Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks

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• dc.contributor.author Morales, Xabier
• dc.contributor.author Albors, Carlos
• dc.contributor.author Mill, Jordi
• dc.contributor.author Camara, Oscar
• dc.date.accessioned 2024-04-16T14:39:22Z
• dc.date.available 2024-04-16T14:39:22Z
• dc.date.issued 2022
• dc.description Comunicació presentada a 13th International Workshop, STACOM 2022, conjuntament amb MICCAI 2022, celebrada el 18 de setembre de 2022 a Singapur.
• dc.description.abstract The adoption of patient-specific computational fluid dynamics (CFD) simulations has been instrumental toward a better understanding of the mechanisms underlying thrombogenesis in the left atrial appendage. Such simulations can help optimize the placement of left atrial appendage occlusion (LAAO) devices in atrial fibrillation patients and avoid the generation of device-related thrombosis. However, integrating conventional solvers into clinical practice is cumbersome, as even the slightest change in model geometry involves computing the entire simulation from scratch. In contrast, neural networks can entirely circumvent this issue by transferring knowledge across models targeted at similar physical domains. Thus, in the present study, we introduced a neural network capable of predicting left atrial hemodynamics under different occlusion device configurations, relying solely on a single finite element simulation for training. To this end, we leveraged physics-informed neural networks (PINN), which embed the physical laws governing the domain of interest into the model, exhibiting far superior generalization capabilities than conventional data-driven models. Several device types and positions have been tested in two distinct left atrial geometries. By employing a single reference simulation per patient the network can predict the updated hemodynamics for a variety of device types and positions, orders of magnitude faster than with conventional CFD solvers.
• dc.description.sponsorship This work was funded 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 00608) and the Spanish Ministry of Economy and Competitiveness under the Programme for the Formation of Doctors (PRE2018-084062), and the Retos Investigación project (RTI2018-101193-B-I00), and the H2020 EU SimCardioTest project (Digital transformation in Health and Care SC1-DTH-06-2020; grant agreement No. 101016496). Additionally, this research was supported by grants from NVIDIA and utilized NVIDIA RTX A6000.
• dc.format.mimetype application/pdf
• dc.identifier.citation Morales X, Albors C, Mill J, Camara O. Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks. In: Camara O, Puyol-Antón E, Qin C, Sermesant M, Suinesiaputra A, Wang S, Young A, editors. STACOM 2022: Statistical atlases and computational models of the heart. Regular and CMRxMotion challenge papers; 2022 Sep 18; Singapore. Cham: Springer; 2022. p. 36–45. DOI: 10.1007/978-3-031-23443-9_4
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-031-23443-9_4
• dc.identifier.uri http://hdl.handle.net/10230/59789
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Camara O, Puyol-Antón E, Qin C, Sermesant M, Suinesiaputra A, Wang S, Young A, editors. STACOM 2022: Statistical atlases and computational models of the heart. Regular and CMRxMotion challenge papers; 2022 Sep 18; Singapore. Cham: Springer; 2022. p. 36–45.
• dc.relation.isreferencedby https://github.com/Xtaltec/RT-optimization-LAAO-placement-PINNs
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101016496
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-101193-B-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PRE2018-084062
• dc.rights © Springer This is a author's accepted manuscript of: Morales X, Albors C, Mill J, Camara O. Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks. In: STACOM 2022: Statistical atlases and computational models of the heart. Regular and CMRxMotion challenge papers; 2022 Sep 18; Singapore. Cham: Springer; 2022. p. 36–45. DOI: 10.1007/978-3-031-23443-9_4
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Physics-Informed Neural Networks
• dc.subject.keyword Device Related Thrombosis
• dc.subject.keyword Computational Fluid Dynamics
• dc.subject.keyword Left Atrial Appendage Occlusion
• dc.title Towards real-time optimization of left atrial appendage occlusion device placement through physics-informed neural networks
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion