The risk of discovering an intracranial aneurysm during the initial screening and follow-up
screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these …

Purpose Most studies evaluating artificial intelligence (AI) models that detect abnormalities
in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently …

Despite recent progress on semi-supervised federated learning (FL) for medical image
diagnosis, the problem of imbalanced class distributions among unlabeled clients is still …

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors,
reduce radiologist workload, and accelerate diagnosis. Training such deep learning models …

The evolution of intracranial pressure (ICP) of critically ill patients admitted to a
neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and …

Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia
and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an …

Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is
a severe disease that results in death, if it is not treated and diagnosed properly at the early …

Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate
action from radiologists. Due to the heavy workload, less experienced staff, and the …

There is an expanding body of literature that describes the application of deep learning and
other machine learning and artificial intelligence methods with potential relevance to …

Introduction Cerebrovascular diseases are known to cause significant morbidity and
mortality to the general population. In patients with cerebrovascular disease, prompt clinical …