Alan Wang, Tam T Doan, Charitha Reddy, Pei-Ni Jone
{"title":"Artificial Intelligence in Fetal and Pediatric Echocardiography.","authors":"Alan Wang, Tam T Doan, Charitha Reddy, Pei-Ni Jone","doi":"10.3390/children12010014","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":48588,"journal":{"name":"Children-Basel","volume":"12 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11764430/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Children-Basel","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/children12010014","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PEDIATRICS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
Children is an international, open access journal dedicated to a streamlined, yet scientifically rigorous, dissemination of peer-reviewed science related to childhood health and disease in developed and developing countries.
The publication focuses on sharing clinical, epidemiological and translational science relevant to children’s health. Moreover, the primary goals of the publication are to highlight under‑represented pediatric disciplines, to emphasize interdisciplinary research and to disseminate advances in knowledge in global child health. In addition to original research, the journal publishes expert editorials and commentaries, clinical case reports, and insightful communications reflecting the latest developments in pediatric medicine. By publishing meritorious articles as soon as the editorial review process is completed, rather than at predefined intervals, Children also permits rapid open access sharing of new information, allowing us to reach the broadest audience in the most expedient fashion.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
