A narrative review was conducted of machine learning applications and research in the field
of stroke rehabilitation. The machine learning models commonly used in medical research …

Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal
subarachnoid hemorrhage (aSAH) may decrease the duration of in-hospital stay and reduce …

Background Mechanical thrombectomy (MT) is the standard-of-care treatment for stroke
patients with emergent large vessel occlusions. Despite this, little is known about physician …

Objective Prediction of malignant middle cerebral artery infarction (MMI) could identify
patients for early intervention. We trained and internally validated a ML model that predicts …

Endovascular thrombectomy (EVT) is the most effective treatment for acute ischemic stroke
caused by large vessel occlusion (LVO). Yet, long-term outcome (LTO) and health-related …

Stroke recovery is multifaceted and complex. Machine learning approaches have potential
to identify patterns of brain activity associated with clinical outcomes, providing new insights …

Background Postoperative pneumonia (POP) is one of the primary complications after
aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative …

Intra-arterial thrombectomy (IAT) has been increasingly applied in the treatment of acute
ischemic stroke (AIS) due to large-vessel occlusion, and many related studies have been …

Abstract Background and Purpose We aimed to predict the functional outcome of acute
ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective …

Objective Mechanical thrombectomy (MT) improves outcomes in patients with LVO but many
still experience mortality or severe disability. We sought to develop machine learning (ML) …