Neurology

Dementia

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

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Fully automated MRI-based analysis of the locus coeruleus in aging and Alzheimer's disease dementia using ELSI-Net.

INTRODUCTION: The locus coeruleus (LC) is linked to the development and pathophysiology of neurodege...

Creating Chemiluminescence Signature Arrays Coupled with Machine Learning for Alzheimer's Disease Serum Diagnosis.

Although omics and multi-omics approaches are the most used methods to create signature arrays for l...

Association of RDoC dimensions with brain transcriptional profiles in Alzheimer's disease.

INTRODUCTION: Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across al...

A Multi-Label Deep Learning Model for Detailed Classification of Alzheimer's Disease.

BACKGROUND: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effect...

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric...

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra.

Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive...

Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.

Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. T...

Biologically Enhanced Machine Learning Model to uncover Novel Gene-Drug Targets for Alzheimer's Disease.

Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-...

Combining Real-Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study.

Looking at caregivers' faces is important for early social development, and there is a concomitant i...

Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores...

A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by dest...

Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network.

BACKGROUND: Dementia associated with Alzheimer's disease (AD) is a neurological disorder. AD is a pr...

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.

Dynamic prediction models capable of retaining accuracy by evolving over time could play a significa...

Accurate and Efficient Algorithm for Detection of Alzheimer Disability Based on Deep Learning.

BACKGROUND/AIMS: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely ...

segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MR...

Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.

The interactions between long noncoding RNA (lncRNA) and microRNA (miRNA) play critical roles in lif...

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our abili...

Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease.

Augmenting traditional genome-wide association studies (GWAS) with advanced machine learning algorit...

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progr...

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