{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:22:03Z","timestamp":1788456123550,"version":"build-2803163510"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to investigate longitudinal changes in ocular parameters and develop a machine learning-based model for predicting myopia onset and shift within 1\u202fyear in school-aged premyopic children.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective cohort study enrolled 320 premyopic children aged 6\u201312\u202fyears from the Ophthalmology Clinic of The Second Affiliated Hospital of Dalian Medical University. Uncorrected visual acuity (logMAR), cycloplegic spherical equivalent (SE), axial length (AL), average corneal curvature (CC), and subfoveal choroidal thickness (SFCT) were measured at baseline and 6-month intervals for 12\u202fmonths. Premyopia was defined as - 0.50 D\u202f&amp;lt;\u202fSE\u202f\u2264\u202f+ 0.75 D. A multivariable analysis evaluated predictive factors including age, gender, parental myopia, baseline SE, AL, CC, axial length\/corneal radius (AL\/CR), and SFCT. Machine learning algorithms were used to predict 1-year myopia onset and myopia shift, along with Shapley Additive exPlanations (SHAP) interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 284 participants (88.8% retention rate), 141 children (49.3%) developed myopia. The cohort exhibited an annual SE progression of \u22120.695\u202f\u00b1\u202f0.222 D and AL elongation of 0.356\u202f\u00b1\u202f0.122\u202fmm. The AL\/CR increased from 2.986\u202f\u00b1\u202f0.061 to 3.029\u202f\u00b1\u202f0.072 (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001), while SFCT demonstrated a significant reduction of 21.535\u202f\u00b1\u202f9.731\u202f\u03bcm (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). The optimal model achieved an AUC-ROC of 0.963 (95% CI: 0.930\u20130.997) for myopia onset prediction, with baseline SE emerging as the most significant predictor, followed by parental myopia, SFCT, and age. Meanwhile, our algorithm also achieved clinically acceptable 1-year predictions of SE.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Premyopic children exhibited accelerated myopic progression. Our machine learning-based predictive models showed promising performance for myopia onset and myopia shift, providing clinically valuable risk stratification for targeted prevention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1646277","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T06:28:14Z","timestamp":1763360894000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of myopia onset and shift in premyopic school-aged children: a machine learning-based algorithm"],"prefix":"10.3389","volume":"12","author":[{"given":"Mingjun","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhua","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanhua","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3760\/cma.j.cn112150-20221008-00964","article-title":"Trend of the detection rate of myopia among Chinese Han children and adolescents aged 7-18 years from 2010 to 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