Artificial intelligence-assisted organoid construction in congenital heart disease: current applications and future prospects

Abstract

Congenital heart disease (CHD) is a complex group of cardiac abnormalities arising during fetal development. Despite advancements in diagnostics and surgery, CHD mechanisms remain elusive due to inadequate disease models. Recent innovations in artificial intelligence (AI)-assisted organoid construction, which replicate tissue architecture and function, provide a promising in vitro platform for modeling cardiac development and CHD progression with high precision. This review summarizes AI-driven approaches in CHD organoid construction, focusing on machine learning (ML) applications in self-assembly, three-dimensional (3D) bioprinting, tissue engineering, and microfluidic organ-on-a-chip (OOC) technologies. We also discuss refinements in AI algorithms - such as support vector machines (SVMs), decision trees, and neural networks - to enhance cell-cell interaction analysis, optimize drug screening, and improve toxicity/efficacy assessments. Looking ahead, AI is poised to accelerate CHD organoid translation to clinical practice, advancing precision medicine.