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

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

“…The proposed system was connected to the CPB circuit, and the following parameters were measured: blood temperature, cardiotomy suction, flow rate, venous reservoir level, perfusion flow rate, and Hct values. Furthermore, using our previous system [36] , [37] , blood viscosity was estimated in real time using inlet and outlet pressures of the membrane artificial lung and perfusion flow rate. The MB count rate was estimated using measured and estimated parameters.…”

“…To differentiate the proposed model from our previous one, it is referred to as the five-factor input model. In our previous studies [36] , [37] , we used blood viscosity (V) as the input for the neural network model. This is because blood viscosity affects the buoyancy and ascent velocity of MBs, which in turn influences the residence duration of MBs and ultimately affects the number of MBs delivered to a patient's body.…”

“…The number of MBs delivered to patients during CPB can be reduced by minimizing the number of MBs counted at the outlet of the venous reservoir [35] . In our previous in vitro experiments using bovine blood, four factors were strongly associated with MB generation: cardiotomy suction, venous reservoir level, perfusion flow rate, and blood viscosity [36] , [37] . Blood viscosity, however, is influenced by various subfactors including blood temperature, hematocrit (Hct), and blood cell morphology, and the specific contribution of these subfactors to the MB count rate remains still unknown.…”