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Intrasaccular flow disruptors treat cerebral aneurysms by diverting the bloodflow from the aneurysm sac. Residual flow into the sac after the interventionis a failure that could be due to the use of an undersized device, or tovascular anatomy and clinical condition of the patient. We report a machinelearning model based on over 100 clinical and imaging features that predict theoutcome of wide-neck bifurcation aneurysm treatment with an intravascularembolization device. We combine clinical features with a diverse set of commonand novel imaging measurements within a random forest model. We also developneural network segmentation algorithms in 2D and 3D to contour the sac inangiographic images and automatically calculate the imaging features. Thesedeliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictivemodel classifies complete vs. partial occlusion outcomes with an accuracy of75.31%, and weighted F1-score of 0.74.

Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms