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Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes
Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365116/ https://www.ncbi.nlm.nih.gov/pubmed/37492680 http://dx.doi.org/10.3389/fradi.2022.881777 |
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author | Nguyen, Minh B. Villemain, Olivier Friedberg, Mark K. Lovstakken, Lasse Rusin, Craig G. Mertens, Luc |
author_facet | Nguyen, Minh B. Villemain, Olivier Friedberg, Mark K. Lovstakken, Lasse Rusin, Craig G. Mertens, Luc |
author_sort | Nguyen, Minh B. |
collection | PubMed |
description | Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography. |
format | Online Article Text |
id | pubmed-10365116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103651162023-07-25 Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes Nguyen, Minh B. Villemain, Olivier Friedberg, Mark K. Lovstakken, Lasse Rusin, Craig G. Mertens, Luc Front Radiol Radiology Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography. Frontiers Media S.A. 2022-09-09 /pmc/articles/PMC10365116/ /pubmed/37492680 http://dx.doi.org/10.3389/fradi.2022.881777 Text en Copyright © 2022 Nguyen, Villemain, Friedberg, Lovstakken, Rusin and Mertens. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Radiology Nguyen, Minh B. Villemain, Olivier Friedberg, Mark K. Lovstakken, Lasse Rusin, Craig G. Mertens, Luc Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes |
title | Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes |
title_full | Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes |
title_fullStr | Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes |
title_full_unstemmed | Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes |
title_short | Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes |
title_sort | artificial intelligence in the pediatric echocardiography laboratory: automation, physiology, and outcomes |
topic | Radiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365116/ https://www.ncbi.nlm.nih.gov/pubmed/37492680 http://dx.doi.org/10.3389/fradi.2022.881777 |
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