Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality.
It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict …

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and
has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic …

Background Patients suffered aneurysmal subarachnoid hemorrhage (aSAH) usually
develop poor survival and functional outcome. Evaluating aSAH patients at high risk of poor …

Background: A predictive model can provide physicians, relatives, and patients the accurate
information regarding the severity of disease and its predicted outcome. In this study, we …

We hypothesized that imaging-only-based machine learning algorithms can analyze non-
enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This …

Abstract Machine learning (ML) as a novel approach could help clinicians address the
challenge of accurate stability assessment of unruptured intracranial aneurysms (IAs). We …

Background Early computed tomography perfusion (CTP) is frequently used to predict
delayed cerebral ischemia following aneurysmatic subarachnoid hemorrhage (aSAH) …

Background: Reliable prediction models of subarachnoid hemorrhage (SAH) outcomes are
needed for decision-making of the treatment. SAFIRE score using only four variables is a …

Objectives The survival rates for patients affected by aneurysmal subarachnoid hemorrhage
(aSAH) have increased in recent years; however, many patients continue to develop …

Spontaneous intracerebral hemorrhage (SICH) has been common in China with high
morbidity and mortality rates. This study aims to develop a machine learning (ML)-based …