Neurology

Virtual hospital and artificial intelligence: a first step towards the application of an innovative health system for the care of rare cerebrovascular diseases.

The development of virtual care options, including virtual hospital platforms, is rapidly changing the healthcare, mostly in the pandemic period, due to difficulties in in-person consultations. For this purpose, in 2020, a neurological Virtual Hospital (NOVHO) pilot study has been implemented, in order to experiment a multidisciplinary second opinion evaluation system for neurological diseases. Ce...

A wearable system to assist impaired-neck patients: Design and evaluation.

Patients with neurological disorders, such as amyotrophic lateral sclerosis, Parkinson's disease, and cerebral palsy, often face challenges due to head-neck immobility. The conventional treatment approach involves using a neck collar to maintain an upright head position, but this can be cumbersome and restricts head-neck movements over prolonged periods. This study introduces a wearable robot capa...

A multimodal screening system for elderly neurological diseases based on deep learning.

In this paper, we propose a deep-learning-based algorithm for screening neurological diseases. We proposed various examination protocols for screening...

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty.

In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by anal...

A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography.

Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine ...

ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

UNLABELLED: In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its co...

Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation.

Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and impro...

Label-free identification of protein aggregates using deep learning.

Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington's disease...

Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts.

Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of concussions. However, repetitive sub-concussive hea...

Cerebrospinal fluid flow artifact reduction with deep learning to optimize the evaluation of spinal canal stenosis on spine MRI.

PURPOSE: The aim of study was to employ the Cycle Generative Adversarial Network (CycleGAN) deep learning model to diminish the cerebrospinal fluid (C...

An Effective Hybrid Deep Learning Model for Single-Channel EEG-Based Subject-Independent Drowsiness Recognition.

Nowadays, road accidents pose a severe risk in cases of sleep disorders. We proposed a novel hybrid deep-learning model for detecting drowsiness to ad...

Assessing the performance of ChatGPT's responses to questions related to epilepsy: A cross-sectional study on natural language processing and medical information retrieval.

BACKGROUND: Epilepsy is a neurological condition marked by frequent seizures and various cognitive and psychological effects. Reliable information is ...

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network.

Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decodi...

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way ...

Diagnosis of carpal tunnel syndrome using a 10-s grip-and-release test with video and machine learning analysis.

UNLABELLED: We developed a finger motion-based diagnostic system for carpal tunnel syndrome by analysing 10 second grip-and-release test videos. Using...

Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.

AIMS: We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iSco...

Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations.

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for...

Feasibility interventional study investigating PAIN in neurorehabilitation through wearabLE SensorS (PAINLESS): a study protocol.

INTRODUCTION: Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition t...

Predictive Value of Acute Neurological Progression Using Bayesian CT Perfusion for Acute Ischemic Stroke with Large or Median Vessel Occlusion.

OBJECTIVE: Since the efficacy of mechanical thrombectomy (MT) for acute cerebral infarction due to large vessel occlusion has been proven, the time av...

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects.

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotraum...