Neurology

Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm.

OBJECTIVES: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based ASPECTS calculation software that utilizes a three-dimensional fully convolutional network-based brain hemisphere comparison algorithm (3D-BHCA).

Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review.

PURPOSE: Stroke, spinal cord injury and other neuromuscular disorders lead to impairments in the human body. Upper limb impairments, especially hand impairments affect activities of daily living (ADL) and reduce the quality of life. The purpose of this review is to compare and evaluate the available robotic rehabilitation and assistive devices that can lead to motor recovery or maintain the curren...

Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network.

A lower-limb exoskeleton robot identifies the wearer's walking intention and assists the walking movement through mechanical force; thus, it is import...

Feasibility and preliminary efficacy of a combined virtual reality, robotics and electrical stimulation intervention in upper extremity stroke rehabilitation.

BACKGROUND: Approximately 80% of individuals with chronic stroke present with long lasting upper extremity (UE) impairments. We designed the perSonali...

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed netwo...

Robotic Locomotor Training Leads to Cardiovascular Changes in Individuals With Incomplete Spinal Cord Injury Over a 24-Week Rehabilitation Period: A Randomized Controlled Pilot Study.

OBJECTIVE: To describe the effect of robotic locomotor training (RLT) and activity-based training (ABT) on cardiovascular indices during various physi...

Deep learning for robust detection of interictal epileptiform discharges.

Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epileptic brain can help predict seizure recurrence and su...

Unbiased Recursive Partitioning Enables Robust and Reliable Outcome Prediction in Acute Spinal Cord Injury.

Neurological disorders usually present very heterogeneous recovery patterns. Nonetheless, accurate prediction of future clinical end-points and robust...

Robotic rehabilitation for end-effector device and botulinum toxin in upper limb rehabilitation in chronic post-stroke patients: an integrated rehabilitative approach.

BACKGROUND: Determine the effects of an integrated rehabilitation protocol, including botulinum toxin and conventional rehabilitation exercise plus en...

Effects of robot-assisted gait training on postural instability in Parkinson's disease: a systematic review.

INTRODUCTION: Postural instability is a cardinal feature of Parkinson's disease, together with rest tremor, rigidity and bradykinesia. It is a highly ...

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time.

. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spi...

A New Dataset for Facial Motion Analysis in Individuals With Neurological Disorders.

We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorde...

Neurovascular multiparametric MRI defines epileptogenic and seizure propagation regions in experimental mesiotemporal lobe epilepsy.

OBJECTIVE: Improving the identification of the epileptogenic zone and associated seizure-spreading regions represents a significant challenge. Innovat...

Longitudinal self-supervised learning.

Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires large number of ground-truth lab...

Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction.

Postoperative cognitive dysfunction (POCD) refers to the complications of the central nervous system before and after surgery in patients without ment...

Uncovering the structure of clinical EEG signals with self-supervised learning.

Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinic...

Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III.

BACKGROUND: Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity o...

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease.

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer's Disease (AD) detection and elucidates its molecular pathogeneses. Genome-w...

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-ca...

Human Motion Intent Description Based on Bumpless Switching Mechanism for Rehabilitation Robot.

This paper aims to improve the performance of an electromyography (EMG) decoder based on a switching mechanism in controlling a rehabilitation robot f...