Neurology

Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.

Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke. This study ana...

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of...

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports t...

Preictal period optimization for deep learning-based epileptic seizure prediction.

. Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-ba...

Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay.

As robots become increasingly integrated into human society, the importance of human-machine interfaces continues to grow. This study proposes a faste...

Patient Support in Obstructive Sleep Apnoea by a Large Language Model - ChatGPT 4o on Answering Frequently Asked Questions on First Line Positive Airway Pressure and Second Line Hypoglossal Nerve Stimulation Therapy: A Pilot Study.

PURPOSE: Obstructive sleep apnoea (OSA) is a common disease that benefits from early treatment and patient support in order to prevent secondary illne...

Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.

BACKGROUND: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incid...

Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.

BACKGROUND: Identifying neurobiological targets predictive of the molecular neuropathophysiological signature of human opioid use disorder (OUD) could...

Evaluation of cerebral blood flow after subarachnoid hemorrhage using near-field coupling and machine learning.

BackgroundBedside continuous monitoring of cerebral blood flow (CBF) has significant implications in guiding individualized management and improving t...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This stu...

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.

This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the ty...

Effect of artificial intelligence-based video-game system on dysphagia in patients with stroke: A randomized controlled trial.

BACKGROUND AND AIMS: Post-stroke dysphagia is highly prevalent and causes complication. While video games have demonstrated potential to increase pati...

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missions, with microgravity-induced changes that threate...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate ch...

Evaluating Neural Network Performance in Predicting Disease Status and Tissue Source of JC Polyomavirus from Patient Isolates Based on the Hypervariable Region of the Viral Genome.

JC polyomavirus (JCPyV) establishes a persistent, asymptomatic kidney infection in most of the population. However, JCPyV can reactivate in immunocomp...

Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, and its early identification is crucial for improving patient o...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting function...

Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

BACKGROUND: Obstructive sleep apnea (OSA) is common after stroke. Still, routine screening of OSA with polysomnography (PSG) is often unfeasible in cl...

Computer model for gait assessments in Parkinson's patients using a fuzzy inference model and inertial sensors.

Patients with Parkinson's disease (PD) in the moderate and severe stages can present several walk alterations. They can show slow movements and diffic...

Mask R-CNN assisted diagnosis of spinal tuberculosis.

The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to m...