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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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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. |
format | Online Article Text |
id | pubmed-8594980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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