Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease

Xiangyu Liu; Xiangyu Liu; Yingying Zhang; Yingying Zhang; Haogang Zhu; Haogang Zhu; Haogang Zhu; Bosen Jia; Jingyi Wang; Jingyi Wang; Yihua He; Yihua He; Hongjia Zhang; Hongjia Zhang

doi:10.3389/fcvm.2024.1345761

Frontiers in Cardiovascular Medicine (Apr 2024)

Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease

Xiangyu Liu,
Xiangyu Liu,
Yingying Zhang,
Yingying Zhang,
Haogang Zhu,
Haogang Zhu,
Haogang Zhu,
Bosen Jia,
Jingyi Wang,
Jingyi Wang,
Yihua He,
Yihua He,
Hongjia Zhang,
Hongjia Zhang

Affiliations

Xiangyu Liu: School of Biological Science and Medical Engineering, Beihang University, Beijing, China
Xiangyu Liu: Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
Yingying Zhang: School of Biological Science and Medical Engineering, Beihang University, Beijing, China
Yingying Zhang: Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
Haogang Zhu: Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
Haogang Zhu: State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
Haogang Zhu: School of Computer Science and Engineering, Beihang University, Beijing, China
Bosen Jia: School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
Jingyi Wang: Echocardiography Medical Center Beijing Anzhen Hospital, Capital Medical University, Beijing, China
Jingyi Wang: Maternal-Fetal Medicine Center in Fetal Heart Disease, Beijing Anzhen Hospital, Beijing, China
Yihua He: Echocardiography Medical Center Beijing Anzhen Hospital, Capital Medical University, Beijing, China
Yihua He: Maternal-Fetal Medicine Center in Fetal Heart Disease, Beijing Anzhen Hospital, Beijing, China
Hongjia Zhang: Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Hangzhou, China
Hongjia Zhang: Beijing Lab for Cardiovascular Precision Medicine, Beijing, China

DOI: https://doi.org/10.3389/fcvm.2024.1345761
Journal volume & issue: Vol. 11

Abstract

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Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.

Published in Frontiers in Cardiovascular Medicine

ISSN: 2297-055X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Internal medicine: Specialties of internal medicine: Diseases of the circulatory (Cardiovascular) system
Website: https://www.frontiersin.org/journals/cardiovascular-medicine

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