Accurately assessing congenital heart disease using artificial intelligence

Khan K. Ullah F. Syed I. Ali H.
2024 PeerJ Inc.

PeerJ Computer Science
2024 #10 1 - 43 pp.

Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.

Artificial intelligence , Congenital heart disease , Critical aortic stenosis , Echocardiography , Hypoplastic left heart syndrome , ML algorithms , Parental ultrasound

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Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Dept of Information and Communication Engineering, Hankuk University of Foreign Studies, Gyeonggy-do, Yongin, South Korea

Department of Computer Science
College of Computer Science and Software Engineering
Dept of Information and Communication Engineering

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