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Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment
The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143561/ https://www.ncbi.nlm.nih.gov/pubmed/35629019 http://dx.doi.org/10.3390/jcm11102893 |
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author | Zhang, Zisang Zhu, Ye Liu, Manwei Zhang, Ziming Zhao, Yang Yang, Xin Xie, Mingxing Zhang, Li |
author_facet | Zhang, Zisang Zhu, Ye Liu, Manwei Zhang, Ziming Zhao, Yang Yang, Xin Xie, Mingxing Zhang, Li |
author_sort | Zhang, Zisang |
collection | PubMed |
description | The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field. |
format | Online Article Text |
id | pubmed-9143561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91435612022-05-29 Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment Zhang, Zisang Zhu, Ye Liu, Manwei Zhang, Ziming Zhao, Yang Yang, Xin Xie, Mingxing Zhang, Li J Clin Med Review The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer’s experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field. MDPI 2022-05-20 /pmc/articles/PMC9143561/ /pubmed/35629019 http://dx.doi.org/10.3390/jcm11102893 Text en © 2022 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 Zhang, Zisang Zhu, Ye Liu, Manwei Zhang, Ziming Zhao, Yang Yang, Xin Xie, Mingxing Zhang, Li Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment |
title | Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment |
title_full | Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment |
title_fullStr | Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment |
title_full_unstemmed | Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment |
title_short | Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment |
title_sort | artificial intelligence-enhanced echocardiography for systolic function assessment |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143561/ https://www.ncbi.nlm.nih.gov/pubmed/35629019 http://dx.doi.org/10.3390/jcm11102893 |
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