Machine learning-driven prediction of cycloplegic refractive error in Chinese children
ML models provide accurate estimates of cycloplegic SER using non-cycloplegic parameters, offering a practical alternative for pediatric refractive assessments when cycloplegia is infeasible.
Authors
- Feng Tian (ORCID: https://orcid.org/0000-0002-9686-2769)
- Yang Qing (ORCID: https://orcid.org/0000-0003-2844-5098)
- Chuanyan Wu (ORCID: https://orcid.org/0000-0003-2705-866X)
- Bichi Chen
- Ying Ruan
- Ying Li (ORCID: https://orcid.org/0000-0002-6278-2357)
- Li Tian (ORCID: https://orcid.org/0000-0002-7661-5381)
- Ruzhi Deng (ORCID: https://orcid.org/0000-0002-4587-7223)
- Min Cao (ORCID: https://orcid.org/0009-0008-1354-2921)
- Maiping Yang (ORCID: https://orcid.org/0000-0002-2262-9251)
- Suzhong Xu
Institutions
- Wenzhou Medical University (CN)
- Affiliated Eye Hospital of Wenzhou Medical College (CN)
- Shanghai Eye Disease Prevention & Treatment Center (CN)
- Vision Medicals (China) (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-05-22
- DOI
- https://doi.org/10.3389/fcell.2025.1608494
- Citations
- 1
- Primary Topic
- Ophthalmology and Visual Impairment Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.35