Neurology

Robot-assisted training with functional electrical stimulation enhances lower extremity function after spinal cord injury.

INTRODUCTION: Functional electrical stimulation (FES) synchronized with robot-assisted lower extremity training is used in spinal cord injury (SCI) rehabilitation to promote residual function.

The Impact of Engagement with the PARO Therapeutic Robot on the Psychological Benefits of Older Adults with Dementia.

OBJECTIVES: This study aimed to examine the effect of 8-weeks of a 60-minute PARO intervention to reduce depressive symptoms and loneliness in older adults with dementia and investigated changes in their emotional or behavioral expressions and level of engagement with the PARO robot.

Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.

BACKGROUND: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world's largest population with ...

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals w...

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and ...

Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.

CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust compar...

Machine learning in neuroimaging: from research to clinical practice.

Neuroimaging is critical in clinical care and research, enabling us to investigate the brain in health and disease. There is a complex link between th...

CerebelluMorphic: Large-Scale Neuromorphic Model and Architecture for Supervised Motor Learning.

The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse ap...

Magnetic resonance imaging image analysis of the therapeutic effect and neuroprotective effect of deep brain stimulation in Parkinson's disease based on a deep learning algorithm.

In order to study the therapeutic neuroprotective effect of deep brain stimulation (DBS) in Parkinson's disease (PD), based on the deep learning algor...

A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design.

PURPOSE: To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerv...

ResNet-50 based technique for EEG image characterization due to varying environmental stimuli.

BACKGROUND AND OBJECTIVE: Emotion is an important factor affecting a person's physical and mental health, but there are few ways to detect a patient's...

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Co...

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (...

Comparative Effectiveness of Robot-Assisted Training Versus Enhanced Upper Extremity Therapy on Upper and Lower Extremity for Stroke Survivors: A Multicentre Randomized Controlled Trial.

OBJECTIVE: Robot-assisted neuro-rehabilitation therapy plays a central role in upper extremity recovery of stroke. However, the efficacy of robotic tr...

Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad ...

Deep learning: applications in retinal and optic nerve diseases.

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional p...

The Impact of Cognitive Impairment on Robot-Based Upper-Limb Motor Assessment in Chronic Stroke.

BACKGROUND: Chronic upper extremity motor deficits are present in up to 65% of stroke survivors, and cognitive impairment is prevalent in 46-61% of st...

Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.

The timed up-and-go (TUG) test is an efficient way to evaluate an individual's basic functional mobility, such as standing up, walking, turning around...

Finger dexterity measured by the Grooved Pegboard test indexes Parkinson's motor severity in a tremor-independent manner.

Fine motor impairments are frequent complaints in people with Parkinson's disease (PD). While they may develop at an early stage of the disease, they ...

Effectiveness of robot-assisted virtual reality mirror therapy for upper limb motor dysfunction after stroke: study protocol for a single-center randomized controlled clinical trial.

BACKGROUND: Upper limb motor dysfunction is a common sequela of stroke, and its clinical efficacy needs to be improved. This protocol describes a tria...