biostudies-literatureXu SScience and Technology Program of Guangzhou, ChinaMajor Science and Technology Project of Zhongshan CityGuangzhou Municipal Science and Technology ProjectNational Natural Science Foundation of China876https://www.ebi.ac.uk/biostudies/studies/S-EPMC12330041biostudies-literatureUnknown23(1)<h4>Background</h4>Previous Artificial Intelligence (AI) models are mainly based on hospital data to predict myopia progression in myopic children. However, school-level AI models for predicting myopia onset in non-myopic children are lacking. There is a need for more precise and comprehensive tools for full myopia management, from the onset of myopia to its progression.<h4>Methods</h4>This study was conducted in 870 centers dispersed across seven cities in China from September 2019 to December 2021, with participants observed for two years. Machine learning models were trained and internally validated on datasets from Shenzhen, and then externally tested from the other six cities. Of 1,123,602 children and adolescents aged 4-18 years old, 1,105,271 individuals were confirmed eligible. AftJournal of translational medicineSchool-level prediction and management of myopia in children and adolescents.PMC12330041823710892022A1007821710572024A03J0336202206080005Zhang GRuan ZDeng SHu YZhuo XZhu YZhuo YLu YLi LZhou ZYang XLi ZHuang XLeng YXu SQu YWang ZFu MHou FfalseSchool-level prediction and management of myopia in children and adolescents.<h4>Background</h4>Previous Artificial Intelligence (AI) models are mainly based on hospital data to predict myopia progression in myopic children. However, school-level AI models for predicting myopia onset in non-myopic children are lacking. There is a need for more precise and comprehensive tools for full myopia management, from the onset of myopia to its progression.<h4>Methods</h4>This study was conducted in 870 centers dispersed across seven cities in China from September 2019 to December 2021, with participants observed for two years. Machine learning models were trained and internally validated on datasets from Shenzhen, and then externally tested from the other six cities. Of 1,123,602 children and adolescents aged 4-18 years old, 1,105,271 individuals were confirmed eligible. Aft2025-01-01T00:00:00Z2025 Aug2026-04-13T08:22:51.493Z2026-04-07T13:29:03.953ZS-EPMC123300414077075610.1186/s12967-025-06855-y