Neurology

Frontotemporal EEG to guide sedation in COVID-19 related acute respiratory distress syndrome.

OBJECTIVE: To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 2019 (COVID-19) patients receiving neuromuscular blocking agent.

Deep Learning for Robust Decomposition of High-Density Surface EMG Signals.

Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. ...

Preictal state detection using prodromal symptoms: A machine learning approach.

A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We...

Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.

BACKGROUND: Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investig...

An objective structural and functional reference standard in glaucoma.

The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In th...

Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Vete...

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correcti...

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction.

BACKGROUND: Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle...

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases.

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are...

Use of Machine Learning to Re-Assess Patterns of Multivariate Functional Recovery after Fluid Percussion Injury: Operation Brain Trauma Therapy.

Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Tran...

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. H...

A novel retinal ganglion cell quantification tool based on deep learning.

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major r...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors deter...

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.

Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficul...

Behavioral and neurophysiological effects of an intensified robot-assisted therapy in subacute stroke: a case control study.

BACKGROUND: Physical training is able to induce changes at neurophysiological and behavioral level associated with performance changes for the trained...

A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since mi...

Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get ea...

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning.

INTRODUCTION: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause er...

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fund...

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic strok...