Latest AI and machine learning research in neurology for healthcare professionals.
Artifacts are noisy signals that commonly contaminate electroencephalographic (EEG) recordings, mixing with underlying brain activity and degrading the quality of neurophysiological data. Previous research on epileptic Anomaly Detection has shown that this approach is also sensitive to unlabelled artifacts, often leading to an increased False Positive rate. While most methods focus on detecting or...
Accurate classification of motor imagery (MI)-based electroencephalogram (EEG) signals is often challenged by signal non-stationarity, subject-specific variability, and privacy concerns associated with sharing raw neural data. To address these challenges, this study proposes a hybrid Markov chain-spatial statistical (MCSS) machine learning framework for privacy-preserving MI-EEG classification. Sp...
Predicting the risk of Alzheimer's disease (AD) is fundamental for early-stage intervention. Nevertheless, most methods struggle to extract multi-omic...
Tick-borne encephalitis (TBE) remains a severe public health threat in affected areas with shifting geographic distribution linked to environmental ch...
Traumatic brain injury (TBI) is a stage-dependent disorder that evolves from acute neuroinflammation, blood-brain barrier (BBB) disruption, oxidative ...
INTRODUCTION: Although deep learning methods for EEG analysis are rapidly advancing, architectures developed for human multichannel recordings may not...
RATIONALE AND OBJECTIVES: To develop and validate the performance and prognostic value of a deep-learning (DL) model for carotid plaque component quan...
Early diagnosis of Parkinson's disease (PD) is challenging due to the difficulty in identification of various early motor signs. We aimed to develop a...
Manual annotation of spike-wave discharges (SWDs), the electrographic hallmark of absence seizures, is labor-intensive for long-term electroencephalog...
Examining sleep patterns in relation to chronological ageing and dementia can provide insights for risk screening. Integrating predictive models with ...
Histological analysis is essential for understanding disease pathology and the microenvironment, particularly in Alzheimer's disease (AD), characteriz...
OBJECTIVE: To investigate risk factors for urinary incontinence (UI) after robot-assisted laparoscopic radical prostatectomy (RARP) using interpretabl...
Multiple sclerosis has undergone a therapeutic revolution over the past three decades. Randomized clinical trials and real-world data demonstrate that...
Portable, low-field (LF) magnetic resonance imaging (MRI) is emerging as a clinically relevant adjunct in acute stroke care, enabling MRI in environme...
Neurological deterioration is a frequent and clinically significant challenge in patients with acute stroke admitted to neurocritical care units, wher...
Understanding the trajectory of Huntington's disease (HD) is critical for patient stratification and the development of targeted interventions. Tradit...
Neuronal functional diversity and pathological vulnerability are governed by multi-layered regulatory programs. While high-throughput omics and neuroi...
PURPOSE: Accurate identification of neural structures is essential for safe ultrasound-guided regional anesthesia. Although artificial intelligence (A...
Artificial intelligence (AI) is revolutionizing health care, particularly in radiology for which large retrospective electronic datasets are naturally...
Epileptic seizures are short episodes of abnormal electrical activity in the brain that can cause convulsions, loss of consciousness, and other simila...