2026-04-13T00:58:17Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/544732025-12-20T16:58:53Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Morales, Xabier author Mill, Jordi author Simeon, Guillem author Juhl, Kristine Aavild author De Backer, Ole author Paulsen, Rasmus R. author Camara, Oscar author 2022-10-18T15:21:52Z 2022-10-18T15:21:52Z 2021 Comunicació presentada a: FIMH 2021 11th International Conference, celebrada del 21 al 25 de juny de 2021 a Stanford, CA, USA. The assessment of left atrial appendage (LAA) thrombogenesis has experienced major advances with the adoption of patient-specific computational fluid dynamics (CFD) simulations. Nonetheless, due to the vast computational resources and long execution times required by fluid dynamics solvers, there is an ever-growing body of work aiming to develop surrogate models of fluid flow simulations based on neural networks. The present study builds on this foundation by developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), linked to the risk of thrombosis, solely from the patient-specific LAA geometry. To this end, we leveraged recent advancements in Geometric DL, which seamlessly extend the unparalleled potential of convolutional neural networks (CNN), to non-Euclidean data such as meshes. The model was trained with a dataset combining 202 synthetic and 54 real LAA, predicting the ECAP distributions instantaneously, with an average mean absolute error of 0.563. Moreover, the resulting framework manages to predict the anatomical features related to higher ECAP values even when trained exclusively on synthetic cases. This work was supported by the Agency for Management of University and Research Grants of the Generalitat de Catalunya under the the Grants for the Contracting of New Research Staff Programme - FI (2020 FI B 00608) and the Spanish Ministry of Economy and Competitiveness under the Programme for the Formation of Doctors (PRE2018-084062), the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Retos Investigaci´on project (RTI2018-101193-B-I00). Additionally, this work was supported by the H2020 EU SimCardioTest project (Digital transformation in Health and Care SC1- DTH-06-2020; grant agreement No. 101016496). Geometric deep learning Left atrial appendage Thrombus formation Computational fluid dynamic Geometric deep learning for the assessment of thrombosis risk in the left atrial appendage