitive decline, memory loss, and death.
• Early diagnosis is critical for improving patient survival rates but traditional methods are challenging.
• Computer-aided diagnosis (CAD) systems using AI and deep learning offer rapid AD detection.

Purpose of the Study:

• To survey various modalities, feature extraction, datasets, machine learning techniques, and validation methods for AD detection.
• To identify challenges in current AI-based AD detection literature.
• To emphasize the importance of interpretability and explainability in deep learning models for AD diagnosis.

Main Methods:

• Systematic review of 116 relevant research papers from major scientific repositories.
• Categorization of studies based on modalities, feature extraction, datasets, machine learning algorithms, and validation approaches.
• Analysis of findings presented in tabular format for clarity and ease of reference.

Main Results:

• A comprehensive overview of diverse AI and deep learning techniques applied to Alzheimer's disease detection.
• Identification of common datasets, feature extraction strategies, and validation metrics used in the field.
• Summary of challenges and limitations in existing research, including a focus on model interpretability.

Conclusions:

• AI and deep learning show significant promise for rapid and accurate Alzheimer's disease detection.
• Addressing challenges related to data heterogeneity, standardization, and model explainability is crucial for future advancements.
• Further research is needed to guide the development of robust and interpretable AI tools for clinical application in diagnosing neurological disorders like AD.