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Updated: Jun 30, 2025
Cardiopulmonary Bypass in a Mouse Model: A Novel Approach
Published on: September 22, 2017
The Number of Microbubbles Generated During Cardiopulmonary Bypass Can Be Estimated Using Machine Learning From
Satoshi Miyamoto1,2, Zu Soh3, Shigeyuki Okahara4
1Department of System Cybernetics, Graduate School of EngineeringHiroshima University Higashihiroshima 739-8527 Japan.
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 (R > 0.95, p < 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 (R = 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.
