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Predicting Early Keratoconus Progression Using Biomechanics via Multi-Machine Learning: A Multicenter 2-Year

Yan Huo1, Ruisi Xie1, Zhengyuan Qu2

  • 1School of Medicine, Nankai University, Tianjin, China.

Clinical & Experimental Ophthalmology
|February 11, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning index, the biomechanical early progression index (BEPI), accurately predicts early keratoconus progression at the first visit. This tool aids clinicians in early detection and personalized treatment to preserve vision.

Keywords:
biomechanicsdisease progressionearly keratoconusmachine learningprognosis

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Data Science

Background:

  • Keratoconus is a progressive corneal disease.
  • Early detection of keratoconus progression is crucial for timely intervention.
  • Current methods may not reliably predict early disease progression.

Purpose of the Study:

  • To develop and validate a machine learning-based index for predicting early keratoconus progression.
  • To utilize corneal biomechanical and tomographic data for prediction.
  • To establish a reliable tool for initial patient assessment.

Main Methods:

  • A multicenter prospective cohort study involving 247 eyes.
  • Collected biomechanical (Corvis ST) and tomographic (Pentacam) data.
  • Applied machine learning algorithms (Random Forest, XGBoost, etc.) to predict progression.

Main Results:

  • The Random Forest model, termed BEPI, showed the highest predictive performance.
  • BEPI achieved high sensitivity (0.989) and specificity (0.985) in the training cohort.
  • In external validation, BEPI demonstrated 0.941 accuracy and 0.947 F1-score.

Conclusions:

  • The BEPI accurately predicts early keratoconus progression using corneal biomechanics.
  • BEPI offers a novel index for evaluating disease progression at the first visit.
  • This tool facilitates personalized clinical decisions to improve patient prognosis and preserve vision.