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The Role of Artificial Intelligence in Echocardiography
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiogr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962859/ https://www.ncbi.nlm.nih.gov/pubmed/36826969 http://dx.doi.org/10.3390/jimaging9020050 |
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author | Barry, Timothy Farina, Juan Maria Chao, Chieh-Ju Ayoub, Chadi Jeong, Jiwoong Patel, Bhavik N. Banerjee, Imon Arsanjani, Reza |
author_facet | Barry, Timothy Farina, Juan Maria Chao, Chieh-Ju Ayoub, Chadi Jeong, Jiwoong Patel, Bhavik N. Banerjee, Imon Arsanjani, Reza |
author_sort | Barry, Timothy |
collection | PubMed |
description | Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care. |
format | Online Article Text |
id | pubmed-9962859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99628592023-02-26 The Role of Artificial Intelligence in Echocardiography Barry, Timothy Farina, Juan Maria Chao, Chieh-Ju Ayoub, Chadi Jeong, Jiwoong Patel, Bhavik N. Banerjee, Imon Arsanjani, Reza J Imaging Review Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care. MDPI 2023-02-20 /pmc/articles/PMC9962859/ /pubmed/36826969 http://dx.doi.org/10.3390/jimaging9020050 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Barry, Timothy Farina, Juan Maria Chao, Chieh-Ju Ayoub, Chadi Jeong, Jiwoong Patel, Bhavik N. Banerjee, Imon Arsanjani, Reza The Role of Artificial Intelligence in Echocardiography |
title | The Role of Artificial Intelligence in Echocardiography |
title_full | The Role of Artificial Intelligence in Echocardiography |
title_fullStr | The Role of Artificial Intelligence in Echocardiography |
title_full_unstemmed | The Role of Artificial Intelligence in Echocardiography |
title_short | The Role of Artificial Intelligence in Echocardiography |
title_sort | role of artificial intelligence in echocardiography |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9962859/ https://www.ncbi.nlm.nih.gov/pubmed/36826969 http://dx.doi.org/10.3390/jimaging9020050 |
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