Neurology

Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.

Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus across different imaging modalities. We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging/angiography (MRI/MRA) sequences in AVM patients, with the goal of...

Humanoid Robot Based Platform to Evaluate the Efficacy of Using Inertial Sensors for Spasticity Assessment in Cerebral Palsy.

Spasticity is commonly present in individuals with cerebral palsy (CP) and manifests itself as shaky movements, muscle tightness and joint stiffness. Accurate and objective measurement of spasticity is investigated using inertial measurement unit (IMU) sensors. However, use of current IMU-based devices is limited to clinics in urban areas where experienced and trained health professionals are avai...

WeDea: A New EEG-Based Framework for Emotion Recognition.

With the development of sensing technologies and machine learning, techniques that can identify emotions and inner states of a human through physiolog...

Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.

INTRODUCTION: Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We ...

Comparative effects of EMG-driven robot-assisted therapy versus task-oriented training on motor and daily function in patients with stroke: a randomized cross-over trial.

BACKGROUND: Robot-assisted hand training has shown positive effects on promoting neuromuscular control. Since both robot-assisted therapy and task-ori...

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive sur...

Deep learning of early brain imaging to predict post-arrest electroencephalography.

INTRODUCTION: Guidelines recommend use of computerized tomography (CT) and electroencephalography (EEG) in post-arrest prognostication. Strong associa...

Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fe...

A tale of two robots: Operating times and learning curves in robot-assisted lumbar fusion.

Robotic assistance technologies are being incorporated into minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) to minimize radiation...

Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases.

Farming is essential to the long-term viability of any economy. It differs in each country, but it is essential for long-term economic success. Only a...

Single-Port Retzius-Sparing Robot-Assisted Radical Prostatectomy: Feasibility and Early Outcomes.

To determine safety and feasibility of single-port Retzius-sparing robot-assisted radical prostatectomy (SP-rsRARP) using the da Vinci SP (Intuitive ...

Nerve spare robot assisted laparoscopic prostatectomy with amniotic membranes: medium term outcomes.

dHACM is a source of factors including cytokines that allow anti-inflammatory and proliferative elements to be utilized for wound and ulcer management...

Meta-analysis of human prediction error for incentives, perception, cognition, and action.

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species...

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques.

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic...

Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is s...

Fine motor impairment in children with epilepsy: Relations with seizure severity and lateralizing value.

Motor skill deficits are common in epilepsy. The Grooved Pegboard Test (GPT) is the most commonly used fine motor task and is included in the NIH Comm...

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model.

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive c...

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans.

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin ...

Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts.

Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning te...

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Wherea...