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Published on: January 15, 2022
Development of a surrogate model for predicting atherosclerotic plaque progression based on agent based modeling data
Lemana Spahić1, Nenad Filipović2
1Research and Development center for Bioengineering, BioIRC, Kragujevac, Serbia.
Insights
Researchers developed a highly accurate 95.4% surrogate model for predicting coronary atherosclerosis plaque progression. This artificial neural network model offers a faster alternative to complex simulations, aiding real-time clinical decision support.
Area of Science:
- Cardiovascular Research
- Computational Biology
- Biomedical Engineering
Background:
- Coronary atherosclerosis (CATS) is a leading global cause of death, characterized by plaque buildup in arteries.
- Computational modeling, particularly agent-based modeling (ABM), has advanced the simulation of plaque progression.
- There is a need for optimized predictive modeling resources, leading to the development of surrogate models.
Purpose of the Study:
- To develop a surrogate model for simulating atherosclerotic plaque progression.
- To utilize data from agent-based modeling (ABM) simulations for surrogate model training.
- To create a computationally efficient alternative to lengthy simulations.
Main Methods:
- Utilized a dataset comprising latin-hypercube sampling parameters and 15 patient-specific geometries with plaque progression data.
- Developed a deep learning-based surrogate model employing artificial neural networks (ANN).
- Benchmarked the surrogate model against the original ABM framework.
Main Results:
- The developed surrogate model achieved a high accuracy of 95.4% when benchmarked against the ABM model.
- Demonstrated the robustness and reliability of the artificial neural network framework for this application.
- Indicated the potential for accurate prediction of atherosclerotic plaque progression.
Conclusions:
- The high accuracy of the surrogate model supports its practical adoption.
- This framework enables the use of high-fidelity decision support systems for real-time prediction of atherosclerotic plaque progression.
- Facilitates faster and more efficient clinical decision-making in managing coronary artery disease.
Abstract:
BackgroundAtherosclerosis 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.ObjectiveThis study explores the development of a surrogate model for atherosclerotic plaque progression using ABM simulation data.MethodThe 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).ResultsThe 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.ConclusionAdoption 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.
