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

Yimin Qu Orcid Logo, Jack Jock-Wai Lee, Yuanyuan Zhuo Orcid Logo, Shukai Liu, Becky Thomas, David Owens Orcid Logo, Benny Chung-Ying Zee Orcid Logo

Journal of Clinical Medicine, Volume: 11, Issue: 10, Start page: 2687

Swansea University Authors: Becky Thomas, David Owens Orcid Logo

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DOI (Published version): 10.3390/jcm11102687

Abstract

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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Published in: Journal of Clinical Medicine
ISSN: 2077-0383
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60527
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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&#x2013;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. 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spelling 2022-08-01T17:07:19.7333682 v2 60527 2022-07-19 Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders e83b45ec71428bd748ce201048f43d6a Becky Thomas Becky Thomas true false 2fd4b7c3f82c6d3bd546eff61ff944e9 0000-0003-1002-1238 David Owens David Owens true false 2022-07-19 HDAT 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. Journal Article Journal of Clinical Medicine 11 10 2687 MDPI AG 2077-0383 coronary heart disease; retinal images; machine learning; cardiometabolic disorders 10 5 2022 2022-05-10 10.3390/jcm11102687 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University Another institution paid the OA fee This study was supported by the General Research Fund (GRF) of the Research Grant Council Hong Kong (RGC Ref No. 14139116) and the Shenzhen Science and Technology Innovation Commission (No: KCXFZ20201221173208024) 2022-08-01T17:07:19.7333682 2022-07-19T09:25:29.0743931 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Yimin Qu 0000-0002-1940-2638 1 Jack Jock-Wai Lee 2 Yuanyuan Zhuo 0000-0002-9416-4203 3 Shukai Liu 4 Becky Thomas 5 David Owens 0000-0003-1002-1238 6 Benny Chung-Ying Zee 0000-0002-7238-845x 7 60527__24787__37cb0235385540fc9a0e6a58b86b0853.pdf 60527.pdf 2022-08-01T12:53:35.3702566 Output 1206436 application/pdf Version of Record true © 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
spellingShingle Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
Becky Thomas
David Owens
title_short 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_sort Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders
author_id_str_mv e83b45ec71428bd748ce201048f43d6a
2fd4b7c3f82c6d3bd546eff61ff944e9
author_id_fullname_str_mv e83b45ec71428bd748ce201048f43d6a_***_Becky Thomas
2fd4b7c3f82c6d3bd546eff61ff944e9_***_David Owens
author Becky Thomas
David Owens
author2 Yimin Qu
Jack Jock-Wai Lee
Yuanyuan Zhuo
Shukai Liu
Becky Thomas
David Owens
Benny Chung-Ying Zee
format Journal article
container_title Journal of Clinical Medicine
container_volume 11
container_issue 10
container_start_page 2687
publishDate 2022
institution Swansea University
issn 2077-0383
doi_str_mv 10.3390/jcm11102687
publisher MDPI AG
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
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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.
published_date 2022-05-10T04:18:42Z
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