aceArtificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography
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…
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.
Findings are bounded to a single-center cohort aged 10 to 40y and to Galilei dual Scheimpflug imaging, limiting generalizability to other ages, devices, and settings.
Evidence
- Peer-reviewedInternational Journal of Ophthalmology2026-07-18
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Truvace Impact Record TRV-2026-0256, v1: “Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography.” Truvace, 2026-07-18. /record/TRV-2026-0256 (accessed at citation time). sha256 52e59cfeb8cac18f…
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