Neurology

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

11,888 articles
Stay Ahead - Weekly Neurology research updates
Subscribe
Browse Categories
Showing 1030-1050 of 11,888 articles
DC-ASTGCN: EEG Emotion Recognition Based on Fusion Deep Convolutional and Adaptive Spatio-Temporal Graph Convolutional Networks.

Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there...

An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network.

Benefiting from the high-temporal resolution of electroencephalogram (EEG), EEG-based emotion recogn...

Design and Implementation of Pavlovian Associative Memory Based on DNA Neurons.

In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (D...

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and ...

Radiomics across modalities: a comprehensive review of neurodegenerative diseases.

Radiomics allows extraction from medical images of quantitative features that are able to reveal tis...

Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug-protein-disease network-based deep learning.

Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillat...

Multimodal imaging platform for enhanced tumor resection in neurosurgery: integrating hyperspectral and pCLE technologies.

PURPOSE: This work presents a novel multimodal imaging platform that integrates hyperspectral imagin...

Reinforcement learning for safe autonomous two-device navigation of cerebral vessels in mechanical thrombectomy.

PURPOSE: Autonomous systems in mechanical thrombectomy (MT) hold promise for reducing procedure time...

Deep learning assisted retinal microvasculature assessment and cerebral small vessel disease in Fabry disease.

PURPOSE: The aim of this study was to assess retinal microvascular parameters (RMPs) in Fabry diseas...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observation...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest...

LGFormer: integrating local and global representations for EEG decoding.

Electroencephalography (EEG) decoding is challenging because of its temporal variability and low sig...

Does infection alter antifungal distribution: an animal model exploring pharmacokinetic changes.

AIM: Assessing the disseminated meningitis caused by in Wistar rats and its impact on antifungal di...

A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score.

INTRODUCTION/AIMS: The adoption of telemedicine is generally considered as advantageous for patients...

Niuhuang jiedu prescription alleviates realgar-induced dopaminergic and GABAergic neurotoxicity in Caenorhabditis elegans.

ETHNOPHARMACOLOGICAL RELEVANCE: Niuhuang Jiedu (NHJD) is a Chinese medicine prescription containing ...

Automated segmentation of the dorsal root ganglia in MRI.

The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in soma...

An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients.

Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of re...

Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

This work presents the development of on-chip machine learning (ML) classifiers for implantable neur...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common et...

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance ...

Browse Categories