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Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass
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 obstruct...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804121/ https://www.ncbi.nlm.nih.gov/pubmed/33436919 http://dx.doi.org/10.1038/s41598-020-80810-3 |
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author | Miyamoto, Satoshi Soh, Zu Okahara, Shigeyuki Furui, Akira Takasaki, Taiichi Katayama, Keijiro Takahashi, Shinya Tsuji, Toshio |
author_facet | Miyamoto, Satoshi Soh, Zu Okahara, Shigeyuki Furui, Akira Takasaki, Taiichi Katayama, Keijiro Takahashi, Shinya Tsuji, Toshio |
author_sort | Miyamoto, Satoshi |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-7804121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78041212021-01-13 Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass Miyamoto, Satoshi Soh, Zu Okahara, Shigeyuki Furui, Akira Takasaki, Taiichi Katayama, Keijiro Takahashi, Shinya Tsuji, Toshio Sci Rep Article 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. Nature Publishing Group UK 2021-01-12 /pmc/articles/PMC7804121/ /pubmed/33436919 http://dx.doi.org/10.1038/s41598-020-80810-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Miyamoto, Satoshi Soh, Zu Okahara, Shigeyuki Furui, Akira Takasaki, Taiichi Katayama, Keijiro Takahashi, Shinya Tsuji, Toshio Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804121/ https://www.ncbi.nlm.nih.gov/pubmed/33436919 http://dx.doi.org/10.1038/s41598-020-80810-3 |
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