000126026 001__ 126026
000126026 005__ 20241125101153.0
000126026 0247_ $$2doi$$a10.3389/fphys.2023.1162436
000126026 0248_ $$2sideral$$a133472
000126026 037__ $$aART-2023-133472
000126026 041__ $$aeng
000126026 100__ $$aCaballero, Ricardo$$uUniversidad de Zaragoza
000126026 245__ $$aCoronary artery properties in atherosclerosis: A deep learning predictive model
000126026 260__ $$c2023
000126026 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126026 5203_ $$aIn this work an Artificial Neural Network (ANN) was developed to help in the diagnosis of plaque vulnerability by predicting the Young modulus of the core (Ecore) and the plaque (Eplaque) of atherosclerotic coronary arteries. A representative in silico database was constructed to train the ANN using Finite Element simulations covering the ranges of mechanical properties present in the bibliography. A statistical analysis to pre-process the data and determine the most influential variables was performed to select the inputs of the ANN. The ANN was based on Multilayer Perceptron architecture and trained using the developed database, resulting in a Mean Squared Error (MSE) in the loss function under 10–7, enabling accurate predictions on the test dataset for Ecore and Eplaque. Finally, the ANN was applied to estimate the mechanical properties of 10,000 realistic plaques, resulting in relative errors lower than 3%.
000126026 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-107517RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN PRE2020-095671
000126026 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000126026 590__ $$a3.2$$b2023
000126026 592__ $$a1.006$$b2023
000126026 591__ $$aPHYSIOLOGY$$b24 / 85 = 0.282$$c2023$$dQ2$$eT1
000126026 593__ $$aPhysiology (medical)$$c2023$$dQ2
000126026 593__ $$aPhysiology$$c2023$$dQ2
000126026 594__ $$a6.5$$b2023
000126026 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126026 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, Miguel Ángel$$uUniversidad de Zaragoza
000126026 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, Estefanía$$uUniversidad de Zaragoza
000126026 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000126026 773__ $$g14 (2023), 1162436 [11 pp.]$$pFront. physiol.$$tFrontiers in physiology$$x1664-042X
000126026 8564_ $$s454828$$uhttps://zaguan.unizar.es/record/126026/files/texto_completo.pdf$$yVersión publicada
000126026 8564_ $$s2346073$$uhttps://zaguan.unizar.es/record/126026/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126026 909CO $$ooai:zaguan.unizar.es:126026$$particulos$$pdriver
000126026 951__ $$a2024-11-22-12:07:41
000126026 980__ $$aARTICLE