Neurology

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

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Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke.

PURPOSE: Clinical risk scores are essential for predicting outcomes in stroke patients. The advancem...

Migraine headache (MH) classification using machine learning methods with data augmentation.

Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges ...

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients...

Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis.

Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based c...

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally pro...

Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consumi...

Fast reconstruction of EEG signal compression sensing based on deep learning.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG...

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple ...

Construction of an aerolysin-based multi-epitope vaccine against an machine learning and artificial intelligence-supported approach.

, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic ...

Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees.

Variations in muscular contraction are known to significantly impact the quality of the generated EM...

Multi Degree of Freedom Hybrid FES and Robotic Control of the Upper Limb.

Individuals who have suffered a spinal cord injury often require assistance to complete daily activi...

Patient portal messages to support an age-friendly health system for persons with dementia.

BACKGROUND: Patient portal secure messaging can support age-friendly dementia care, yet little is kn...

Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.

Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic te...

SlumberNet: deep learning classification of sleep stages using residual neural networks.

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential f...

Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.

The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is th...

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.

OBJECTIVE: In this study, we present a novel biomimetic deep learning network for epileptic spasms a...

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of trac...

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