Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

Anzai et al. propose a deep learning approach to estimate the 3D hemodynamics of complex aorta-coronary artery geometry in the context of coronary artery bypass surgery. Their method reduces the calculation time 600-fold, while allowing high resolution and similar accuracy as traditional computation...

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Autores principales:	Gaoyang Li, Haoran Wang, Mingzi Zhang, Simon Tupin, Aike Qiao, Youjun Liu, Makoto Ohta, Hitomi Anzai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f908395dcdb74fa3815c2468a4e3b0a5
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spelling	oai:doaj.org-article:f908395dcdb74fa3815c2468a4e3b0a52021-12-02T13:51:05ZPrediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning10.1038/s42003-020-01638-12399-3642https://doaj.org/article/f908395dcdb74fa3815c2468a4e3b0a52021-01-01T00:00:00Zhttps://doi.org/10.1038/s42003-020-01638-1https://doaj.org/toc/2399-3642Anzai et al. propose a deep learning approach to estimate the 3D hemodynamics of complex aorta-coronary artery geometry in the context of coronary artery bypass surgery. Their method reduces the calculation time 600-fold, while allowing high resolution and similar accuracy as traditional computational fluid dynamics (CFD) method.Gaoyang LiHaoran WangMingzi ZhangSimon TupinAike QiaoYoujun LiuMakoto OhtaHitomi AnzaiNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Gaoyang Li Haoran Wang Mingzi Zhang Simon Tupin Aike Qiao Youjun Liu Makoto Ohta Hitomi Anzai Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
description	Anzai et al. propose a deep learning approach to estimate the 3D hemodynamics of complex aorta-coronary artery geometry in the context of coronary artery bypass surgery. Their method reduces the calculation time 600-fold, while allowing high resolution and similar accuracy as traditional computational fluid dynamics (CFD) method.
format	article
author	Gaoyang Li Haoran Wang Mingzi Zhang Simon Tupin Aike Qiao Youjun Liu Makoto Ohta Hitomi Anzai
author_facet	Gaoyang Li Haoran Wang Mingzi Zhang Simon Tupin Aike Qiao Youjun Liu Makoto Ohta Hitomi Anzai
author_sort	Gaoyang Li
title	Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
title_short	Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
title_full	Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
title_fullStr	Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
title_full_unstemmed	Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
title_sort	prediction of 3d cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f908395dcdb74fa3815c2468a4e3b0a5
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Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

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