Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/151889 |
Resumo: | Funding Information: This work is supported by National Medical Research Council Individual Research Grant (NMRC/0975/2005), National Medical Research Council Center Grant (NMRC/CG/C010A/2017_SERI) and Nurturing Clinician Researcher Scheme Program Grant Award (05/FY2021/P2/11-A92). Publisher Copyright: © 2023, The Author(s). |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Deep learning system to predict the 5-year risk of high myopia using fundus imaging in childrenMedicine (miscellaneous)Health InformaticsComputer Science ApplicationsHealth Information ManagementFunding Information: This work is supported by National Medical Research Council Individual Research Grant (NMRC/0975/2005), National Medical Research Council Center Grant (NMRC/CG/C010A/2017_SERI) and Nurturing Clinician Researcher Scheme Program Grant Award (05/FY2021/P2/11-A92). Publisher Copyright: © 2023, The Author(s).Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6–12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms – image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ −6.00 diopter) during teenage years (5 years later, age 11–17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93–0.95; Test dataset 0.91–0.93), clinical models (Primary dataset AUC 0.90–0.97; Test dataset 0.93–0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97–0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify “at-risk” children for early intervention.Comprehensive Health Research Centre (CHRC) - Pólo ENSPCentro de Investigação em Saúde Pública (CISP/PHRC)Escola Nacional de Saúde Pública (ENSP)RUNFoo, Li LianLim, Gilbert Yong SanLanca, CarlaWong, Chee WaiHoang, Quan V.Zhang, Xiu JuanYam, Jason C.Schmetterer, LeopoldChia, AudreyWong, Tien YinTing, Daniel S.W.Saw, Seang MeiAng, Marcus2023-04-17T22:19:55Z2023-122023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/151889engPURE: 58501758https://doi.org/10.1038/s41746-023-00752-8info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:34:13Zoai:run.unl.pt:10362/151889Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:43.047096Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
title |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
spellingShingle |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children Foo, Li Lian Medicine (miscellaneous) Health Informatics Computer Science Applications Health Information Management |
title_short |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
title_full |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
title_fullStr |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
title_full_unstemmed |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
title_sort |
Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children |
author |
Foo, Li Lian |
author_facet |
Foo, Li Lian Lim, Gilbert Yong San Lanca, Carla Wong, Chee Wai Hoang, Quan V. Zhang, Xiu Juan Yam, Jason C. Schmetterer, Leopold Chia, Audrey Wong, Tien Yin Ting, Daniel S.W. Saw, Seang Mei Ang, Marcus |
author_role |
author |
author2 |
Lim, Gilbert Yong San Lanca, Carla Wong, Chee Wai Hoang, Quan V. Zhang, Xiu Juan Yam, Jason C. Schmetterer, Leopold Chia, Audrey Wong, Tien Yin Ting, Daniel S.W. Saw, Seang Mei Ang, Marcus |
author2_role |
author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Comprehensive Health Research Centre (CHRC) - Pólo ENSP Centro de Investigação em Saúde Pública (CISP/PHRC) Escola Nacional de Saúde Pública (ENSP) RUN |
dc.contributor.author.fl_str_mv |
Foo, Li Lian Lim, Gilbert Yong San Lanca, Carla Wong, Chee Wai Hoang, Quan V. Zhang, Xiu Juan Yam, Jason C. Schmetterer, Leopold Chia, Audrey Wong, Tien Yin Ting, Daniel S.W. Saw, Seang Mei Ang, Marcus |
dc.subject.por.fl_str_mv |
Medicine (miscellaneous) Health Informatics Computer Science Applications Health Information Management |
topic |
Medicine (miscellaneous) Health Informatics Computer Science Applications Health Information Management |
description |
Funding Information: This work is supported by National Medical Research Council Individual Research Grant (NMRC/0975/2005), National Medical Research Council Center Grant (NMRC/CG/C010A/2017_SERI) and Nurturing Clinician Researcher Scheme Program Grant Award (05/FY2021/P2/11-A92). Publisher Copyright: © 2023, The Author(s). |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-17T22:19:55Z 2023-12 2023-12-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/151889 |
url |
http://hdl.handle.net/10362/151889 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PURE: 58501758 https://doi.org/10.1038/s41746-023-00752-8 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138135909597184 |