# Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery
## 4 Discussion  
In this work, we developed simulation-driven ML models to predict stent expansion in a calcified coronary artery. The validated FE models saved efforts for post-stenting labeling and provided better feature selection based on mechanistic understanding. Specifically, the FE simulation guided us to select stretch features (Area_L, ArcLength_C, and ArcLength_F) besides classical calcification features (Area_C, Angle_C, and Thickness_C) [2]. Eight pre-stenting geometric features and the post-stenting lumen area were used for training and testing the ML models. The predicted lumen areas from ML models were compared with the ones from FE models. Results have shown that the SVR models with nonlinear kernels provided better predictions. The prediction accuracy of ML in this work was attributed to both our feature selection and the training dataset. Existing studies have focused on the relationships between calcification attributes and stenting outcomes without considering other relevant features of the artery. Specifically, the retrospective studies provided either associations between calcification attributes and stent expansion [1], or the probability of stent underexansion using a calcium scoring system [8]. Our recent OCT-based FE models inspected the underlying mechanism of lumen gain. We identified the stretch of fibrotic tissue as the major index of lumen gain following stenting [2]. These lumen features, specifically ArcLength_C and ArcLength_F, were then adopted and tested in our machine learning models via subgroups: calcification feature group and stretch group. The stretch features alone performed better than the calcification features. This indicates that the mechanistic understandings from FE models enable us to identify better features for enhancing the ML analysis. In addition, the validated FE models saved efforts for post-stenting labeling and provided feature selection based on mechanistic understanding. The prediction accuracy depends on how good training datasets could capture the major features of testing cases. We used one segment of the artery for training (first 90 images out of 120), and the remaining segment for testing. Even though the major characteristics of these images were different (Figure 2), the independence of the testing dataset within one vessel might be limited. Thus, we experimented with various training choices considering the limited dataset. We randomly chose 90 images from this artery, and then the remaining 30 images of the same artery were used for testing. The prediction accuracy slightly increased, compared with the aforementioned segment  
Figure 6: Influence of neighboring cross sections on SVR_P prediction errors: (**a**) scatter plot, and (**b**) box plot. training and testing (supplemental Figure S1). Moreover, our simulation-driven ML framework was further examined in three more patient-specific arterial models with different calcification attributes. First, we mixed all cross section images from three vessels, and used 75% of randomly drawn images for training. Then, we used cross section images from three vessels for training, and the images from the fourth vessel for testing. The prediction accuracy of both experiments declined further (data not shown for brevity). This was expected since the training dataset was not large enough to capture the major features of the testing case. We also examined neighboring cross sections to avoid potential imaging artifacts. No statistically significant differences were observed in terms of the bias and range of the prediction errors. This indicated that individual cross sections are capable of predicting the post-stenting lumen area. There are limitations to our work. In machine learning, more data is almost always advised. In our case, we trained and tested on data from different segments of one vessel. This might not adequately test generalizability.