Rapid and accurate prediction of cycloplegic refraction in Chinese children: development and validation of machine learning models
The ML models enable cSE prediction based on non-cycloplegic refraction data and ocular biometric parameters, providing a fast, practical method for estimating refractive error. Multicenter validation and targeted oversampling of rare refractive subgroups are required, however, before robust clinical implementation of the models.
Authors
- Xingxue Zhu (ORCID: https://orcid.org/0000-0001-9784-6341)
- Xiaomei Qu (ORCID: https://orcid.org/0000-0002-2393-5840)
- Yujia Liu (ORCID: https://orcid.org/0000-0002-1561-2582)
- Yuliang Wang (ORCID: https://orcid.org/0000-0002-7354-7854)
- Chongyang Wang (ORCID: https://orcid.org/0000-0002-8495-1262)
- Jianmin Shang (ORCID: https://orcid.org/0000-0002-6125-769X)
- Chaoying Ye
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Pixelworks (China) (CN)
- Eye & ENT Hospital of Fudan University (CN)
- Shanghai Clinical Research Center (CN)
Publication Details
- Journal
- Journal of Global Health
- Published
- 2025-10-17
- DOI
- https://doi.org/10.7189/jogh.15.04281
- Primary Topic
- Ophthalmology and Visual Impairment Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00