Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

Abstract The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular...

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Autores principales:	Satoshi Miyamoto, Zu Soh, Shigeyuki Okahara, Akira Furui, Taiichi Takasaki, Keijiro Katayama, Shinya Takahashi, Toshio Tsuji
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1c39fcc816c5413c9d3efdc00ca449f4
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spelling	oai:doaj.org-article:1c39fcc816c5413c9d3efdc00ca449f42021-12-02T15:22:56ZNeural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass10.1038/s41598-020-80810-32045-2322https://doaj.org/article/1c39fcc816c5413c9d3efdc00ca449f42021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80810-3https://doaj.org/toc/2045-2322Abstract The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R 2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.Satoshi MiyamotoZu SohShigeyuki OkaharaAkira FuruiTaiichi TakasakiKeijiro KatayamaShinya TakahashiToshio TsujiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Satoshi Miyamoto Zu Soh Shigeyuki Okahara Akira Furui Taiichi Takasaki Keijiro Katayama Shinya Takahashi Toshio Tsuji Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
description	Abstract The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R 2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.
format	article
author	Satoshi Miyamoto Zu Soh Shigeyuki Okahara Akira Furui Taiichi Takasaki Keijiro Katayama Shinya Takahashi Toshio Tsuji
author_facet	Satoshi Miyamoto Zu Soh Shigeyuki Okahara Akira Furui Taiichi Takasaki Keijiro Katayama Shinya Takahashi Toshio Tsuji
author_sort	Satoshi Miyamoto
title	Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
title_short	Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
title_full	Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
title_fullStr	Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
title_full_unstemmed	Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
title_sort	neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1c39fcc816c5413c9d3efdc00ca449f4
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Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

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