Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms

Autor:	Jadhav, Ashutosh, Kashyap, Satyananda, Bulu, Hakan, Dholakia, Ronak, Liu, Amon Y., Syeda-Mahmood, Tanveer, Patterson, William R., Rangwala, Hussain, Moradi, Mehdi
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Physics - Medical Physics
Zdroj:	AMAI 2022 Annual Symposium
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.11749 Zobrazit plný text záznamu View this record from Arxiv