Neurology

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors determine individual proneness to psychotic symptoms. Schizotypy is a multifaceted personality trait related to psychosis-proneness and dopaminergic neurotransmission in healthy subjects. We investigated whether (1) PD patients exhibit lower schizotypy th...

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.

Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient's symptoms. For several medical organizations, disease prediction is important for making the best feasib...

Behavioral and neurophysiological effects of an intensified robot-assisted therapy in subacute stroke: a case control study.

BACKGROUND: Physical training is able to induce changes at neurophysiological and behavioral level associated with performance changes for the trained...

A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since mi...

Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get ea...

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning.

INTRODUCTION: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause er...

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fund...

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic strok...

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

OBJECTIVE: This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonanc...

Postoperative analgesia in patients undergoing robot-assisted thoracic surgery: a comparison between thoracic epidural analgesia and intercostal nerve block combined with intravenous patient-controlled analgesia.

BACKGROUND: Recently, robot-assisted thoracic surgery (RATS) is increasingly applied to lung or mediastinal tumor surgery. However, appropriate method...

Statistical field theory of the transmission of nerve impulses.

BACKGROUND: Stochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane...

Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network.

The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from...

Robotic-Assisted Peripheral Nerve Surgery: A Systematic Review.

BACKGROUND:  Robotic-assisted techniques are a tremendous revolution in modern surgery, and the advantages and indications were well discussed in diff...

Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk o...

Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices.

Learning vector quantization (LVQ) is a simple and efficient classification method, enjoying great popularity. However, in many classification scenari...

The diagnosis of tuberculous meningitis in adults and adolescents: protocol for a systematic review and individual patient data meta-analysis to inform a multivariable prediction model.

Tuberculous meningitis (TBM) is the most lethal and disabling form of tuberculosis. Delayed diagnosis and treatment, which is a risk factor for poor ...

Robotic Assessment of Upper Limb Function in a Nonhuman Primate Model of Chronic Stroke.

Stroke is a leading cause of death and disability worldwide and survivors are frequently left with long-term disabilities that diminish their autonomy...

Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state.

OBJECTIVE: Our objective is to study how to obtain features which can reflect the continuity and internal dynamic changes of electroencephalography (E...

Caffeine mouth rinse enhances performance, fatigue tolerance and reduces muscle activity during moderate-intensity cycling.

We investigated the effects of caffeine mouth rinse on endurance performance, muscle recruitment (i.e., electromyographic activity of the vastus later...

Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center.

BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz ...