Endovascular treatment of acute ischemic stroke.

Endovascular treatment of acute ischemic stroke has become the first choice of treatment in large cerebral vessel occlusions, with a very high efficacy in terms of revascularization and reducing disability of affected patients. Revolutionizing acute therapy, it induced important paradigm shifts in the concepts of time and salvageable brain. In this review we focus on the current concepts of patient selection, imaging, techniques and perspectives of endovascular stroke treatment.

Delayed mesencephalic venous infarction after endovascular treatment of a giant aneurysm of the posterior cerebral artery: Case report and anatomical review.

A 54-year old male patient underwent stent reconstruction of the P1-2 segment of the left posterior cerebral artery (PCA) and concomitant coil embolization of a symptomatic giant partially thrombosed P1 segment aneurysm. After an uneventful postinterventional period, on the 7th day the patient developed severe disturbance of consciousness. The imaging workup demonstrated acute venous infarction in the midbrain, caused by the compressive occlusion of the median anterior pontomesncephalic vein by the aneurysm in the interpeduncular fossa.

Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study.

BACKGROUND: Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anteroposterior and lateral 2D digital subtraction angiography images. MATERIAL AND METHODS: Seven hundred and six digital subtraction angiography images were included from a cohort of 240 patients (157 female, mean age 59 years, range 20-92; 83 male, mean age 55 years, range 19-83). Three hundred and thirty-five (47%) single frame anteroposterior and lateral images of a digital subtraction angiography series of 187 aneurysms (41 ruptured, 146 unruptured; average size 7±5.3 mm, range 1-5 mm; total 372 depicted aneurysms) and 371 (53%) aneurysm-negative study images were retrospectively analysed regarding the presence of intracranial aneurysms. The 2D data was split into testing and training sets in a ratio of 4:1 with 3D rotational digital subtraction angiography as gold standard. Supervised deep learning was performed using commercial-grade machine learning software (Cognex, ViDi Suite 2.0). Monte Carlo cross validation was performed. RESULTS: Intracranial aneurysms were detected with a sensitivity of 79%, a specificity of 79%, a precision of 0.75, a F1 score of 0.77, and a mean area-under-the-curve of 0.76 (range 0.68-0.86) after Monte Carlo cross-validation, run 45 times. CONCLUSION: The commercial-grade deep learning software allows for detection of intracranial aneurysms on whole-brain, 2D anteroposterior and lateral digital subtraction angiography images, with results being comparable to more specifically engineered deep learning techniques.