Neurology

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

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Gait training using powered robotic exoskeleton for a person with spinal cord injury: a case report.

INTRODUCTION: Robotic Exoskeleton-assisted gait training is an emerging approach in spinal cord inju...

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations.

Recent advances in human genomics have revealed that missense mutations in a single protein can lead...

Deep Learning-Based Acceleration in MRI: Current Landscape and Clinical Applications in Neuroradiology.

Magnetic resonance imaging (MRI) is a cornerstone of neuroimaging, providing unparalleled soft-tissu...

Investigating Membership Inference Attacks against CNN Models for BCI Systems.

As Deep Learning (DL) algorithms become more widely adopted in healthcare applications, there is a g...

Evaluating the impact of an AI-powered chatbot on epilepsy education and stigma reduction: A pre-post intervention study using EpiloBot.

OBJECTIVE: Effective epilepsy management requires accurate epilepsy knowledge, active patient engage...

Wavelet-Attention deep model for pediatric ADHD diagnosis via EEG.

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in childr...

Comparison of Generative Artificial Intelligence and Student-Generated Veterinary Handouts.

Generative artificial intelligence (gAI) is becoming increasingly prevalent in our daily lives. Stud...

Electrical stimulation of stem cell-derived human neural networks for evaluating anti-seizure medications.

OBJECTIVE: Current preclinical epilepsy drug screening relies on animal models that poorly reflect h...

Application of machine learning in EEG-based dementia diagnosis: Classification and differential diagnosis.

BackgroundElectroencephalogram (EEG) is a promising, non-invasive method for identifying the presenc...

EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.

Speech imagery is a nascent paradigm that is receiving widespread attention in current brain-compute...

Connectomic stroke lesion measures provide no benefit over basic spatial lesion features in the prognosis of global stroke outcome measures.

The prediction of stroke outcome from imaging markers could be used to guide individualized therapeu...

Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.

Cognitive impairment is a frequent complication of Parkinson's disease (PD), affecting up to half of...

Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.

BACKGROUND: While codes suffice for identifying stroke events in surveillance, accurately classifyi...

Methinks AI software for identifying large vessel occlusion in non-contrast head CT: A pilot retrospective study in American population.

BackgroundNon-contrast computed tomography (NCCT) is the first image for stroke assessment, but its ...

Ferroelectric Charged Domain-Wall Synapse for Neuromorphic Computing.

Inspired by brain neural networks, integrated memory-computing devices are critical to meet the dema...

Hijacked Brain in Modern Obesity: Cue, Habit, Addiction, Emotion, and Restraint as Targets for Personalized Digital Therapy and Electroceuticals.

The global obesity epidemic can no longer be explained by personal choice or caloric excess alone. M...

Machine learning identification of key genes in cardioembolic stroke and atherosclerosis: their association with pan-cancer and immune cells.

BACKGROUND: Cardioembolic stroke (CS) and atherosclerosis (AS) are closely related diseases. Ferropt...

Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients.

BACKGROUND: Coronary artery disease (CAD) remains a leading cause of global mortality, with stroke c...

Perceived social support in the daily life of people with Parkinson's disease: a distinct role and potential classifier.

Motor outcomes in Parkinson's disease (PD) have long been the primary diagnostic criteria and treatm...

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