Neurology

Effects of high-intensity gait training with and without soft robotic exosuits in people post-stroke: a development-of-concept pilot crossover trial.

INTRODUCTION: High-intensity gait training is widely recognized as an effective rehabilitation appro...

Aided diagnosis of cervical spondylotic myelopathy using deep learning methods based on electroencephalography.

Cervical spondylotic myelopathy (CSM) is the most severe type of cervical spondylosis. It is challen...

A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate.

In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stres...

Predicting long-term neurocognitive outcome after pediatric intensive care unit admission for bronchiolitis-preliminary exploration of the potential of machine learning.

PURPOSE: For successful prevention and intervention, it is important to unravel the complex constell...

A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson's Disease for Use in Tele-Assessments.

Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurologic...

Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help id...

Preclinical Evaluation of a Novel Steerable Robotic Neuroendoscope Tool.

BACKGROUND AND OBJECTIVES: To improve the outcomes of minimally invasive, endoscopic, intracranial p...

Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings.

OBJECTIVE: To develop an artificial neural network (ANN) for classification of motor unit action pot...

Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.

The major issue faced by elderly people in society is the loss of memory, difficulty learning new th...

Early Prognostication of Critical Patients With Spinal Cord Injury: A Machine Learning Study With 1485 Cases.

STUDY DESIGN: A retrospective case-series.

Optimized 3D brachial plexus MR neurography using deep learning reconstruction.

OBJECTIVE: To evaluate whether 'fast,' unilateral, brachial plexus, 3D magnetic resonance neurograph...

Applications of artificial intelligence in dementia.

The recent evolution of artificial intelligence (AI) can be considered life-changing. In particular,...

ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely us...

Letter to the Editor: "Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline".

India ink to 3D imaging: The legacy of Dr. Deepak "Dee" N. Pandya and his influence on generations of neuroanatomists.

Dr. Deepak "Dee" Pandya spent his career as an internal medicine physician as well as in his respect...

A bi-functional three-terminal memristor applicable as an artificial synapse and neuron.

Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show pr...

From Diagnosis to Treatment: A Comprehensive Review of Biomarkers and Therapeutic Advances in Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by th...

Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods.

Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even de...

Bridging Neuroscience and Robotics: Spiking Neural Networks in Action.

Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area t...

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

IMPORTANCE: Predictive models using machine learning techniques have potential to improve early dete...

In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.

BACKGROUND: Falls are one of the most common accidents in medical institutions, which can threaten t...