Geriatrics

Alzheimer's Disease

Latest AI and machine learning research in alzheimer's disease for healthcare professionals.

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Geriatrics Subcategories: Alzheimer's Disease Medicare
Showing 904-924 of 11,654 articles
Self-Supervised Longitudinal Neighbourhood Embedding.

Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and functio...

Long-term effect of tocilizumab on left ventricular hypertrophy and systolic dysfunction in AA amyloidosis with rheumatoid arthritis.

Because cardiac involvement of amyloid A (AA) is not frequent, little is known about the effects of ...

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.

The rise of high-throughput experiments has transformed how scientists approach biological questions...

Dementia care, robot pets, and aliefs.

Studies have shown that using robot pets in dementia care contributes to a reduction in loneliness a...

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

Deep neural networks (DNNs) capture complex relationships among variables, however, because they req...

Deep learning-based model for diagnosing Alzheimer's disease and tauopathies.

AIMS: This study aimed to develop a deep learning-based model for differentiating tauopathies, inclu...

Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagn...

Detection of dementia on voice recordings using deep learning: a Framingham Heart Study.

BACKGROUND: Identification of reliable, affordable, and easy-to-use strategies for detection of deme...

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker ...

Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.

Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on vis...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in car...

Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.

OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of ...

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy p...

Predicting mutant outcome by combining deep mutational scanning and machine learning.

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional...

Neuroinflammation and Alzheimer's Disease: A Machine Learning Approach to CSF Proteomics.

In Alzheimer's disease (AD), the contribution of pathophysiological mechanisms other than amyloidosi...

Generation of synthetic PET images of synaptic density and amyloid from F-FDG images using deep learning.

PURPOSE: Positron emission tomography (PET) imaging with various tracers is increasingly used in Alz...

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion.

The increasing population of the elderly and motion-impaired people brings a huge challenge to our s...

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.

A growing body of evidence currently proposes that deep learning approaches can serve as an essentia...

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