Coronary artery properties in atherosclerosis: A deep learning predictive model

Caballero, Ricardo (Universidad de Zaragoza) ; Martínez, Miguel Ángel (Universidad de Zaragoza) ; Peña, Estefanía (Universidad de Zaragoza)

Resumen: 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%.

Idioma: Inglés

DOI: 10.3389/fphys.2023.1162436

Año: 2023

Publicado en: Frontiers in physiology 14 (2023), 1162436 [11 pp.]
ISSN: 1664-042X

Factor impacto JCR: 3.2 (2023)

Categ. JCR: PHYSIOLOGY rank: 24 / 85 = 0.282 (2023) - Q2 - T1

Factor impacto CITESCORE: 6.5 - Physiology (medical) (Q2) - Physiology (Q2)

Factor impacto SCIMAGO: 1.006 - Physiology (medical) (Q2) - Physiology (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-107517RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN PRE2020-095671

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)