Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm

Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage a...

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Autores principales:	Mingliang Li, Yidong Chen, Yujie Mao, Mingfeng Jiang, Yujun Liu, Yuefu Zhan, Xiangying Li, Caixia Su, Guangming Zhang, Xiaobo Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/4755cdd795ed41e584eb324676956110
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spelling	oai:doaj.org-article:4755cdd795ed41e584eb3246769561102021-11-22T01:09:36ZDiagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm1748-671810.1155/2021/4186648https://doaj.org/article/4755cdd795ed41e584eb3246769561102021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4186648https://doaj.org/toc/1748-6718Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac MRI. However, it takes time for cardiologists to measure DCM-related parameters to decide whether patients have this disease. We have presented a method for automatic ventricular segmentation, parameter extraction, and diagnosing DCM. In this paper, left ventricle and right ventricle are segmented by parasternal short-axis cardiac MR image sequence; then, related parameters are extracted in the end-diastole and end-systole of the heart. Machine learning classifiers use extracted parameters as input to predict normal people and patients with DCM, among which Random forest classifier gives the highest accuracy. The results show that the proposed system can be effectively utilized to detect and diagnose DCM automatically. The experimental results suggest the capabilities and advantages of the proposed method to diagnose DCM. A small amount of sample input can generate results comparable to more complex methods.Mingliang LiYidong ChenYujie MaoMingfeng JiangYujun LiuYuefu ZhanXiangying LiCaixia SuGuangming ZhangXiaobo ZhouHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Mingliang Li Yidong Chen Yujie Mao Mingfeng Jiang Yujun Liu Yuefu Zhan Xiangying Li Caixia Su Guangming Zhang Xiaobo Zhou Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
description	Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac MRI. However, it takes time for cardiologists to measure DCM-related parameters to decide whether patients have this disease. We have presented a method for automatic ventricular segmentation, parameter extraction, and diagnosing DCM. In this paper, left ventricle and right ventricle are segmented by parasternal short-axis cardiac MR image sequence; then, related parameters are extracted in the end-diastole and end-systole of the heart. Machine learning classifiers use extracted parameters as input to predict normal people and patients with DCM, among which Random forest classifier gives the highest accuracy. The results show that the proposed system can be effectively utilized to detect and diagnose DCM automatically. The experimental results suggest the capabilities and advantages of the proposed method to diagnose DCM. A small amount of sample input can generate results comparable to more complex methods.
format	article
author	Mingliang Li Yidong Chen Yujie Mao Mingfeng Jiang Yujun Liu Yuefu Zhan Xiangying Li Caixia Su Guangming Zhang Xiaobo Zhou
author_facet	Mingliang Li Yidong Chen Yujie Mao Mingfeng Jiang Yujun Liu Yuefu Zhan Xiangying Li Caixia Su Guangming Zhang Xiaobo Zhou
author_sort	Mingliang Li
title	Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
title_short	Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
title_full	Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
title_fullStr	Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
title_full_unstemmed	Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
title_sort	diagnostic classification of patients with dilated cardiomyopathy using ventricular strain analysis algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4755cdd795ed41e584eb324676956110
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Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm

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