162310 20251009133841.0 doi 10.1364/BOE.559663 sideral 144923 ART-2025-144923 eng Casado-Moreno, Juan Universidad de Zaragoza (orcid)0009-0009-6868-8327 Deep learning-based keratoconus detection from Scheimpflug images 2025 Access copy available to the general public Unrestricted 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. 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 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Masia, Belén Universidad de Zaragoza (orcid)0000-0003-0060-7278 Lu, Nanji Cui, Lele Consejo, Alejandra Universidad de Zaragoza (orcid)0000-0001-5186-1837 2002 385 Universidad de Zaragoza Dpto. Física Aplicada Área Física Aplicada 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 16, 8 (2025), 3047-3060 BIOMEDICAL OPTICS EXPRESS Biomedical Optics Express 2156-7085 4240097 http://zaguan.unizar.es/record/162310/files/texto_completo.pdf Versión publicada 2523568 http://zaguan.unizar.es/record/162310/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:162310 articulos driver 2025-10-09-13:25:56 ARTICLE