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Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment

The use of in silico tools for the interventional planning of complex vascular conditions, such as Aortic Dissections has been often limited by high computational cost, involving long timescales for accurate results to be produced and low numbers of patients, precluding the use of statistical analys...

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Autores principales: Diaz-Zuccarini, Vanessa, Bonfanti, Mirko, Franzetti, Gaia, Balabani, Stavroula
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299687/
https://www.ncbi.nlm.nih.gov/pubmed/30613306
http://dx.doi.org/10.7150/thno.30753
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author Diaz-Zuccarini, Vanessa
Bonfanti, Mirko
Franzetti, Gaia
Balabani, Stavroula
author_facet Diaz-Zuccarini, Vanessa
Bonfanti, Mirko
Franzetti, Gaia
Balabani, Stavroula
author_sort Diaz-Zuccarini, Vanessa
collection PubMed
description The use of in silico tools for the interventional planning of complex vascular conditions, such as Aortic Dissections has been often limited by high computational cost, involving long timescales for accurate results to be produced and low numbers of patients, precluding the use of statistical analyses to inform individual-level models. In the paper [Theranostics 2018; 8(20):5758-5771. doi:10.7150/thno.28944], Chen et al. proposed a novel algorithm to compute patient-specific 'virtual TEVAR' that will help clinicians to approach individual treatment and decision-making based on objective and quantifiable metrics and validated on a cohort of 66 patients in real time. This research will significantly impact the field and has the potential to transform the way clinical interventions will be approached in the future.
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spelling pubmed-62996872019-01-04 Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment Diaz-Zuccarini, Vanessa Bonfanti, Mirko Franzetti, Gaia Balabani, Stavroula Theranostics Editorial The use of in silico tools for the interventional planning of complex vascular conditions, such as Aortic Dissections has been often limited by high computational cost, involving long timescales for accurate results to be produced and low numbers of patients, precluding the use of statistical analyses to inform individual-level models. In the paper [Theranostics 2018; 8(20):5758-5771. doi:10.7150/thno.28944], Chen et al. proposed a novel algorithm to compute patient-specific 'virtual TEVAR' that will help clinicians to approach individual treatment and decision-making based on objective and quantifiable metrics and validated on a cohort of 66 patients in real time. This research will significantly impact the field and has the potential to transform the way clinical interventions will be approached in the future. Ivyspring International Publisher 2018-12-07 /pmc/articles/PMC6299687/ /pubmed/30613306 http://dx.doi.org/10.7150/thno.30753 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Editorial
Diaz-Zuccarini, Vanessa
Bonfanti, Mirko
Franzetti, Gaia
Balabani, Stavroula
Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment
title Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment
title_full Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment
title_fullStr Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment
title_full_unstemmed Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment
title_short Virtual TEVAR: Overcoming the Roadblocks of In-Silico Tools for Aortic Dissection Treatment
title_sort virtual tevar: overcoming the roadblocks of in-silico tools for aortic dissection treatment
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299687/
https://www.ncbi.nlm.nih.gov/pubmed/30613306
http://dx.doi.org/10.7150/thno.30753
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