Advancing Machine Learning-Enhanced Flow Modeling for Collision Phenomena in Total Cavopulmonary Connection

Abstract

The Fontan procedure, essential for newborns with single-ventricle malformations, establishes a total cavopulmonary connection (TCPC). Understanding TCPC hemodynamics is critical for evaluating surgical outcomes. However, the intricate flow collisions within TCPC present a significant challenge. While computational fluid dynamics (CFD) offers insight into these mechanics, its computational inefficiency limits clinical application. Machine learning (ML) shows promise in overcoming this limitation. This study develops an ML framework to analyze TCPC hemodynamics. The ML models are trained to predict the relationship between TCPC flow parameters and two hemodynamic metrics: power loss (PL) and hepatic flow distribution (HFD). Four ML algorithms—random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural networks (ANNs)—are evaluated for their performance using a training dataset of 7056 CFD simulations. All ML models demonstrate strong correlations with CFD results (R2 = 0.95). Notably, the ANN model slightly outperforms others in predicting HFD (R2 = 0.98), while both ANN and XGBoost models achieve similar accuracy in predicting PL (R2 = 0.99). Additionally, the study investigates the feasibility of training an ANN model with a reduced dataset of 968 samples, which can still successfully capture the relationship between flow parameters and TCPC hemodynamics. This study underscores the potential of ML-enabled models to enhance the efficiency of hemodynamic assessments in TCPC with flow collision scenarios. Given that flow collision phenomena are common in various physiological systems and engineering contexts, these findings may drive advancements in ML-augmented flow modeling across a broad range of applications.