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 In recent years, machine learning (ML) has had notable success in providing
automated analyses of neuroimaging studies, and its role is likely to increase in the future …

Background and Purpose: This study assessed the predictive performance and relative
importance of clinical, multimodal imaging, and angiographic characteristics for predicting …

Background and Purpose: The National Institutes of Health Stroke Scale (NIHSS)
underestimates clinical severity in posterior circulation stroke and patients presenting with …

Background and Purpose: Mechanical thrombectomy (MTB) is a reference treatment for
acute ischemic stroke, with several endovascular strategies currently available. However, no …

Background Optimal outcomes after large-vessel occlusion (LVO) stroke are highly
dependent on prompt diagnosis, effective communication, and treatment, making LVO an …

Background and Purpose—For patients with large vessel occlusion, neuroimaging
biomarkers that evaluate the changes in brain tissue are important for determining the …

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions
in therapy with the aim of treating the cause and preventing a new cerebral ischemic event …

To examine whether machine learning (ML) approach can be used to predict hematoma
expansion in acute intracerebral hemorrhage (ICH) with accuracy and widespread …

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine.
Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality …