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

2023-01Journal article Open access
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dc.contributor.authorFoo, Li Lian
dc.contributor.authorLim, Gilbert Yong San
dc.contributor.authorLança, Carla
dc.contributor.authorWong, Chee Wai
dc.contributor.authorHoang, Quan V.
dc.contributor.authorZhang, Xiu Juan
dc.contributor.authorYam, Jason C.
dc.contributor.authorSchmetterer, Leopold
dc.contributor.authorChia, Audrey
dc.contributor.authorWong, Tien Yin
dc.contributor.authorTing, Daniel S. W.
dc.contributor.authorSaw, Seang-Mei
dc.contributor.authorAng, Marcus
dc.date.accessioned2023-02-03T16:35:18Z
dc.date.available2023-02-03T16:35:18Z
dc.date.issued2023-01
dc.description.abstractOur 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 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 the 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 the primary dataset, 0.97 versus 0.94 in the test dataset; mixed model AUC 0.99 versus 0.97 in the 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFoo LL, Lim GY, Lança C, Wong CW, Hoang QV, Zhang XJ, et al. Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children. NPJ Digit Med. 2023;6(1):10.pt_PT
dc.identifier.doi10.1038/s41746-023-00752-8pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/15482
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relation.publisherversionhttps://www.nature.com/articles/s41746-023-00752-8pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectOrthopticspt_PT
dc.subjectMyopiapt_PT
dc.subjectChildrenpt_PT
dc.subjectFundus imagingpt_PT
dc.titleDeep learning system to predict the 5-year risk of high myopia using fundus imaging in childrenpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.startPage10pt_PT
oaire.citation.titleNPJ Digital Medicinept_PT
oaire.citation.volume6pt_PT
person.familyNameLança
person.givenNameCarla
person.identifier.ciencia-id601A-6412-BF2F
person.identifier.orcid0000-0001-9918-787X
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication0320b455-ee19-4670-8bf2-10dce9de1bec
relation.isAuthorOfPublication.latestForDiscovery0320b455-ee19-4670-8bf2-10dce9de1bec

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