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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: Li, Mingliang, Chen, Yidong, Mao, Yujie, Jiang, Mingfeng, Liu, Yujun, Zhan, Yuefu, Li, Xiangying, Su, Caixia, Zhang, Guangming, Zhou, Xiaobo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594980/
https://www.ncbi.nlm.nih.gov/pubmed/34795790
http://dx.doi.org/10.1155/2021/4186648
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author Li, Mingliang
Chen, Yidong
Mao, Yujie
Jiang, Mingfeng
Liu, Yujun
Zhan, Yuefu
Li, Xiangying
Su, Caixia
Zhang, Guangming
Zhou, Xiaobo
author_facet Li, Mingliang
Chen, Yidong
Mao, Yujie
Jiang, Mingfeng
Liu, Yujun
Zhan, Yuefu
Li, Xiangying
Su, Caixia
Zhang, Guangming
Zhou, Xiaobo
author_sort Li, Mingliang
collection PubMed
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.
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spelling pubmed-85949802021-11-17 Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm Li, Mingliang Chen, Yidong Mao, Yujie Jiang, Mingfeng Liu, Yujun Zhan, Yuefu Li, Xiangying Su, Caixia Zhang, Guangming Zhou, Xiaobo Comput Math Methods Med Research Article 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. Hindawi 2021-11-09 /pmc/articles/PMC8594980/ /pubmed/34795790 http://dx.doi.org/10.1155/2021/4186648 Text en Copyright © 2021 Mingliang Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Mingliang
Chen, Yidong
Mao, Yujie
Jiang, Mingfeng
Liu, Yujun
Zhan, Yuefu
Li, Xiangying
Su, Caixia
Zhang, Guangming
Zhou, Xiaobo
Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594980/
https://www.ncbi.nlm.nih.gov/pubmed/34795790
http://dx.doi.org/10.1155/2021/4186648
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