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 715-735 of 11,654 articles
Rational design of functional amyloid fibrillar assemblies.

Amyloid fibrillar assemblies, originally identified as pathological entities in neurodegenerative di...

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.

BACKGROUND: Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important no...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease am...

Social robotics to support older people with dementia: a study protocol with Paro seal robot in an Italian Alzheimer's day center.

INTRODUCTION: The aging of the population and the high incidence of those over 80 lead to an inevita...

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.

Machine learning (ML) could have advantages over traditional statistical models in identifying risk ...

EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning.

Multiple sequence alignments (MSAs) are the workhorse of molecular evolution and structural biology ...

Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.

To estimate causal effects, analysts performing observational studies in health settings utilize sev...

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and...

Deep learning application for the classification of Alzheimer's disease using F-flortaucipir (AV-1451) tau positron emission tomography.

The positron emission tomography (PET) with F-flortaucipir can distinguish individuals with mild cog...

Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands.

The clock drawing test is a simple and inexpensive method to screen for cognitive frailties, includi...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth i...

Implementing PainChek and PARO to Support Pain Assessment and Management in Residents with Dementia: A Qualitative Study.

BACKGROUND: Pain is a common problem but often undiagnosed and untreated in people with dementia.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics...

Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.

Hippocampal subregions differ in specialization and vulnerability to cell death. Neuron death and hi...

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers.

Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely dif...

A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease.

PURPOSE: Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that ...

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset.

For automatic EEG diagnosis, this paper presents a new EEG data set with well-organized clinical ann...

Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.

Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance...

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