State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
Eye (Lond). 2022 May;36(5):921-929. doi: 10.1038/s41433-021-01805-6. Epub 2021 Oct 13.
Myopia is a leading cause of visual impairment and has raised significant international concern in recent decades with rapidly increasing prevalence and incidence worldwide. Accurate prediction of future myopia risk could help identify high-risk children for early targeted intervention to delay myopia onset or slow myopia progression. Researchers have built and assessed various myopia prediction models based on different datasets, including baseline refraction or biometric data, lifestyle data, genetic data, and data integration. Here, we summarize all related work published in the past 30 years and provide a comprehensive review of myopia prediction methods, datasets, and performance, which could serve as a useful reference and valuable guideline for future research.
近视是视力损害的主要原因，近年来其患病率和发病率在全球范围内迅速上升，引起了国际社会的高度关注。准确预测未来近视风险有助于识别高风险儿童，以便进行早期有针对性的干预，延缓近视的发生或减缓近视的进展。研究人员已经基于不同的数据集，如基线屈光度或生物测量数据、生活方式数据、遗传数据和数据集成，构建和评估了各种近视预测模型。在这里，我们总结了过去 30 年发表的所有相关工作，并对近视预测方法、数据集和性能进行了全面回顾，可为未来的研究提供有用的参考和有价值的指导。
