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dc.contributor.author | Goswami, Somdatta | es_ES |
dc.contributor.author | Li, David S. | es_ES |
dc.contributor.author | Rego, Bruno V. | es_ES |
dc.contributor.author | Latorre, Marcos | es_ES |
dc.contributor.author | Humphrey, Jay D. | es_ES |
dc.contributor.author | Karniadakis, George Em | es_ES |
dc.date.accessioned | 2023-01-23T19:00:35Z | |
dc.date.available | 2023-01-23T19:00:35Z | |
dc.date.issued | 2022-08-31 | es_ES |
dc.identifier.issn | 1742-5689 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/191443 | |
dc.description.abstract | [EN] Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta that can lead to life-threatening dissection or rupture. In vivo assessments of TAA progression are largely limited to measurements of aneurysm size and growth rate. There is promise, however, that computational modelling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating mechanobiological insults. We present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify TAA contributing factors using synthetic finite-element-based datasets. For training, we employ a constrained mixture model of aortic growth and remodelling to generate maps of local aortic dilatation and distensibility for multiple TAA risk factors. We evaluate the performance of the surrogate model for insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We propose two frameworks, one trained on sparse information and one on full-field greyscale images, to gain insight into a preferred neural operator-based approach. We show that this continuous learning approach can predict the patient-specific insult profile associated with any given dilatation and distensibility map with high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of applying DeepONet to support transfer learning of patient-specific inputs to predict TAA progression. | es_ES |
dc.description.sponsorship | This work was supported by the National Institutes of Health (grant nos. P01 HL134605 and U01 HL142518) | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | The Royal Society | es_ES |
dc.relation.ispartof | Journal of The Royal Society Interface | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Operator-based neural network | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Growth and remodelling | es_ES |
dc.subject | Thoracic aortic aneurysm | es_ES |
dc.title | Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1098/rsif.2022.0410 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/NIH//U01 HL142518//Multimodality imaging-driven multifidelity modeling of aortic dissection/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/NIH//P01 HL134605 //Endothelial Mechanotransduction in Thoracic Aneurysm Formation and Progression/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Goswami, S.; Li, DS.; Rego, BV.; Latorre, M.; Humphrey, JD.; Karniadakis, GE. (2022). Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms. Journal of The Royal Society Interface. 19(193):1-16. https://doi.org/10.1098/rsif.2022.0410 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1098/rsif.2022.0410 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 16 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 19 | es_ES |
dc.description.issue | 193 | es_ES |
dc.identifier.pmid | 36043289 | es_ES |
dc.identifier.pmcid | PMC9428523 | es_ES |
dc.relation.pasarela | S\471076 | es_ES |
dc.contributor.funder | National Institutes of Health, EEUU | es_ES |
dc.subject.ods | 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades | es_ES |