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

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.本研究凸显了机器学习（Machine Learning）赋能模型在伴有血流碰撞场景的全腔肺吻合术（TCPC）血流动力学评估中提升效率的潜力。鉴于血流碰撞现象在各类生理系统与工程场景中均较为常见，本研究成果可推动机器学习增强型血流建模在广泛应用场景中的技术发展。