AD Diagnosis

Alzheimer's disease (AD) diagnosis research focuses on developing accurate and cost-effective methods for early detection and sub-type classification. Current efforts leverage diverse data modalities, including MRI, PET scans, cerebrospinal fluid analysis via Raman spectroscopy, and speech analysis, employing deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and increasingly, topological machine learning and reinforcement learning approaches to improve diagnostic accuracy and handle open-set recognition challenges. These advancements aim to improve patient care by enabling earlier interventions and more personalized treatment strategies, ultimately reducing the burden of this debilitating disease.