Background Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of
morbidity and mortality. Early aneurysm identification, aided by automated systems, may …

Intracranial aneurysm is a common life-threatening disease. Computed tomography
angiography is recommended as the standard diagnosis tool; yet, interpretation can be time …

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 …

Objectives To develop a deep learning algorithm for automated detection and localization of
intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic …

Objective The clinical ability of radiomics to predict intracranial aneurysm rupture risk
remains unexplored. This study aims to investigate the potential uses of radiomics and …

Background and Purpose: Outcome prediction after aneurysmal subarachnoid hemorrhage
(aSAH) is challenging. CRP (C-reactive protein) has been reported to be associated with …

Background Artificial intelligence (AI) models in real-world implementation are scarce. Our
study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm …

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 …

Objectives Prediction of intracranial aneurysm rupture is important in the management of
unruptured aneurysms. The application of radiomics in predicting aneurysm rupture …

Objectives We proposed a new approach to train deep learning model for aneurysm rupture
prediction which only uses a limited amount of labeled data. Method Using segmented …