{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T23:49:45Z","timestamp":1787701785292,"version":"build-2784847793"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Province Key Research Grant","award":["2021C03032"],"award-info":[{"award-number":["2021C03032"]}]},{"name":"Zhejiang Province Key Research Grant","award":["NEI K23EY034893"],"award-info":[{"award-number":["NEI K23EY034893"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Vision"],"abstract":"<jats:p>We previously developed machine learning (ML) models for predicting cycloplegic spherical equivalent refraction (SER) and myopia using non-cycloplegic data and following a standardized protocol (cycloplegia with 0.5% tropicamide and biometry using NIDEK A-scan), but the models\u2019 performance may not be generalizable to other settings. This study evaluated the performance of ML models in an independent cohort using a different cycloplegic agent and biometer. Chinese students (N = 614) aged 8\u201313 years underwent autorefraction before and after cycloplegia with 0.5% tropicamide (n = 505) or 1% cyclopentolate (n = 109). Biometric measures were obtained using an IOLMaster 700 (n = 207) or Optical Biometer SW-9000 (n = 407). ML models were evaluated using R2, mean absolute error (MAE), sensitivity, specificity, and area under the ROC curve (AUC). The XGBoost model predicted cycloplegic SER very well (R2 = 0.95, MAE (SD) = 0.32 (0.30) D). Both ML models predicted myopia well (random forest: AUC 0.99, sensitivity 93.7%, specificity 96.4%; XGBoost: sensitivity 90.1%, specificity 96.8%) and accurately predicted the myopia rate (observed 62.9%; random forest: 60.6%; XGBoost: 58.8%) despite heterogeneous cycloplegia and biometry factors. In this independent cohort of students, XGBoost and random forest performed very well for predicting cycloplegic SER and myopia status using non-cycloplegic data. This external validation study demonstrated that ML may provide a useful tool for estimating cycloplegic SER and myopia prevalence with heterogeneous clinical parameters, and study in additional populations is warranted.<\/jats:p>","DOI":"10.3390\/vision9040084","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:19:26Z","timestamp":1760015966000},"page":"84","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Myopia Prediction Using Machine Learning: An External Validation Study"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5402-8452","authenticated-orcid":false,"given":"Rajat S.","family":"Chandra","sequence":"first","affiliation":[{"name":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bole","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongguang","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5752-1355","authenticated-orcid":false,"given":"Guishuang","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3945-2077","authenticated-orcid":false,"given":"Julius T.","family":"Oatts","sequence":"additional","affiliation":[{"name":"Division of Ophthalmology, Children\u2019s Hospital of Philadelphia, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1167\/iovs.62.5.2","article-title":"IMI Impact of Myopia","volume":"62","author":"Sankaridurg","year":"2021","journal-title":"Investig. 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