Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Bibliographic Details
Main Author: Foo, Li Lian
Publication Date: 2023
Other Authors: 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
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/151889
Summary: 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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spelling 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:10:56Zoai:run.unl.pt:10362/151889Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:41:15.737181Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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