Neurology

AI and neurosurgery: a new era of enhanced outcomes.

Artificial Intelligence (AI) is revolutionizing neurosurgery by enhancing diagnostic accuracy, surgi...

Artificial intelligence enhancements in the neurosurgical treatment of tuberous sclerosis.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability.

Numerous automatic sleep stage classification systems have been developed, but none have become effe...

Effects of high-definition tDCS targeting individual motor hotspot with EMG-driven robotic hand training on upper extremity motor function: a pilot randomized controlled trial.

BACKGROUND: Delivering HD-tDCS on individual motor hotspot with optimal electric fields could overco...

Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study.

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage ...

Real-Time Calibration-Free Musculotendon Kinematics for Neuromusculoskeletal Models.

Neuromusculoskeletal (NMS) models enable non-invasive estimation of clinically important internal bi...

Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transf...

Robotic Assisted Transcranial Doppler Monitoring in Acute Neurovascular Care: A Feasibility and Safety Study.

BACKGROUND: Transcranial color Doppler (TCD) is currently the only noninvasive bedside tool capable ...

Interpretable prediction of acute ischemic stroke after hip fracture in patients 65 years and older based on machine learning and SHAP.

BACKGROUND: Hip fracture and acute ischemic stroke (AIS) are prevalent conditions among the older po...

Development of a Natural Language Processing (NLP) model to automatically extract clinical data from electronic health records: results from an Italian comprehensive stroke center.

INTRODUCTION: Data collection often relies on time-consuming manual inputs, with a vast amount of in...

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the mos...

Employ machine learning to identify NAD+ metabolism-related diagnostic markers for ischemic stroke and develop a diagnostic model.

Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study ai...

Interactive Surgical Training in Neuroendoscopy: Real-Time Anatomical Feature Localization Using Natural Language Expressions.

OBJECTIVE: This study addresses challenges in surgical education, particularly in neuroendoscopy, wh...

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.

OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) h...

Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura.

To investigate the application value of a machine learning model in predicting mild depression asso...

Efficacy of robot-assisted gait training on lower extremity function in subacute stroke patients: a systematic review and meta-analysis.

BACKGROUND: Robot-Assisted Gait Training (RAGT) is a novel technology widely employed in the field o...

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy.

Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of e...

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mo...

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate dia...

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

BACKGROUND: Recent advances in multivariate pattern recognition have fostered the search for reliabl...

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half wil...