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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u201312 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 \u2013 image, clinical and mix (image\u2009+\u2009clinical) models to predict high myopia development (SE\u2009\u2264\u2009\u22126.00\u2009diopter) during teenage years (5 years later, age 11\u201317). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93\u20130.95; Test dataset 0.91\u20130.93), clinical models (Primary dataset AUC 0.90\u20130.97; Test dataset 0.93\u20130.94) and mixed (image\u2009+\u2009clinical) models (Primary dataset AUC 0.97; Test dataset 0.97\u20130.98) 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 \u201cat-risk\u201d children for early intervention.<\/jats:p>","DOI":"10.1038\/s41746-023-00752-8","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T10:02:55Z","timestamp":1674727375000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7785-9556","authenticated-orcid":false,"given":"Li Lian","family":"Foo","sequence":"first","affiliation":[]},{"given":"Gilbert Yong San","family":"Lim","sequence":"additional","affiliation":[]},{"given":"Carla","family":"Lanca","sequence":"additional","affiliation":[]},{"given":"Chee Wai","family":"Wong","sequence":"additional","affiliation":[]},{"given":"Quan V.","family":"Hoang","sequence":"additional","affiliation":[]},{"given":"Xiu Juan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jason C.","family":"Yam","sequence":"additional","affiliation":[]},{"given":"Leopold","family":"Schmetterer","sequence":"additional","affiliation":[]},{"given":"Audrey","family":"Chia","sequence":"additional","affiliation":[]},{"given":"Tien Yin","family":"Wong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2264-7174","authenticated-orcid":false,"given":"Daniel S. 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