Accurately assessing congenital heart disease using artificial intelligence

• Published in: PeerJ. Computer science Vol. 10; p. e2535
• Main Authors: Khan, Khalil, Ullah, Farhan, Syed, Ikram, Ali, Hashim
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29.11.2024; PeerJ, Inc; PeerJ Inc
• Subjects: Algorithms; Aortic stenosis; Artificial intelligence; Births; Cardiovascular disease; Congenital diseases; Congenital heart disease; Critical aortic stenosis; Deep learning; Diagnosis; Echocardiography; Genetic disorders; Health care; Heart; Heart diseases; Hypoplastic left heart syndrome; Infants; Machine learning; Medical diagnosis; Medical research; Medicine, Experimental; ML algorithms; Mortality; Neural networks; Newborn babies; Parental ultrasound; Prediction models; Public health; United States; United States; China; Echocardiography; Hypoplastic left heart syndrome; Parental ultrasound; ML algorithms; Critical aortic stenosis; Congenital heart disease; Artificial intelligence
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2535

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.