Artificial Intelligence in Fetal and Pediatric Echocardiography
Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in...
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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/2227-9067/12/1/14 |
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| author | Alan Wang Tam T. Doan Charitha Reddy Pei-Ni Jone |
| author_facet | Alan Wang Tam T. Doan Charitha Reddy Pei-Ni Jone |
| author_sort | Alan Wang |
| collection | DOAJ |
| description | Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI’s current applications, challenges, and future directions in fetal and pediatric echocardiography. |
| format | Article |
| id | doaj-art-406ba054ca4540f2b767c69cb40b799e |
| institution | Kabale University |
| issn | 2227-9067 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj-art-406ba054ca4540f2b767c69cb40b799e2025-01-24T13:26:59ZengMDPI AGChildren2227-90672024-12-011211410.3390/children12010014Artificial Intelligence in Fetal and Pediatric EchocardiographyAlan Wang0Tam T. Doan1Charitha Reddy2Pei-Ni Jone3Division of Pediatric Cardiology, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USADivision of Pediatric Cardiology, Department of Pediatrics, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX 77030, USADivision of Pediatric Cardiology, Stanford Children’s Hospital, Palo Alto, CA 94304, USADivision of Pediatric Cardiology, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USAEchocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI’s current applications, challenges, and future directions in fetal and pediatric echocardiography.https://www.mdpi.com/2227-9067/12/1/14artificial intelligencepediatric echocardiographyfetal echocardiographycongenital heart diseasemachine learningdeep learning |
| spellingShingle | Alan Wang Tam T. Doan Charitha Reddy Pei-Ni Jone Artificial Intelligence in Fetal and Pediatric Echocardiography Children artificial intelligence pediatric echocardiography fetal echocardiography congenital heart disease machine learning deep learning |
| title | Artificial Intelligence in Fetal and Pediatric Echocardiography |
| title_full | Artificial Intelligence in Fetal and Pediatric Echocardiography |
| title_fullStr | Artificial Intelligence in Fetal and Pediatric Echocardiography |
| title_full_unstemmed | Artificial Intelligence in Fetal and Pediatric Echocardiography |
| title_short | Artificial Intelligence in Fetal and Pediatric Echocardiography |
| title_sort | artificial intelligence in fetal and pediatric echocardiography |
| topic | artificial intelligence pediatric echocardiography fetal echocardiography congenital heart disease machine learning deep learning |
| url | https://www.mdpi.com/2227-9067/12/1/14 |
| work_keys_str_mv | AT alanwang artificialintelligenceinfetalandpediatricechocardiography AT tamtdoan artificialintelligenceinfetalandpediatricechocardiography AT charithareddy artificialintelligenceinfetalandpediatricechocardiography AT peinijone artificialintelligenceinfetalandpediatricechocardiography |