Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

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

The Segment Anything Model (SAM) has recently emerged as a groundbreaking model in
the field of image segmentation. Nevertheless, both the original SAM and its medical …

Intracranial aneurysms are a common asymptomatic vascular pathology, the rupture of
which is a devastating event with a significant risk of morbidity and mortality. Aneurysm …

Segmentation of intracranial aneurysm images acquired using magnetic resonance
angiography (MRA) is essential for medical auxiliary treatments, which can effectively …

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the
3D reconstruction procedure are labor-intensive and prone to human errors. To meet the …

Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a
significant cause of morbidity and mortality. Early identification of aneurysms on Computed …

Purpose Early detection and diagnosis of intracranial aneurysms (IAs) are particularly
critical. Deep learning models (DLMs) are now widely used in the diagnosis of various …

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely
and appropriate treatment because the fatality rate after rupture is 50%. Existing methods …

Background Deep learning (DL) could improve the labor-intensive, challenging processes of
diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To …