Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …

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

Background and Purpose—Treatment options for patients with acute ischemic stroke
depend on the volume of salvageable tissue. This volume assessment is currently based on …

This paper dispenses an exhaustive review on deep learning techniques used in the
prognosis of eight different neuropsychiatric and neurological disorders such as stroke …

Importance Predicting infarct size and location is important for decision-making and
prognosis in patients with acute stroke. Objectives To determine whether a deep learning …

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 and Purpose: Stroke-related functional risk scores are used to predict patients'
functional outcomes following a stroke event. We evaluate the predictive accuracy of …

Stroke is a leading cause of long-term disability, and outcome is directly related to timely
intervention. Not all patients benefit from rapid intervention, however. Thus a significant …

Stroke prediction plays a crucial role in preventing and managing this debilitating condition.
In this study, we address the challenge of stroke prediction using a comprehensive dataset …

Amongst the most common causes of death globally, stroke is one of top three affecting over
100 million people worldwide annually. There are two classes of stroke, namely ischemic …