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, respectively. Based on the cut-off values, the CNN trained with WSS (sensitivity: 0.5, specificity: 0.79) or strain (sensitivity: 0.74, specificity: 0.71) images alone was not highly predictive. However, the CNN trained with combination images of WSS and strain showed a sensitivity and specificity of 0.81 and 0.82, respectively.
Conclusion CNN-based deep learning algorithm using hemodynamic factors, including WSS and strain, could be an effective tool for predicting rupture risk in cerebral aneurysms with good predictive accuracy.
Data availability statement
No data are available.
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HY and K-CC are joint first authors.
HY and K-CC contributed equally.
YBK and JHO contributed equally.
Contributors HY and K-CC contributed equally to this work as co-first authors. JHO and YBK contributed equally to this work as co-corresponding authors. HY and K-CC gathered the data and drafted the manuscript in collaboration. J-JK and JHK assisted in the discussions and reviewed the manuscript. JHO and YBK conceptualized the study and supervised the process, corresponding to each field of specialty. All authors approved the final version of the manuscript. JHO and YBK are guarantors of this work.
Funding This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2020R1A2C1011918). It was also supported by the NRF grant funded by the Korea government (No. 2021R1F1A1049435).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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