Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.

Background: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).

Methods: Deep learning models were trained and tested on baseline clinical and imaging (CT head and CT angiography) data to predict 3-month functional outcomes in stroke patients who underwent EVT. Classical machine learning models (logistic regression and random forest classifiers) were constructed to compare their performance with the deep learning models.

Penumbral cooling in ischemic stroke with intraarterial, intravenous or active conductive head cooling: A thermal modeling study.

In ischemic stroke, selectively cooling the ischemic penumbra might lead to neuroprotection while avoiding systemic complications. Because penumbral tissue has reduced cerebral blood flow and brain temperature measurement remains challenging, the effect of different methods of therapeutic hypothermia on penumbral temperature are unknown. We used the COMSOL Multiphysics® software to model a range of cases of therapeutic hypothermia in ischemic stroke.

Adjunctive Intra-arterial Thrombolysis in Endovascular Thrombectomy: A Systematic Review and Meta-analysis.

Objective: To evaluate the safety and efficacy of intra-arterial thrombolysis (IAT) as an adjunct to endovascular thrombectomy (EVT) in ischemic stroke, we performed a systematic review and meta-analysis of the literature.

Methods: Searches were performed using MEDLINE, Embase, and Cochrane databases for studies that compared EVT with EVT with adjunctive IAT (EVT + IAT). Safety outcomes included symptomatic intracerebral hemorrhage and mortality at 3 months.