Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms

TL;DR

This paper presents a machine learning approach combining clinical data and imaging features to predict treatment outcomes for cerebral aneurysms, achieving over 75% accuracy and automating image analysis with neural networks.

Contribution

It introduces a novel integrated model using clinical and imaging data, including automated segmentation algorithms, to predict aneurysm treatment outcomes.

Findings

Predictive accuracy of 75.31% for treatment outcomes

Neural network segmentation achieves 90% overlap in 2D and 83% in 3D

Automated feature calculation enhances prediction reliability

Abstract

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.