Axial and choroidal changes as key predictors of myopia control in red-light therapy: Evidence from machine learning models
We developed and validated an interpretable machine learning model that accurately predicts short- and long-term outcomes of RLRL therapy in children. ChangeAL and ChangeChT serve as key dynamic biomarkers for treatment monitoring. These findings provide a foundation for personalized clinical decision-making in myopia management.
Authors
- Xiaoling Zhang (ORCID: https://orcid.org/0000-0001-8237-1857)
- Jie Hong (ORCID: https://orcid.org/0009-0000-3855-535X)
- Zixun Wang (ORCID: https://orcid.org/0000-0001-8207-9442)
- Ruihua Wei
- D. X. Wang
- Xiaoxue Hu
- Boxuan Sun
- Rui Li
- Hui Zong
Institutions
- The Central Hospital of Xiao gan (CN)
- Han Dan City Eye Hospital - The Third Hospital of Handan (CN)
- Wuhan Children's Hospital (CN)
- Tianjin Medical University Eye Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Photodiagnosis and Photodynamic Therapy
- Published
- 2026-01-08
- DOI
- https://doi.org/10.1016/j.pdpdt.2026.105342
- Citations
- 2
- Primary Topic
- Ophthalmology and Visual Impairment Studies
- Type
- article
- Field-Weighted Citation Impact
- 63.04