Machine learning and statistical shape modelling for real-time prediction of stent deployment in realistic anatomies

Background and Objective:

The rise in minimally invasive procedures has created a demand for efficient and reliable planning software to predict intra- and post-operative outcomes. Surrogate modelling has shown promise, but challenges remain, particularly in cardiovascular applications, due to the complexity of parametrising anatomical structures and the need for large training datasets. This study aims to apply statistical shape modelling and machine learning for predicting stent deployment in real time using patient-specific models. ►

Methods:

We built a statistical shape model starting from an open-source clinical dataset, which we then used to generate new synthetic cases. Finite element simulations of stent deployment were performed on these cases using an in-house software. A surrogate model was then trained to map the statistical features of the synthetic models to the corresponding stent configurations, evaluating sensitivity to dataset size. ►

Results:

Even with the smallest dataset (400 samples), the average prediction error in position among the tested cases never exceeded 8.6%, with a median one within the testing dataset of 1.6%. As the number of training samples increased (4900), we achieved a median position error lower than 0.1 mm (0.97%) and a maximum position error of 0.5 mm (4.8%). Notably, the largest errors occur in the radial direction of the stent, while the deployed length is accurately predicted in all the cases. ►

Conclusions:

The consistent success in performance strongly suggests that surrogate modelling represents a clinically valuable tool for accurately computing stent deployment outcomes in real time, even within complex anatomical scenarios.