The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature
Goal: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10382573/ |
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| 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 |
| collection | DOAJ |
| description | Goal: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature. Methods: Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters. Results: Bland–Altman analysis indicated a high estimation accuracy (<italic>R<sup>2</sup></italic> > 0.95, <italic>p</italic> < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases (<italic>R<sup>2</sup></italic> = 0.8576) was achieved between measured and estimated MB count rates. Conclusions: Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk. |
| format | Article |
| id | doaj-art-35dc66359b344ed692e52558f2f5052a |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-35dc66359b344ed692e52558f2f5052a2025-01-30T00:03:36ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-015667410.1109/OJEMB.2024.335092210382573The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood TemperatureSatoshi Miyamoto0https://orcid.org/0000-0003-1005-8794Zu Soh1Shigeyuki Okahara2https://orcid.org/0000-0002-7222-333XAkira Furui3https://orcid.org/0000-0003-1554-6607Taiichi Takasaki4Keijiro Katayama5Shinya Takahashi6https://orcid.org/0000-0002-5339-6534Toshio Tsuji7https://orcid.org/0000-0002-7689-3963Department of System Cybernetics, Graduate School of Engineering, Hiroshima University, Higashihiroshima, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, JapanGraduate School of Health Sciences, Junshin Gakuen University, Fukuoka, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, JapanDepartment of Cardiovascular Surgery, Hiroshima University Hospital, Hiroshima, JapanDepartment of Cardiovascular Surgery, Hiroshima University Hospital, Hiroshima, JapanDepartment of Cardiovascular Surgery, Hiroshima University Hospital, Hiroshima, JapanGraduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, JapanGoal: Microbubbles (MBs) are known to occur within the circuits of cardiopulmonary bypass (CPB) systems, and higher-order dysfunction after cardiac surgery may be caused by MBs as well as atheroma dispersal associated with cannula insertion. As complete MB elimination is not possible, monitoring MB count rates is critical. We propose an online detection system with a neural network-based model to estimate MB count rate using five parameters: suction flow rate, venous reservoir level, perfusion flow rate, hematocrit level, and blood temperature. Methods: Perfusion experiments were performed using an actual CPB circuit, and MB count rates were measured using the five varying parameters. Results: Bland–Altman analysis indicated a high estimation accuracy (<italic>R<sup>2</sup></italic> > 0.95, <italic>p</italic> < 0.001) with no significant systematic error. In clinical practice, although the inclusion of clinical procedures slightly decreased the estimation accuracy, a high coefficient of determination for 30 clinical cases (<italic>R<sup>2</sup></italic> = 0.8576) was achieved between measured and estimated MB count rates. Conclusions: Our results highlight the potential of this system to improve patient outcomes and reduce MB-associated complication risk.https://ieeexplore.ieee.org/document/10382573/Cardiac surgerycardiopulmonary bypassmicrobubblesneural networkonline detection |
| spellingShingle | Satoshi Miyamoto Zu Soh Shigeyuki Okahara Akira Furui Taiichi Takasaki Keijiro Katayama Shinya Takahashi Toshio Tsuji The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature IEEE Open Journal of Engineering in Medicine and Biology Cardiac surgery cardiopulmonary bypass microbubbles neural network online detection |
| title | The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature |
| title_full | The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature |
| title_fullStr | The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature |
| title_full_unstemmed | The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature |
| title_short | The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From Suction Flow Rate, Venous Reservoir Level, Perfusion Flow Rate, Hematocrit Level, and Blood Temperature |
| title_sort | number of microbubbles generated during cardiopulmonary bypass can be estimated using machine learning from suction flow rate venous reservoir level perfusion flow rate hematocrit level and blood temperature |
| topic | Cardiac surgery cardiopulmonary bypass microbubbles neural network online detection |
| url | https://ieeexplore.ieee.org/document/10382573/ |
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