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Rupture Risk Prediction of Cerebral Aneurysms Using a Novel Convolutional Neural Network-based Deep Learning Model by Hyeondong Yang; Kwang-Chun Cho; Jung-Jae Kim; Jae Ho Kim; Yong Bae Kim; Je Hoon Oh is a Medicine article available to read on EtoBox.

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## Background Cerebral aneurysms should be treated before rupture because ruptured aneurysms result in serious disability. Therefore, accurate prediction of rupture risk is important and has been estimated using various hemodynamic factors. ## Objective To suggest a new way to predict rupture risk in cerebral aneurysms using a novel deep learning model based on hemodynamic parameters for better decision-making about treatment. ## Methods A novel convolutional neural network (CNN) model was used for rupture risk prediction retrospectively of 123 aneurysm cases. To include the effect of hemodynamic parameters into the CNN, the hemodynamic parameters were first calculated using computational fluid dynamics and fluid–structure interaction. Then, they were converted into images for training the CNN using a novel approach. In addition, new data augmentation methods were devised to obtain sufficient training data. A total of 53,136 images generated by data augmentation were used to train and test the CNN. ## Results The CNNs trained with wall shear stress (WSS), strain, and combination images had area under the receiver operating characteristics curve values of 0.716, 0.741, and 0.883, re

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Author

Hyeondong Yang; Kwang-Chun Cho; Jung-Jae Kim; Jae Ho Kim; Yong Bae Kim; Je Hoon Oh

Publisher

BMJ

Published

2023

Language

EN

Field

Medicine (Health Sciences)