Accurately assessing congenital heart disease using artificial intelligence
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 pre...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2535.pdf |
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| _version_ | 1850107440165552128 |
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| author | Khalil Khan Farhan Ullah Ikram Syed Hashim Ali |
| author_facet | Khalil Khan Farhan Ullah Ikram Syed Hashim Ali |
| author_sort | Khalil Khan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-38f5d5e899dd45798d07f708e732ab70 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-38f5d5e899dd45798d07f708e732ab702025-08-20T02:38:35ZengPeerJ Inc.PeerJ Computer Science2376-59922024-11-0110e253510.7717/peerj-cs.2535Accurately assessing congenital heart disease using artificial intelligenceKhalil Khan0Farhan Ullah1Ikram Syed2Hashim Ali3Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, KazakhstanCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaDept of Information and Communication Engineering, Hankuk University of Foreign Studies, Yongin, Gyeonggy-do, Republic of South KoreaDepartment of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, KazakhstanCongenital 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.https://peerj.com/articles/cs-2535.pdfCongenital heart diseaseParental ultrasoundCritical aortic stenosisHypoplastic left heart syndromeEchocardiographyML algorithms |
| spellingShingle | Khalil Khan Farhan Ullah Ikram Syed Hashim Ali Accurately assessing congenital heart disease using artificial intelligence PeerJ Computer Science Congenital heart disease Parental ultrasound Critical aortic stenosis Hypoplastic left heart syndrome Echocardiography ML algorithms |
| title | Accurately assessing congenital heart disease using artificial intelligence |
| title_full | Accurately assessing congenital heart disease using artificial intelligence |
| title_fullStr | Accurately assessing congenital heart disease using artificial intelligence |
| title_full_unstemmed | Accurately assessing congenital heart disease using artificial intelligence |
| title_short | Accurately assessing congenital heart disease using artificial intelligence |
| title_sort | accurately assessing congenital heart disease using artificial intelligence |
| topic | Congenital heart disease Parental ultrasound Critical aortic stenosis Hypoplastic left heart syndrome Echocardiography ML algorithms |
| url | https://peerj.com/articles/cs-2535.pdf |
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