ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2025
Machine learning-based progress prediction in accelerated cross-linking for Keratoconus.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Ultraviolet Sensing-Guided Biomedical Systems: From Label-Free Imaging to Dosimetry and Therapy Feedback.Biosensors · 2026Review
- Machine Learning for Predicting Recurrent Course in Uveitis Using Baseline Clinical Characteristics.Investigative ophthalmology & visual science · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo analyze corneal topographic and biomechanical parameters in keratoconus patients before undergoing accelerated corneal collagen cross-linking (A-CXL) surgery and use machine learning models to identify prognostic factors for disease progression after treatment.
methodsThis was a retrospective, single-center study on 95 eyes from 69 keratoconus patients (mean age 21.46 ± 7.07 years) undergoing A-CXL, with 3-22 months follow-up. Corneal tomography (Pentacam) and biomechanical measurements (Corvis ST) were performed at baseline and follow-up visits. Changes in the E-stage were used to define progression. LASSO, XGBoost, and random forest machine learning models were applied to identify prognostic factors. A nomogram was developed to predict progression probabilities.
results42.1% of eyes showed progression based on E-stage change. Maximal keratometry (Kmax) and index of surface variance (ISV) were significantly higher in the progression group. The nomogram incorporating Kmax and ISV predicted progression better than individual parameters. The progression rate was 51.4% in high-risk eyes versus 16% in low-risk eyes stratified by the nomogram.
conclusionsKmax and ISV are important prognostic factors for keratoconus progression after A-CXL. The nomogram can improve prediction accuracy compared to single parameters. It enables personalized risk assessment to guide treatment decisions.
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Registered trials
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