REVIEW article
Front. Bioeng. Biotechnol.
Sec. Organoids and Organ-On-A-Chip
This article is part of the Research TopicAdvancing Disease Modeling and Therapy with Organoids and Organ-on-a-ChipView all 3 articles
Artificial Intelligence-Assisted Organoid Construction in Congenital Heart Disease: Current Applications and Future Prospects
Provisionally accepted
Fang Chen1
Hao-Yi Zhang1
Yan-Long Wan1
Jia-Nan Jia1
Rui-Zhen Wang1Cheng Gao1Zhen-Yu Chao1Yu-Hua Ru2,3Zhe Wang4Kai Cheng1Jiong Zhang1
Juan Feng1Jin-Ling Ren1
Dongrui Ma1*Zhen-Qiang Zhang1- 1Henan University of Chinese Medicine, Zhengzhou, China
- 2Jiangsu Institute of Hematology, Suzhou, China
- 3Soochow University, Suzhou, China
- 4Harvard Medical School, Boston, United States
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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.
Keywords: congenital heart disease (CHD), Cardiac organoids, Artificial intelligence (AI), Three-dimensional (3D) bioprinting, Microfluidic technology
Received: 25 Aug 2025; Accepted: 18 Nov 2025.
Copyright: © 2025 Chen, Zhang, Wan, Jia, Wang, Gao, Chao, Ru, Wang, Cheng, Zhang, Feng, Ren, Ma and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dongrui Ma, dongruima@hactcm.edu.cn
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