A review from arXiv (2608.14731v1) examines transfer learning (TL) for Alzheimer's disease (AD) identification, highlighting its ability to improve diagnostic accuracy with limited neuroimaging data w…
A 2024 study of nearly 9 million U.S. death certificates found that taxi and ambulance drivers had the lowest risk of dying from Alzheimer's disease among 443 occupations, with about 1 in 100 dying fr…
Researchers propose MPP-GNN, a Meta Probabilistic Pooling Graph Neural Network that adaptively partitions brain functional networks per subject for Alzheimer's disease classification, achieving the hi…
A new framework, VMoGE (Variational Mixture of Graph Neural Experts), promises to enhance the accuracy of dementia diagnosis using multi-band EEG analysis, achieving an AUC of 0.89 in distinguishing h…
Researchers have developed End-Net, a convolutional neural network for multi-class MRI classification of neurological disorders, outperforming existing models in accuracy and generalization. The syste…
Human memory constantly updates relationship statuses and recent events, a process called memory updating that is crucial for social interactions and daily decisions. AI systems struggle with this upd…
Researchers propose REVEAL++, a differentiable phenotypic grouping method for vision-language retinal modeling that improves Alzheimer's disease risk prediction by treating phenotypic similarity as a …
Researchers propose ProMUSE, a progressive multi-modal uncertainty-guided network for Alzheimer's disease classification that adaptively determines when additional imaging modalities are needed, reduc…
Researchers developed MEMOR-E, a mobile quadruped robot with a tablet interface that assists Alzheimer's patients and caregivers with medication reminders, routine guidance, and memory-oriented intera…