Deep learning-based keratoconus detection from Scheimpflug images

Casado-Moreno, Juan (Universidad de Zaragoza) ; Masia, Belén (Universidad de Zaragoza) ; Lu, Nanji ; Cui, Lele ; Consejo, Alejandra (Universidad de Zaragoza)

Resumen: This study evaluates the effectiveness of deep learning techniques applied to raw Scheimpflug corneal images for keratoconus detection, with a particular focus on forme fruste (FF) keratoconus, which refers to preclinical cases. Using an original dataset of 22,750 images from 910 eyes, a deep learning model based on transfer learning with a pre-trained VGG16 architecture was trained, incorporating specific preprocessing steps and data augmentation strategies. The proposed approach achieved an overall accuracy of 90.70%, with a sensitivity of 80.57%, and a specificity of 80.56% for FF keratoconus classification, and an AUC of 0.89. For clinical keratoconus, the model demonstrated a sensitivity of 93.28%, a specificity of 99.40%, and an AUC of 1.00. These findings highlight the potential of leveraging raw Scheimpflug images in deep learning-based keratoconus detection, particularly for identifying early-stage structural changes that may not be apparent in conventional topographic assessments.

Idioma: Inglés

DOI: 10.1364/BOE.559663

Año: 2025

Publicado en: Biomedical Optics Express 16, 8 (2025), 3047-3060
ISSN: 2156-7085

Financiación: info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101162733/EU/Visual Impairment Screening using Images from Ophthalmology and Novel pathways for Structural Analysis and Fast Evaluation/VISIONSAFE

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Aplicada (Dpto. Física Aplicada)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)