[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125588-en":3,"doc-seo-125588-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125588,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine learning and reduced order modelling for the simulation of braided stent deployment","Endoluminal reconstruction with flow diverters offers a minimally invasive strategy for intracranial aneurysms, but the deployed configuration of dense braided stents is difficult to predict from volumetric medical images. A fast, accurate framework is proposed using finite element–based data and a two-stage workflow: classification for deployment success and regression for an approximated deployed stent configuration. Non-intrusive reduced order modelling combines proper orthogonal decomposition and Gaussian process regression and is validated on idealized intracranial artery geometries.","TYPE Original Research PUBLISHED 29 March 2023  \nDOI 10.3389/fphys.2023.1148540  \nOPEN ACCESS  \nEDITED BY  \nJerome Noailly,  \nPompeu Fabra University, Spain  \nREVIEWED BY  \nHarvey Ho,  \nUniversity of Auckland, New Zealand Estefania Peña,  \nUniversity of Zaragoza, Spain Frederic Heim,  \nUniversité de Haute-Alsace, France  \n*CORRESPONDENCE  \nStéphane Avril,  [avril@emse.fr](avril@emse.fr)  \nSPECIALTY SECTION  \nThis article was submitted to Computational Physiology and Medicine, a section of the journal Frontiers in Physiology  \nRECEIVED 20 January 2023  \nACCEPTED 16 March 2023  \nPUBLISHED 29 March 2023  \nCITATION  \nBisighini B, Aguirre M, Biancolini ME, Trovalusci F, Perrin D, Avril S and Pierrat B (2023), Machine learning and reduced order modelling for the simulation of braided stent deployment.  \nFront. Physiol. 14:1148540 .  \ndoi: 10.3389/fphys.2023.1148540  \nCOPYRIGHT  \n© 2023 Bisighini, Aguirre, Biancolini, Trovalusci, Perrin, Avril and Pierrat. This isan 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.  \nMachine learning and reduced order modelling for the simulation of braided stent deployment  \nBeatrice Bisighini 1,2,3, Miquel Aguirre 4,5,1,  \nMarco Evangelos Biancolini 3, Federica Trovalusci 3, David Perrin 2, Stéphane Avril 1* and Baptiste Pierrat 1  \n1 Mines Saint-Étienne, University Lyon, University Jean Monnet, INSERM, Saint-Étienne, France, 2 Predisurge, Grande Usine Creative 2, Saint-Etienne, France, 3 Department of Enterprise Engineering, University Tor Vergata, Rome, Italy, 4 Laboratori de Càlcul Numèric, Universitat Politècnica de Catalunya, Barcelona, Spain, 5 International Centre for Numerical Methods in Engineering (CIMNE), Gran Capità, Barcelona, Spain  \nEndoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97% . On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0 .15 mm) . Such accurate predictions can be reached even with a small d","cbCainenSfUpjeM3","https://ap.wps.com/l/cbCainenSfUpjeM3","pdf",56045716,1,18,"English","en",105,"# Introduction\n## Endoluminal reconstruction and flow diverters\n## Machine learning and reduced order modelling framework","[{\"question\":\"What two-stage workflow is used to predict braided stent deployment?\",\"answer\":\"A first classification step predicts whether deployment will be successful by assessing stent–vessel conformity, followed by a regression step that approximates the deployed stent configuration.\"},{\"question\":\"How does the reduced order modelling approach approximate the stent deployment?\",\"answer\":\"It uses a non-intrusive scheme combining proper orthogonal decomposition with Gaussian process regression to obtain fast predictions from finite-element simulations.\"},{\"question\":\"How accurate are the predictions and under what dataset sizes?\",\"answer\":\"For deployment outcome classification, models reach up to 95% accuracy; the support vector machine reaches 93% accuracy and 97% specificity with 50 training cases. For configuration prediction, validation errors are not greater than the imaging spatial resolution, and improve further when training simulations increase from 47 to 147.\"}]","Machine learning and reduced order modelling for the simulation of braided stent deployment | PDF",1785900092,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-reduced-order-modelling-for-the-simulation-of-braided-stent-deployment","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-reduced-order-modelling-for-the-simulation-of-braided-stent-deployment/125588/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What two-stage workflow is used to predict braided stent deployment?","Question",{"text":75,"@type":76},"A first classification step predicts whether deployment will be successful by assessing stent–vessel conformity, followed by a regression step that approximates the deployed stent configuration.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the reduced order modelling approach approximate the stent deployment?",{"text":80,"@type":76},"It uses a non-intrusive scheme combining proper orthogonal decomposition with Gaussian process regression to obtain fast predictions from finite-element simulations.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the predictions and under what dataset sizes?",{"text":84,"@type":76},"For deployment outcome classification, models reach up to 95% accuracy; the support vector machine reaches 93% accuracy and 97% specificity with 50 training cases. 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