Neurology

The Impact of Robotic Rehabilitation on the Motor System in Neurological Diseases. A Multimodal Neurophysiological Approach.

Motor disability is a key feature of many neurological diseases, influencing the social roles of affected patients and their ability to perform daily life activities. Current rehabilitation capacities are overwhelmed by the age-related increase of motor dysfunctions seen, for example, in stroke, extrapyramidal or neuromuscular diseases. As the patient to rehabilitation personnel ration increases, ...

The Vascular Basis of the Pronator Quadratus Muscle Flap and Its Use in Clinical Cases.

 Pronator quadratus (PQ) is a deeply situated muscle in the forearm which may occasionally be utilized for soft-tissue reconstruction. The purpose of this anatomical and clinical study was to confirm vascular supply of PQ muscle (PQM) in order to optimize its transfer and confirm its utility in clinical situations.  In Part A of the anatomical study, fresh human cadavers ( = 7) were prepared wi...

Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography.

BACKGROUND: Alzheimer's Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to ma...

Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network.

Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influ...

Image-level detection of arterial occlusions in 4D-CTA of acute stroke patients using deep learning.

The triage of acute stroke patients is increasingly dependent on four-dimensional CTA (4D-CTA) imaging. In this work, we present a convolutional neura...

User-Driven Functional Movement Training With a Wearable Hand Robot After Stroke.

We studied the performance of a robotic orthosis designed to assist the paretic hand after stroke. It is wearable and fully user-controlled, serving t...

A Methodology to Differentiate Parkinson's Disease and Aging Speech Based on Glottal Flow Acoustic Analysis.

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson's ...

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model.

As is known, cerebral stroke has become one of the main diseases endangering people's health; ischaemic strokes accounts for approximately 85% of cere...

The effects of light touch cue on gait initiation in patients with Parkinson's disease.

INTRODUCTION: One of the common impairments in patients with Parkinson's disease (PD) is disturbance of gait initiation. A light touch cue improves po...

Accessory neuropathy after minimally invasive robotic mitral valve repair: Further evidence to support an inflammatory etiology.

Does including machine learning predictions in ALS clinical trial analysis improve statistical power?

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The inc...

Pain phenotypes classified by machine learning using electroencephalography features.

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subj...

Computational Method for Quantitative Comparison of Activity Landscapes on the Basis of Image Data.

Activity landscape (AL) models are used for visualizing and interpreting structure-activity relationships (SARs) in compound datasets. Therefore, ALs ...

Progressive Multiple Sclerosis Transcriptome Deconvolution Indicates Increased M2 Macrophages in Inactive Lesions.

Accumulating evidence suggests M2 macrophages contribute to tissue reparation and limit inflammation in multiple sclerosis (MS). However, most studies...

Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks.

In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brai...

Deep learning for neonatal seizure detection: a friend rather than foe.

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging ...

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.

PURPOSE: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations w...

Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects.

BACKGROUND AND OBJECTIVE: In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domai...

Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model.

BACKGROUND: Recently approved artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automa...