dc.contributor.author |
Morales Ferez, Xabier |
dc.contributor.author |
Mill, Jordi |
dc.contributor.author |
Juhl, Kristine Aavild |
dc.contributor.author |
Acebes Pinilla, César |
dc.contributor.author |
Iriart, Xavier |
dc.contributor.author |
Legghe, Benoit |
dc.contributor.author |
Cochet, Hubert |
dc.contributor.author |
De Backer, Ole |
dc.contributor.author |
Paulsen, Rasmus R. |
dc.contributor.author |
Camara, Oscar |
dc.date.accessioned |
2022-06-21T05:57:12Z |
dc.date.available |
2022-06-21T05:57:12Z |
dc.date.issued |
2021 |
dc.identifier.citation |
Xabier Morales Ferez X, Mill J, Juhl KA, Acebes C, Iriart X, Legghe B, Cochet H, De Backer O, Paulsen RR, Camara O. Deep learning framework for real-time estimation of in-silico thrombotic risk indices in the left atrial appendage. Front. Physiol. 2021;12:694945. DOI: 10.3389/fphys.2021.694945 |
dc.identifier.issn |
1664-042X |
dc.identifier.uri |
http://hdl.handle.net/10230/53539 |
dc.description.abstract |
Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable
insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics,
and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD
solvers are notoriously time-consuming and computationally demanding, which has
sparked an ever-growing body of literature aiming to develop surrogate models of
fluid simulations based on neural networks. The present study aims at developing
a deep learning (DL) framework capable of predicting the endothelial cell activation
potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived
from CFD simulations, solely from the patient-specific LAA morphology. To this end,
a set of popular DL approaches were evaluated, including fully connected networks
(FCN), convolutional neural networks (CNN), and geometric deep learning. While the
latter directly operated over non-Euclidean domains, the FCN and CNN approaches
required previous registration or 2D mapping of the input LAA mesh. First, the superior
performance of the graph-based DL model was demonstrated in a dataset consisting of
256 synthetic and real LAA, where CFD simulations with simplified boundary conditions
were run. Subsequently, the adaptability of the geometric DL model was further proven
in a more realistic dataset of 114 cases, which included the complete patient-specific
LA and CFD simulations with more complex boundary conditions. The resulting DL
framework successfully predicted the overall distribution of the ECAP in both datasets,
based solely on anatomical features, while reducing computational times by orders of
magnitude compared to conventional CFD solvers. |
dc.description.sponsorship |
This work was supported by the Agency for Management of University and Research Grants of the Generalitat de Catalunya under the Grants for the Contracting of New Research Staff Programme—FI (2020-FI-B-00690) 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 Investigación 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). |
dc.format.mimetype |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
Frontiers |
dc.relation.ispartof |
Frontiers in physiology. 2021;12:694945. |
dc.rights |
© 2021 Morales Ferez, Mill, Juhl, Acebes, Iriart, Legghe, Cochet, De
Backer, Paulsen and Camara. This is an open-access article distributed under the
terms of the Creative Commons Attribution License (CC BY). The use, distribution
or reproduction in other forums is permitted, provided the original author(s) and
the copyright owner(s) are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms. |
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
dc.title |
Deep learning framework for real-time estimation of in-silico thrombotic risk indices in the left atrial appendage |
dc.type |
info:eu-repo/semantics/article |
dc.identifier.doi |
http://doi.org/10.3389/fphys.2021.694945 |
dc.subject.keyword |
geometric deep learning |
dc.subject.keyword |
left atrial appendage |
dc.subject.keyword |
convolutional neural network |
dc.subject.keyword |
thrombus-atrial fibrillation |
dc.subject.keyword |
computational fluid dynamics |
dc.subject.keyword |
principal component analysis |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/H2020/101016496 |
dc.relation.projectID |
info:eu-repo/grantAgreement/ES/2PE/PRE2018-084062 |
dc.rights.accessRights |
info:eu-repo/semantics/openAccess |
dc.type.version |
info:eu-repo/semantics/publishedVersion |