Geriatrics

Latest AI and machine learning research in geriatrics for healthcare professionals.

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Subcategories: Alzheimer's Disease Medicare
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[A national survey from the French Society of Pathology on research and application of artificial intelligence in pathology].

The 2030 French Innovation Healthcare national plan defined "Digital Healthcare" as one of its prior...

Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.

BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-r...

EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has be...

Conceptual understanding and cognitive patterns construction for physical education teaching based on deep learning algorithms.

To improve students' understanding of physical education teaching concepts and help teachers analyze...

Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking pro...

Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.

The gut microbiome, recognized as a critical component in the development of chronic diseases and ag...

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent ...

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the ...

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatmen...

BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization.

The precise spatiotemporal expression of long noncoding RNAs (lncRNAs) plays a pivotal role in biolo...

Correlation between individual thigh muscle volume and grip strength in relation to sarcopenia with automated muscle segmentation.

INTRODUCTION: Grip strength serves as a significant marker for diagnosing and assessing sarcopenia, ...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease ...

3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer's disease via machine learning.

Alzheimer's Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million peopl...

Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.

BACKGROUND: To diagnose Alzheimer disease (AD), individuals are classified according to the severity...

An experimental framework for conjoint measures of olfaction, navigation, and motion as pre-clinical biomarkers of Alzheimer's disease.

Elucidating Alzheimer's disease (AD) prodromal symptoms can resolve the outstanding challenge of ear...

Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity th...

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradu...

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