Machine Learning‐Based Rapid Prediction of Torsional Performance of Personalized Peripheral Artery Stent

The complex mechanical environment of peripheral arteries makes stents with poor torsional performance more prone to fracture, and stent fracture is considered a precursor to in-stent restenosis (ISR). Therefore, studying the torsional performance of stents is crucial. However, while the finite element method (FEM) can accurately simulate the torsional behavior of stents, its time-consuming nature makes it difficult to meet the rapid design requirements for individualized stents. Thus, integrating efficient machine learning (ML) models into the stent design process may be a viable approach. In this study, a machine learning-based rapid prediction method was established to achieve the rapid prediction of torsional performance of personalized peripheral artery stents. A dataset containing 200 different stent designs was generated using Latin Hypercube Sampling (LHS) and FEM. The dataset was divided into a training set (160 samples) and a test set (40 samples). Based on four input variables-the length of strut ring (LS), the width of strut (WS), the width of link (WL), and the thickness of stent (T)-the predictive performance of polynomial regression (PR), random forest regression (RFR), and support vector regression (SVR) for the twist metric (TM) was compared. To simulate the real-world application of ML models, after training and testing the ML models, the entire dataset (combining the training and test sets) was used for re-learning while keeping the control parameters unchanged. A validation set (10 samples) was generated through sampling and FEM, and the re-learned ML models were used to predict and validate their performance. By comprehensively comparing the predictive performance of the ML models on the training set, test set, and validation set, the algorithm performance ranked as follows: PR>SVR>RFR. The PR model achieved a mean absolute error (MAE) of (training set = 0.02847; test set = 0.03083; validation set = 0.04311) and a coefficient of determination (R²) of (training set = 0.95148; test set = 0.97822; validation set = 0.94397). This method can effectively shorten the design cycle of stents and meet the need for personalized stent rapid design and choice. In addition, this method can also be extended to predict other mechanical properties of the stent and can be used in stent multi-objective design optimization.