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  1. Home
  3. Deep Learning-based Keratoconus Detection From Scheimpflug Images.
  1. Home
  3. Deep Learning-based Keratoconus Detection From Scheimpflug Images.

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Deep learning-based keratoconus detection from Scheimpflug images.

Juan Casado-Moreno1, Belen Masia1, Nanji Lu2

  • 1Aragon Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.

Biomedical Optics Express
|August 14, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Deep learning effectively detects keratoconus using raw Scheimpflug images, even in early preclinical (forme fruste) stages. This AI approach shows high accuracy for identifying subtle corneal changes, improving early diagnosis potential.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Keratoconus is a progressive corneal ectasia.
  • Early detection, especially forme fruste (FF) keratoconus, is crucial for timely intervention.
  • Conventional topographic assessments may miss subtle, preclinical changes.

Purpose of the Study:

  • To evaluate deep learning models for keratoconus detection using raw Scheimpflug images.
  • To specifically assess the model's capability in identifying forme fruste keratoconus.
  • To determine the diagnostic performance of AI in early keratoconus detection.

Main Methods:

  • A deep learning model utilizing transfer learning with a VGG16 architecture was developed.
  • The model was trained on a dataset of 22,750 raw Scheimpflug corneal images from 910 eyes.
  • Preprocessing and data augmentation techniques were incorporated to enhance model robustness.

    • Main Results:

    • The model achieved 90.70% accuracy for FF keratoconus classification (AUC 0.89).
    • Sensitivity and specificity for FF keratoconus were 80.57% and 80.56%, respectively.
    • For clinical keratoconus, the model demonstrated high performance with 93.28% sensitivity, 99.40% specificity, and an AUC of 1.00.

    Conclusions:

    • Deep learning applied to raw Scheimpflug images is effective for keratoconus detection.
    • The approach shows significant potential for identifying early-stage structural changes characteristic of FF keratoconus.
    • This AI-driven method may enhance the detection of preclinical keratoconus compared to traditional methods.