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Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis

Yimin Qu, Yuanyuan Zhuo, Jack Lee, Xingxian Huang, Zhuoxin Yang, Haibo Yu, Jinwen Zhang, Weiqu Yuan, Jiaman Wu, David Owens Orcid Logo, Benny Zee

Frontiers in Neurology, Volume: 13

Swansea University Author: David Owens Orcid Logo

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DOI (Published version): 10.3389/fneur.2022.916966

Abstract

Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as ea...

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Published in: Frontiers in Neurology
ISSN: 1664-2295
Published: Frontiers Media SA 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60879
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-09-09T16:24:25.2851314</datestamp><bib-version>v2</bib-version><id>60879</id><entry>2022-08-24</entry><title>Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis</title><swanseaauthors><author><sid>2fd4b7c3f82c6d3bd546eff61ff944e9</sid><ORCID>0000-0003-1002-1238</ORCID><firstname>David</firstname><surname>Owens</surname><name>David Owens</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-08-24</date><deptcode>BMS</deptcode><abstract>Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study design: A case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results: 711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).Conclusion: A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.</abstract><type>Journal Article</type><journal>Frontiers in Neurology</journal><volume>13</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Frontiers Media SA</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1664-2295</issnElectronic><keywords/><publishedDay>22</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-08-22</publishedDate><doi>10.3389/fneur.2022.916966</doi><url/><notes/><college>COLLEGE NANME</college><department>Biomedical Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BMS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This study was supported by the General Research Fund (GRF) of the Hong Kong Research Grant Council (No.14139116); National Natural Science Foundation of China (No.81803952); Science Technology and Innovation Commission of Shenzhen Municipality (KCXFZ20201221173208024).</funders><projectreference/><lastEdited>2022-09-09T16:24:25.2851314</lastEdited><Created>2022-08-24T09:04:43.8787250</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Yimin</firstname><surname>Qu</surname><order>1</order></author><author><firstname>Yuanyuan</firstname><surname>Zhuo</surname><order>2</order></author><author><firstname>Jack</firstname><surname>Lee</surname><order>3</order></author><author><firstname>Xingxian</firstname><surname>Huang</surname><order>4</order></author><author><firstname>Zhuoxin</firstname><surname>Yang</surname><order>5</order></author><author><firstname>Haibo</firstname><surname>Yu</surname><order>6</order></author><author><firstname>Jinwen</firstname><surname>Zhang</surname><order>7</order></author><author><firstname>Weiqu</firstname><surname>Yuan</surname><order>8</order></author><author><firstname>Jiaman</firstname><surname>Wu</surname><order>9</order></author><author><firstname>David</firstname><surname>Owens</surname><orcid>0000-0003-1002-1238</orcid><order>10</order></author><author><firstname>Benny</firstname><surname>Zee</surname><order>11</order></author></authors><documents><document><filename>60879__25108__e4be4e5f68974a788685a87d6cd86074.pdf</filename><originalFilename>60879_VoR.pdf</originalFilename><uploaded>2022-09-09T16:22:01.0816648</uploaded><type>Output</type><contentLength>1280099</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2022 Qu, Zhuo, Lee, Huang, Yang, Yu, Zhang, Yuan, Wu, Owens and Zee. 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spelling 2022-09-09T16:24:25.2851314 v2 60879 2022-08-24 Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis 2fd4b7c3f82c6d3bd546eff61ff944e9 0000-0003-1002-1238 David Owens David Owens true false 2022-08-24 BMS Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study design: A case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results: 711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).Conclusion: A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone. Journal Article Frontiers in Neurology 13 Frontiers Media SA 1664-2295 22 8 2022 2022-08-22 10.3389/fneur.2022.916966 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University This study was supported by the General Research Fund (GRF) of the Hong Kong Research Grant Council (No.14139116); National Natural Science Foundation of China (No.81803952); Science Technology and Innovation Commission of Shenzhen Municipality (KCXFZ20201221173208024). 2022-09-09T16:24:25.2851314 2022-08-24T09:04:43.8787250 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Yimin Qu 1 Yuanyuan Zhuo 2 Jack Lee 3 Xingxian Huang 4 Zhuoxin Yang 5 Haibo Yu 6 Jinwen Zhang 7 Weiqu Yuan 8 Jiaman Wu 9 David Owens 0000-0003-1002-1238 10 Benny Zee 11 60879__25108__e4be4e5f68974a788685a87d6cd86074.pdf 60879_VoR.pdf 2022-09-09T16:22:01.0816648 Output 1280099 application/pdf Version of Record true © 2022 Qu, Zhuo, Lee, Huang, Yang, Yu, Zhang, Yuan, Wu, Owens and Zee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) true eng http://creativecommons.org/licenses/by/4.0/
title Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
spellingShingle Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
David Owens
title_short Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
title_full Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
title_fullStr Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
title_full_unstemmed Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
title_sort Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
author_id_str_mv 2fd4b7c3f82c6d3bd546eff61ff944e9
author_id_fullname_str_mv 2fd4b7c3f82c6d3bd546eff61ff944e9_***_David Owens
author David Owens
author2 Yimin Qu
Yuanyuan Zhuo
Jack Lee
Xingxian Huang
Zhuoxin Yang
Haibo Yu
Jinwen Zhang
Weiqu Yuan
Jiaman Wu
David Owens
Benny Zee
format Journal article
container_title Frontiers in Neurology
container_volume 13
publishDate 2022
institution Swansea University
issn 1664-2295
doi_str_mv 10.3389/fneur.2022.916966
publisher Frontiers Media SA
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
active_str 0
description Background: Stroke is the second leading cause of death worldwide, causing a considerable disease burden. Ischemic stroke is more frequent, but haemorrhagic stroke is responsible for more deaths. The clinical management and treatment are different, and it is advantageous to classify their risk as early as possible for disease prevention. Furthermore, retinal characteristics have been associated with stroke and can be used for stroke risk estimation. This study investigated machine learning approaches to retinal images for risk estimation and classification of ischemic and haemorrhagic stroke.Study design: A case-control study was conducted in the Shenzhen Traditional Chinese Medicine Hospital. According to the computerized tomography scan (CT) or magnetic resonance imaging (MRI) results, stroke patients were classified as either ischemic or hemorrhage stroke. In addition, a control group was formed using non-stroke patients from the hospital and healthy individuals from the community. Baseline demographic and medical information was collected from participants' hospital medical records. Retinal images of both eyes of each participant were taken within 2 weeks of admission. Classification models using a machine-learning approach were developed. A 10-fold cross-validation method was used to validate the results.Results: 711 patients were included, with 145 ischemic stroke patients, 86 haemorrhagic stroke patients, and 480 controls. Based on 10-fold cross-validation, the ischemic stroke risk estimation has a sensitivity and a specificity of 91.0% and 94.8%, respectively. The area under the ROC curve for ischemic stroke is 0.929 (95% CI 0.900 to 0.958). The haemorrhagic stroke risk estimation has a sensitivity and a specificity of 93.0% and 97.1%, respectively. The area under the ROC curve is 0.951 (95% CI 0.918 to 0.983).Conclusion: A fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
published_date 2022-08-22T04:19:22Z
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score 11.013148

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