{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T14:41:45Z","timestamp":1788273705707,"version":"build-2803163510"},"reference-count":15,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec005\">\n<jats:title>Purpose<\/jats:title>\n<jats:p>To develop an ensemble machine learning prediction model for clinical refraction in childhood using partial interferometry measurements.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec006\">\n<jats:title>Methods<\/jats:title>\n<jats:p>Age, sex, cycloplegic refraction, and partial interferometry data collected within one month were obtained from patients aged 5\u201316 years, retrospectively. Four ensemble regression models were used to develop prediction models of spherical equivalents (SE) from the collected data. Root mean squared error (RMSE) was used to compare the accuracy among the models. The accuracy of the ensemble models was compared with that of a previously developed multiple linear regression model.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec007\">\n<jats:title>Results<\/jats:title>\n<jats:p>4156 eyes from 1965 patients (50.3% female) were included. Mean age was 8.4\u2009\u00b1\u20092.3 years and mean SE was \u22121.01\u2009\u00b1\u20092.94 diopters. Mean axial length was 23.63\u2009\u00b1\u20091.41\u2009mm and mean keratometry reading of flat and steep axis was 43.58\u2009\u00b1\u20091.40 diopters. Developed ensemble models had accuracy of RMSE 0.800 to 0.829 diopters, which was superior to that of the conventional regression model (1.213 diopters). Simulations with the same biometric parameters showed that female sex was associated more with myopia than that of male sex. Long eyes showed dampened increase in the myopic refraction per unit axial length.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec008\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Refractive errors can be calculated in the childhood using these ensemble models with ocular biometric parameters. Moreover, the models were able to simulate hypothetical relationships between ocular parameters and SE to understand the nature of clinical refraction.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0328213","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T17:48:40Z","timestamp":1752169720000},"page":"e0328213","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Ensemble machine learning prediction model for clinical refraction using partial interferometry measurements in childhood"],"prefix":"10.1371","volume":"20","author":[{"given":"Sa Ra","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Hyun","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gon Soo","family":"Choe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-551X","authenticated-orcid":true,"given":"Dae Hee","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"issue":"9","key":"pone.0328213.ref001","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/j.ophtha.2020.03.004","article-title":"Accuracy of autorefraction in children: a report by the American Academy of Ophthalmology","volume":"127","author":"LB Wilson","year":"2020","journal-title":"Ophthalmology"},{"issue":"1","key":"pone.0328213.ref002","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1111\/opo.12341","article-title":"Intra- and inter- examiner repeatability of cycloplegic retinoscopy among young children","volume":"37","author":"SJ McCullough","year":"2017","journal-title":"Ophthalmic Physiol Opt"},{"issue":"9","key":"pone.0328213.ref003","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1038\/sj.eye.6702069","article-title":"Reliability and validity of the partial coherence interferometry for measurement of ocular axial length in children","volume":"20","author":"HM Hussin","year":"2006","journal-title":"Eye (Lond)"},{"issue":"12","key":"pone.0328213.ref004","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1016\/j.ophtha.2017.08.046","article-title":"Machine learning has arrived!","volume":"124","author":"A Lee","year":"2017","journal-title":"Ophthalmology"},{"key":"pone.0328213.ref005","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1097\/OPX.0b013e3181b2f903","article-title":"Children\u2019s ocular components and age, gender, and ethnicity","volume":"86","author":"JD Twelker","year":"2009","journal-title":"Optom Vis Sci"},{"key":"pone.0328213.ref006","doi-asserted-by":"crossref","DOI":"10.1016\/j.optom.2022.03.001","article-title":"Prediction of manifest refraction using machine learning ensemble models on wavefront aberrometry data","volume":"15","author":"CS Hern\u00e1ndez","year":"2022","journal-title":"J Optom"},{"issue":"1","key":"pone.0328213.ref007","doi-asserted-by":"crossref","first-page":"8565","DOI":"10.1038\/s41598-020-65417-y","article-title":"Using artificial intelligence and novel polynomials to predict subjective refraction","volume":"10","author":"R Rampat","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0328213.ref008","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"L Breimann","year":"2001","journal-title":"Mach Learn"},{"issue":"5","key":"pone.0328213.ref009","doi-asserted-by":"crossref","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: a gradient boosting machine","volume":"29","author":"JH Friedman","year":"2001","journal-title":"Ann Statist"},{"key":"pone.0328213.ref010","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD\u201916","author":"T Chen","year":"2016"},{"key":"pone.0328213.ref011","first-page":"3147","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume":"2017","author":"G Ke","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"pone.0328213.ref012","first-page":"1","article-title":"Refractive lens power and lens thickness in children (6\u201316 years old","volume":"11","author":"T Lu","year":"2021","journal-title":"). 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