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Background <p>To 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.</p> Methods <p>This was a retrospective, single-center study on 95 eyes from 69 keratoconus patients (mean age 21.46 ± 7.07&#xa0;years) undergoing A-CXL, with 3–22&#xa0;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.</p> Results <p>42.1% 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.</p> Conclusions <p>Kmax 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.</p>

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Machine learning-based progress prediction in accelerated cross-linking for Keratoconus

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Qi Wan,
  • Qiong Wang,
  • Ran Wei,
  • Jing Tang,
  • Hongbo Yin,
  • Ying-ping Deng,
  • Ke Ma

摘要

Background

To 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.

Methods

This 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.

Results

42.1% 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.

Conclusions

Kmax 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.