126026 20241125101153.0 doi 10.3389/fphys.2023.1162436 sideral 133472 ART-2023-133472 eng Caballero, Ricardo Universidad de Zaragoza Coronary artery properties in atherosclerosis: A deep learning predictive model 2023 Access copy available to the general public Unrestricted In this work an Artificial Neural Network (ANN) was developed to help in the diagnosis of plaque vulnerability by predicting the Young modulus of the core (Ecore) and the plaque (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 architecture and 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%. info:eu-repo/grantAgreement/ES/MICINN/PID2019-107517RB-I00 info:eu-repo/grantAgreement/ES/MICINN PRE2020-095671 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.2 2023 PHYSIOLOGY 24 / 85 = 0.282 2023 Q2 T1 1.006 2023 Physiology (medical) 2023 Q2 Physiology 2023 Q2 6.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Martínez, Miguel Ángel Universidad de Zaragoza (orcid)0000-0002-8375-0354 Peña, Estefanía Universidad de Zaragoza (orcid)0000-0002-0664-5024 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 14 (2023), 1162436 [11 pp.] Front. physiol. Frontiers in physiology 1664-042X 454828 http://zaguan.unizar.es/record/126026/files/texto_completo.pdf Versión publicada 2346073 http://zaguan.unizar.es/record/126026/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126026 articulos driver 2024-11-22-12:07:41 ARTICLE