Artificial intelligence technology is a rapidly expanding field with many applications in acute
stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute …

The application of machine learning has rapidly evolved in medicine over the past decade.
In stroke, commercially available machine learning algorithms have already been …

Management of stroke highly depends on informa-tion from imaging studies. Noncontrast
computed tomography (CT) and magnetic resonance imaging (MRI) can both be used to …

Background Accessible tools to efficiently detect and segment diffusion abnormalities in
acute strokes are highly anticipated by the clinical and research communities. Methods We …

Background and objective In recent years, deep learning algorithms have created a massive
impact on addressing research challenges in different domains. The medical field also …

Background Stroke is a neurological condition that occurs when cerebral vessels become
blocked and have reduced blood flow. This research proposes a hybrid deep feature-based …

CT perfusion imaging is commonly used for infarct core quantification in acute ischemic
stroke patients. The outcomes and perfusion maps of CT perfusion software, however, show …

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which
poses a serious challenge to human health and life. Meanwhile, the management of …

Background Magnetic resonance imaging (MRI) is important for the early detection of axial
spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting …

Background Perfusion imaging is important to identify a target mismatch in stroke but
requires contrast agents and postprocessing software. Purpose To use a deep learning …