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Related Experiment Video

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A Hybrid Transformers-based Convolutional Neural Network Model for Keratoconus Detection in Scheimpflug-based Dynamic

Hazem Abdelmotaal1, Rossen Mihaylov Hazarbasanov2,3, Ramin Salouti4

  • 1Department of Ophthalmology, Assiut University, Assiut, Egypt.

Journal of Ophthalmic & Vision Research
|July 21, 2025
PubMed
Summary

A new hybrid Transformer-CNN model accurately detects keratoconus from dynamic corneal deformation videos (DCDVs). This AI tool shows high sensitivity and specificity, offering potential for clinical use in diagnosing this eye condition.

Keywords:
Corneal ImagingDeep LearningKeratoconusScheimpflug ImagingArtificial Intelligence

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Keratoconus is a progressive eye condition affecting corneal shape.
  • Accurate and early detection of keratoconus is crucial for effective management.
  • Dynamic corneal deformation videos (DCDVs) offer rich data for corneal analysis.

Purpose of the Study:

  • To evaluate a hybrid Transformer-based convolutional neural network (CNN) model for automated keratoconus detection.
  • To assess the model's performance using stand-alone Scheimpflug-based DCDVs.
  • To determine the clinical utility of AI in diagnosing keratoconus.

Main Methods:

  • Utilized transfer learning for feature extraction from DCDVs.
  • Incorporated self-attention mechanisms to capture long-range dependencies in feature maps.
  • Classified DCDVs to directly identify keratoconus.
  • Validated model performance on two independent cohorts (275 and 546 subjects).

Main Results:

  • Achieved 93% sensitivity and 84% specificity in keratoconus detection.
  • Obtained an Area Under the Curve (AUC) of 0.97 for the keratoconus probability score on external validation data.
  • Demonstrated high accuracy in discriminating between normal and keratoconic corneas.

Conclusions:

  • The hybrid Transformer-CNN model exhibits high sensitivity and specificity for keratoconus detection using DCDVs.
  • The model's performance suggests significant potential for integration into clinical practice.
  • AI-driven analysis of DCDVs can enhance the diagnostic capabilities for keratoconus.