Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application Study

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 the surgeons to deal with it. To this end, we previously proposed a neural network-based model for estimating the number of MBs from four factors measurable from the cardiopulmonary bypass system: suction flow rate, venous reservoir level, blood viscosity, and perfusion flow rate. However, the model has not been adapted to the data collected from actual surgery cases. In this study, the accuracy of MBs estimated by the proposed model was examined in four clinical cases. The results showed that the coefficient of determination between estimated MBs and the measured MBs throughout the surgeries was R2=0.558 (p<0.001). We found that the surgical treatments, such as administration of drugs, fluids and blood transfusions, increased the number of measured MBs. The coefficient of determination increased to R
²
= 0.8762 (p<0.001) by excluding the duration of these treatments. This result indicates that the model can estimate the number of MBs with high accuracy under the clinical environment.