2026-04-18T06:50:59Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/544732025-12-20T16:58:53Zcom_2072_6col_2072_452952
RECERCAT
author
Morales, Xabier
author
Mill, Jordi
author
Simeon, Guillem
author
Juhl, Kristine Aavild
author
De Backer, Ole
author
Paulsen, Rasmus R.
author
Camara, Oscar
2022-10-18T15:21:52Z2022-10-18T15:21:52Z2021
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).
© Springer The final publication is available at Springer via
http://dx.doi.org/10.1007/978-3-030-78710-3_61 info:eu-repo/semantics/openAccess
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
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion