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Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis.

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of over 12,000 finite element simulations of mechanical behaviour and stress distribution in these arterial models representative of atherosclerotic plaques. We formulate the training data to predict the maximum von Mises stress which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of attributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method which has far-reaching implications for real-time and multi-scale prediction tasks in biomechanics.