OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular tre...
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
39116963
OBJECTIVES: Despite successful recanalization after Mechanical Thrombectomy (MT), approximately 25 % of patients with Acute Ischemic Stroke (AIS) due to Large Vessel Occlusion (LVO) show unfavorable clinical outcomes, namely Futile Recanalization (FR...
OBJECTIVE: We sought to identify patients at risk of "futile" surgery for intrahepatic cholangiocarcinoma using an artificial intelligence (AI)-based model based on preoperative variables.
BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning met...