Latest AI and machine learning research in neurology for healthcare professionals.
OBJECTIVE: Utilize machine learning models to identify factors associated with seeking medical care ...
New high-performance computing architectures are becoming operative, in addition to exascale compute...
Epilepsy is a common neurological disorder, and its diagnosis mainly relies on the analysis of elect...
OBJECTIVE: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults wi...
Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) an...
Alzheimer's disease (AD), marked by tau tangles and amyloid-beta plaques, leads to cognitive decline...
BACKGROUND: Spinal cord injury (SCI) is a significant public health issue, leading to physical, psyc...
PURPOSE: To enhance the performance of machine learning (ML) models for the post-embolization recana...
One of the biggest challenges of computers is collecting data from human behavior, such as interpret...
Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for ...
Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of ret...
5q-spinal muscular atrophy (SMA) is a neuromuscular disorder (NMD) that has become one of the first ...
This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentia...
INTRODUCTION: Formulating reliable prognosis for ischemic stroke patients remains a challenging task...
Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and labe...
Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in...
Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced unde...
Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure predic...
The study of complex behaviors is often challenging when using manual annotation due to the absence ...
Bringing out brain activity through the interpretation of EEG signals is a challenging problem that ...
Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and ...