Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review
Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhan...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Children |
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| Online Access: | https://www.mdpi.com/2227-9067/12/1/25 |
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| author | David M. Leone Donnchadh O’Sullivan Katia Bravo-Jaimes |
| author_facet | David M. Leone Donnchadh O’Sullivan Katia Bravo-Jaimes |
| author_sort | David M. Leone |
| collection | DOAJ |
| description | Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field. It describes a broad range of AI methodologies, investigates the unique challenges inherent in pediatric ECG analysis, reviews the current state of the literature in pediatric AI-ECG, and discusses potential future directions for research and clinical practice. While AI-ECG applications have demonstrated considerable promise, widespread clinical adoption necessitates further research, rigorous validation, and careful consideration of equity, ethical, legal, and practical challenges. |
| format | Article |
| id | doaj-art-b25d156f2f0843fc8aaf8bcefdda3a06 |
| institution | Kabale University |
| issn | 2227-9067 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj-art-b25d156f2f0843fc8aaf8bcefdda3a062025-01-24T13:27:02ZengMDPI AGChildren2227-90672024-12-011212510.3390/children12010025Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive ReviewDavid M. Leone0Donnchadh O’Sullivan1Katia Bravo-Jaimes2Cincinnati Children’s Hospital Heart Institute, University of Cincinnati, Cincinnati, OH 45229, USADepartment of Pediatric Cardiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX 77030, USADepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL 32224, USAArtificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field. It describes a broad range of AI methodologies, investigates the unique challenges inherent in pediatric ECG analysis, reviews the current state of the literature in pediatric AI-ECG, and discusses potential future directions for research and clinical practice. While AI-ECG applications have demonstrated considerable promise, widespread clinical adoption necessitates further research, rigorous validation, and careful consideration of equity, ethical, legal, and practical challenges.https://www.mdpi.com/2227-9067/12/1/25artificial intelligenceelectrocardiogrammachine learningdeep learningconvolutional neural networks |
| spellingShingle | David M. Leone Donnchadh O’Sullivan Katia Bravo-Jaimes Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review Children artificial intelligence electrocardiogram machine learning deep learning convolutional neural networks |
| title | Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review |
| title_full | Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review |
| title_fullStr | Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review |
| title_full_unstemmed | Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review |
| title_short | Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review |
| title_sort | artificial intelligence in pediatric electrocardiography a comprehensive review |
| topic | artificial intelligence electrocardiogram machine learning deep learning convolutional neural networks |
| url | https://www.mdpi.com/2227-9067/12/1/25 |
| work_keys_str_mv | AT davidmleone artificialintelligenceinpediatricelectrocardiographyacomprehensivereview AT donnchadhosullivan artificialintelligenceinpediatricelectrocardiographyacomprehensivereview AT katiabravojaimes artificialintelligenceinpediatricelectrocardiographyacomprehensivereview |