Development of a surrogate model for predicting atherosclerotic plaque progression based on agent based modeling data.

Abstract

Background: Atherosclerosis of the coronary arteries is a chronic, progressive condition characterized by the buildup of plaque within the arterial walls. Coronary artery disease (CAD), more specifically coronary atherosclerosis (CATS), is one of the leading causes of death worldwide. Computational modeling frameworks have been used for simulation of atherosclerotic plaque progression and with the advancement of agent-based modeling (ABM) the simulation results became more accurate. However, there is a need for optimization of resources for predictive modeling, hence surrogate models are being built to substitute lengthy computational models without compromising the results.

Objective: This study explores the development of a surrogate model for atherosclerotic plaque progression using ABM simulation data.

Method: The dataset used for this study contains samples from latin-hypercube sampling based generated simulation parameters used in conjunction with 15 patient-specific geometries and corresponding plaque progression data. The developed surrogate model is based on deep learning using artificial neural networks (ANN).

Results: The surrogate model achieved an accuracy of 95.4% in benchmarking with the ABM model it was built upon which indicates the robustness of the framework.

Conclusion: Adoption of surrogate models with high accuracy in practice opens an avenue for utilization of high-fidelity decision support systems for predicting atherosclerotic plaque progression in real-time.