An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs - Chen, Rong; Mo, Xiao; Chen, Zhenpeng; Feng, Pujie; Li, Haiyun (Frontiers in Neurology, 2022)
Background The rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision making. Purpose We aim to build an integrated model to improve the assessment of the rupture risk of IAs. Materials and Methods 148 (39 ruptured and 109 unruptured) IAs subjects were retrospectively computed with computational fluid dynamics (CFD), an integrated models were proposed combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms including random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM) and LightGBM were respectively adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic clouds features from the hemodynamic clouds obtained from CFD. Morphologic variables and hemodynamic parameters along with the extracted the hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared. Results Without consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM and LightGBM were 0.824, 0.759, 0.839, 0.860 and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925 and 0.890. With consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926 and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969 and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively. Conclusion The integrated model combining ML and DL algorithms could improve classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.