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 Bl

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Abstract: The cardiopulmonary bypass system used in cardiac surgery can generate microbubbles. (MBs) that may cause complications, such as neurocognitive dysfunction, when delivered into the blood vessel. Estimating the number of MBs generated, thus, is necessary to enable perfusionist to deal with it. Experiments were conducted from the operating conditions of five factors related to the generation of microbubbles. The model yielded a high estimation accuracy with coefficient of determination R²=0.9583. In this study, the accuracy of MBs estimated by the proposed model was examined in five clinical cases. The results showed that the coefficient of determination between estimated MBs and the measured MBs throughout the surgeries was R²=0.8576 (p<0.001). This result indicates that the model can estimate the number of MBs with high accuracy under the clinical environment.