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Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images
Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was p...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168207/ https://www.ncbi.nlm.nih.gov/pubmed/35685669 http://dx.doi.org/10.1155/2022/5616939 |
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author | Raghavendra, U. En Wei, Joel Koh Gudigar, Anjan Shetty, Akanksha Samanth, Jyothi Paramasivam, Ganesh Jagadish, Sujay Kadri, Nahrizul Adib Karabatak, Murat Yildirim, Özal Arunkumar, N. Ardakani, Ali Abbasian |
author_facet | Raghavendra, U. En Wei, Joel Koh Gudigar, Anjan Shetty, Akanksha Samanth, Jyothi Paramasivam, Ganesh Jagadish, Sujay Kadri, Nahrizul Adib Karabatak, Murat Yildirim, Özal Arunkumar, N. Ardakani, Ali Abbasian |
author_sort | Raghavendra, U. |
collection | PubMed |
description | Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer-aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics. |
format | Online Article Text |
id | pubmed-9168207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-91682072022-06-08 Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images Raghavendra, U. En Wei, Joel Koh Gudigar, Anjan Shetty, Akanksha Samanth, Jyothi Paramasivam, Ganesh Jagadish, Sujay Kadri, Nahrizul Adib Karabatak, Murat Yildirim, Özal Arunkumar, N. Ardakani, Ali Abbasian Contrast Media Mol Imaging Research Article Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.6% of men and 20.9% of women. It was estimated that this trend will increase to 22.9% and 23.6% for men and women, respectively, by 2025. Controlling blood pressure is therefore important to lower both morbidity and mortality. Computer-aided diagnosis (CAD) is a noninvasive technique which can determine subtle myocardial structural changes at an early stage. In this work, we show how a multi-resolution analysis-based CAD system can be utilized for the detection of early HTN-induced left ventricular heart muscle changes with the help of ultrasound imaging. Firstly, features were extracted from the ultrasound imagery, and then the feature dimensions were reduced using a locality sensitive discriminant analysis (LSDA). The decision tree classifier with contourlet and shearlet transform features was later employed for improved performance and maximized accuracy using only two features. The developed model is applicable for the evaluation of cardiac structural alteration in HTN and can be used as a standalone tool in hospitals and polyclinics. Hindawi 2022-05-29 /pmc/articles/PMC9168207/ /pubmed/35685669 http://dx.doi.org/10.1155/2022/5616939 Text en Copyright © 2022 U. Raghavendra 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 Raghavendra, U. En Wei, Joel Koh Gudigar, Anjan Shetty, Akanksha Samanth, Jyothi Paramasivam, Ganesh Jagadish, Sujay Kadri, Nahrizul Adib Karabatak, Murat Yildirim, Özal Arunkumar, N. Ardakani, Ali Abbasian Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images |
title | Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images |
title_full | Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images |
title_fullStr | Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images |
title_full_unstemmed | Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images |
title_short | Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images |
title_sort | automated diagnosis and assessment of cardiac structural alteration in hypertension ultrasound images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9168207/ https://www.ncbi.nlm.nih.gov/pubmed/35685669 http://dx.doi.org/10.1155/2022/5616939 |
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