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Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders

Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim t...

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Autores principales: Qu, Yimin, Lee, Jack Jock-Wai, Zhuo, Yuanyuan, Liu, Shukai, Thomas, Rebecca L., Owens, David R., Zee, Benny Chung-Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143834/
https://www.ncbi.nlm.nih.gov/pubmed/35628812
http://dx.doi.org/10.3390/jcm11102687
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author Qu, Yimin
Lee, Jack Jock-Wai
Zhuo, Yuanyuan
Liu, Shukai
Thomas, Rebecca L.
Owens, David R.
Zee, Benny Chung-Ying
author_facet Qu, Yimin
Lee, Jack Jock-Wai
Zhuo, Yuanyuan
Liu, Shukai
Thomas, Rebecca L.
Owens, David R.
Zee, Benny Chung-Ying
author_sort Qu, Yimin
collection PubMed
description Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. Methods: We have conducted a case–control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.
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spelling pubmed-91438342022-05-29 Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders Qu, Yimin Lee, Jack Jock-Wai Zhuo, Yuanyuan Liu, Shukai Thomas, Rebecca L. Owens, David R. Zee, Benny Chung-Ying J Clin Med Article Background: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. Methods: We have conducted a case–control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. Results: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. Conclusions: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders. MDPI 2022-05-10 /pmc/articles/PMC9143834/ /pubmed/35628812 http://dx.doi.org/10.3390/jcm11102687 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Qu, Yimin
Lee, Jack Jock-Wai
Zhuo, Yuanyuan
Liu, Shukai
Thomas, Rebecca L.
Owens, David R.
Zee, Benny Chung-Ying
Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
title Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
title_full Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
title_fullStr Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
title_full_unstemmed Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
title_short Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
title_sort risk assessment of chd using retinal images with machine learning approaches for people with cardiometabolic disorders
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143834/
https://www.ncbi.nlm.nih.gov/pubmed/35628812
http://dx.doi.org/10.3390/jcm11102687
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