Neurology

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

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PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes ...

A six degrees-of-freedom cable-driven robotic platform for head-neck movement.

This paper introduces a novel cable-driven robotic platform that enables six degrees-of-freedom (DoF...

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative ele...

Outcome measures applied to robotic assistive technology for people with cerebral palsy: a pilot study.

The application of robotic devices is being used as Assistive Technology (AT) for improving rehabili...

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the ...

Editorial for the Special Issue "Sensing Brain Activity Using EEG and Machine Learning".

Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic o...

vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this stu...

Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers.

Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid N...

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific reha...

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes.

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for...

Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence.

Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating s...

Positive Emotional Responses to Socially Assistive Robots in People With Dementia: Pilot Study.

BACKGROUND: Interventions and care that can evoke positive emotions and reduce apathy or agitation a...

Bridging the gap: robotic applications in cerebral aneurysms neurointerventions - a systematic review.

Cerebral aneurysm is a life-threatening condition, which requires high precision during the neurosur...

Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review.

BACKGROUND: Among neurological pathologies, cerebral palsy and stroke are the main contributors to w...

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions ...

A Comprehensive Review: Robot-Assisted Treatments for Gait Rehabilitation in Stroke Patients.

Robot-assisted gait training (RAGT) is at the cutting edge of stroke rehabilitation, offering a grou...

Empirical modeling and prediction of neuronal dynamics.

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks ...

Posterior circulation ischemic stroke: radiomics-based machine learning approach to identify onset time from magnetic resonance imaging.

PURPOSE: Posterior circulation ischemic stroke (PCIS) possesses unique features. However, previous s...

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