Coronary artery properties in atherosclerosis: A deep learning predictive model - Caballero, Ricardo; Martínez, Miguel Ángel; Peña, Estefanía (Frontiers in Physiology, 2023)
In this work an Artificial Neural Network (ANN) was developed to help in the diagnosis of plaque vulnerability by predicting the mechanical properties (Ecore and Eplaque) of atherosclerotic coronary arteries. A representative in-silico database was constructed to train the ANN using Finite Element simulations covering the ranges of mechanical properties present in the bibliography. A statistical analysis to pre-process the data and determine the most influential variables was performed to select the inputs of the ANN. The ANN was based on Multilayer Perceptron (MLP) architecture and was trained using the developed database, resulting in a Mean Squared Error (MSE) in the loss function under 10−7, enabling accurate predictions on the test dataset for Ecore and Eplaque. Finally, the ANN was applied to estimate the mechanical properties of 10, 000 realistic plaques, resulting in relative errors lower than 3%.