Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients...

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Autores principales:	Lingxia Zhu, Zhiping Xu, Ting Fang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/d4f512ba59ac44948008e9acebfd4fd1
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id	oai:doaj.org-article:d4f512ba59ac44948008e9acebfd4fd1
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spelling	oai:doaj.org-article:d4f512ba59ac44948008e9acebfd4fd12021-11-08T02:37:29ZAnalysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning2040-230910.1155/2021/6050433https://doaj.org/article/d4f512ba59ac44948008e9acebfd4fd12021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6050433https://doaj.org/toc/2040-2309Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.Lingxia ZhuZhiping XuTing FangHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Lingxia Zhu Zhiping Xu Ting Fang Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
description	Cardiovascular disease remains a substantial cause of morbidity and mortality in the developed world and is becoming an increasingly important cause of death in developing countries too. While current cardiovascular treatments can assist to reduce the risk of this disease, a large number of patients still retain a high risk of experiencing a life-threatening cardiovascular event. Thus, the advent of new treatments methods capable of reducing this residual risk remains an important healthcare objective. This paper proposes a deep learning-based method for section recognition of cardiac ultrasound images of critically ill cardiac patients. A convolution neural network (CNN) is used to classify the standard ultrasound video data. The ultrasound video data is parsed into a static image, and InceptionV3 and ResNet50 networks are used to classify eight ultrasound static sections, and the ResNet50 with better classification accuracy is selected as the standard network for classification. The correlation between the ultrasound video data frames is used to construct the ResNet50 + LSTM model. Next, the time-series features of the two-dimensional image sequence are extracted and the classification of the ultrasound section video data is realized. Experimental results show that the proposed cardiac ultrasound image recognition model has good performance and can meet the requirements of clinical section classification accuracy.
format	article
author	Lingxia Zhu Zhiping Xu Ting Fang
author_facet	Lingxia Zhu Zhiping Xu Ting Fang
author_sort	Lingxia Zhu
title	Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
title_short	Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
title_full	Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
title_fullStr	Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
title_full_unstemmed	Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning
title_sort	analysis of cardiac ultrasound images of critically ill patients using deep learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d4f512ba59ac44948008e9acebfd4fd1
work_keys_str_mv	AT lingxiazhu analysisofcardiacultrasoundimagesofcriticallyillpatientsusingdeeplearning AT zhipingxu analysisofcardiacultrasoundimagesofcriticallyillpatientsusingdeeplearning AT tingfang analysisofcardiacultrasoundimagesofcriticallyillpatientsusingdeeplearning
_version_	1718443044962828288

Analysis of Cardiac Ultrasound Images of Critically Ill Patients Using Deep Learning

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