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Health·G Space·Evidence-backed gain·Published 2026-07-18

Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography

Abstract: Aim To develop and evaluate the diagnostic accuracy of deep learning (DL) models in differentiating keratoconus (KC) from normal eyes with regular astigmatism. Methods A comparative cross-sectional study was conducted at the Cornea and Diagnostic Department of Al-Shifa Trust Eye Hospital, Pakistan. Galilei dual Scheimpflug-based corneal topography was performed to obtain four corneal maps: anterior axial curvature, posterior axial curvature, corneal thickness, and posterior elevation. Four convolutional neural n…

TRV-2026-0256Peer-reviewedPermanent record — cite & verify
Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography

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The quick read

By July 2026, a cross-sectional study at Al-Shifa Trust Eye Hospital in Pakistan developed four CNN models on 5602 Scheimpflug-derived corneal maps from 1411 eyes to distinguish keratoconus from normal eyes, reporting internal accuracies of 98.1% to 99.2% and AUCs up to 1.00, with external validation on 85 participants confirming 97.1% to 98.3% accuracy.

High-accuracy automated detection matters because early keratoconus identification guides timely crosslinking and refractive management, yet the reported performance is confined to a 10-40 year age range, a single institution, and one imaging platform, leaving open questions about generalizability, prospective clinical workflow integration, and impact on patient outcomes.

Main points
  • Study was comparative cross-sectional at Cornea and Diagnostic Department of Al-Shifa Trust Eye Hospital, Pakistan using Galilei dual Scheimpflug topography.
  • Dataset comprised 5602 corneal maps from 1411 eyes of 827 participants aged 10 to 40y, including 790 KC and 621 normal eyes.
  • Four models were trained on anterior axial curvature, posterior axial curvature, corneal thickness, and posterior elevation maps.
  • External validation used independent dataset of 85 participants, 150 eyes, 1050 maps.
Gain

Convolutional neural networks trained on four Scheimpflug-based corneal maps differentiated keratoconus from normal astigmatic eyes with up to 99.2% accuracy and AUC 1.00, with external validation retaining 97-98% accuracy.

The rundown

Researchers extracted four map types per eye from Galilei dual Scheimpflug tomography and trained DenseNet-121, ResNet-50, Inception-V3, and EfficientNet-B0 to classify KC versus normal regular astigmatism, evaluating AUC, accuracy, sensitivity, and specificity.

Internal testing showed DenseNet-121 at 99.2% accuracy and ResNet-50 at 99.0% with both reaching AUC 1.00, while external validation on 150 eyes maintained high performance, supporting potential clinical implementation for optimized KC management.

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