Alzheimer'S Disease

Alzheimer's disease (AD) research focuses on improving early and accurate diagnosis to facilitate timely intervention. Current efforts utilize diverse data sources (MRI, EEG, speech, genetics) and advanced machine learning models, including convolutional neural networks (CNNs), transformers, and Bayesian networks, to identify disease biomarkers and predict progression. These advancements aim to enhance diagnostic accuracy, personalize treatment strategies, and ultimately improve patient outcomes, though challenges remain in data standardization, model interpretability, and generalizability across diverse populations. The integration of multimodal data and explainable AI techniques is a key trend to improve both predictive power and clinical utility.