Machine Learning-Based Prediction of Keratoconus Progression After Accelerated Corneal Cross-Linking Using the Thinnest Corneal Thickness-to-Maximum Keratometry Ratio.
- DOI
- 10.1016/j.pdpdt.2026.105663
- Published
- 2026 Sep 24
- Container
- Photodiagnosis and photodynamic therapy
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.pdpdt.2026.105663,
title = {Machine Learning-Based Prediction of Keratoconus Progression After Accelerated Corneal Cross-Linking Using the Thinnest Corneal Thickness-to-Maximum Keratometry Ratio.},
author = {Wu T and Liu Y and Cao X and Gong Y and Jiang Y and Huang J and Li Z and Ma J and Wang J},
year = {2026},
journal = {Photodiagnosis and photodynamic therapy},
doi = {10.1016/j.pdpdt.2026.105663},
url = {https://doi.org/10.1016/j.pdpdt.2026.105663}
}RIS
TY - JOUR TI - Machine Learning-Based Prediction of Keratoconus Progression After Accelerated Corneal Cross-Linking Using the Thinnest Corneal Thickness-to-Maximum Keratometry Ratio. AU - Wu T AU - Liu Y AU - Cao X AU - Gong Y AU - Jiang Y AU - Huang J AU - Li Z AU - Ma J AU - Wang J PY - 2026 JO - Photodiagnosis and photodynamic therapy DO - 10.1016/j.pdpdt.2026.105663 UR - https://doi.org/10.1016/j.pdpdt.2026.105663 ER -
APA
T, W., Y, L., X, C., Y, G., Y, J., J, H., Z, L., J, M., & J, W. (2026). Machine Learning-Based Prediction of Keratoconus Progression After Accelerated Corneal Cross-Linking Using the Thinnest Corneal Thickness-to-Maximum Keratometry Ratio.. Photodiagnosis and photodynamic therapy. https://doi.org/10.1016/j.pdpdt.2026.105663
Source records
- pubmed · retrieved 2026-09-25T14:22:06.015Z