  Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood
flow from the aneurysm sac. Residual flow into the sac after the intervention
is a failure that could be due to the use of an undersized device, or to
vascular anatomy and clinical condition of the patient. We report a machine
learning model based on over 100 clinical and imaging features that predict the
outcome of wide-neck bifurcation aneurysm treatment with an intravascular
embolization device. We combine clinical features with a diverse set of common
and novel imaging measurements within a random forest model. We also develop
neural network segmentation algorithms in 2D and 3D to contour the sac in
angiographic images and automatically calculate the imaging features. These
deliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictive
model classifies complete vs. partial occlusion outcomes with an accuracy of
75.31%, and weighted F1-score of 0.74.
