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.
Papers
Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts
Wupadrasta Santosh Kumar, Sayali Rajendra Bhutare, Neelam Sinha, Thomas Gregor Issac
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies
Mohammad Hossein Najafi, Mohammad Morsali, Mohammadmahdi Vahediahmar, Saeed Bagheri Shouraki
Classification of Alzheimer's Dementia vs. Healthy subjects by studying structural disparities in fMRI Time-Series of DMN
Sneha Noble, Chakka Sai Pradeep, Neelam Sinha, Thomas Gregor Issac
Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews
Wen Wu, Chao Zhang, Philip C. Woodland