Latest AI and machine learning research in dementia for healthcare professionals.
Alzheimer's disease (AD) is characterized by substantial clinical and biological heterogeneity, with...
Early identification of Alzheimer's disease (AD) remains challenging because established assessment ...
When there is not enough labeled data to properly train deep learning models, transfer learning can ...
Multimodal biomarkers have transformed Alzheimer's disease research, but the incremental contributio...
The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor ...
Early identification of Alzheimer's disease and related dementias (ADRD) remains challenging despite...
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely...
Background: The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely us...
Electroencephalography (EEG) is a promising, non-invasive, and cost-effective modality for Alzheimer...
Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiol...
Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particul...
Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease pr...
Early identification of cognitive impairment remains challenging in settings where comprehensive cog...
Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-re...
Background and Objectives: Word-finding difficulty is common in healthy aging and in neurologic diso...
Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGL...
In Alzheimer's disease (AD), misfolded proteins emerge across the entire brain in structured, yet no...
Abstract EEG foundation models (EEG-FMs) are evaluated almost entirely on disease-discrimination acc...
Background and Objectives: Subjective cognitive decline (SCD), self-reported worsening confusion or ...
Explainability methods applied to deep learning models for Alzheimer's disease neuroimaging produce ...
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroi...