In this paper, we present CornOrb, a publicly accessible multimodal dataset of Orbscan corneal topography images and clinical annotations collected from patients in Algeria. The dataset comprises 1,454 eyes from 744 patients, including 889 normal eyes and 565 keratoconus cases. For each eye, four corneal maps are provided (axial curvature, anterior elevation, posterior elevation, and pachymetry), together with structured tabular data including demographic information and key clinical parameters such as astigmatism, maximum keratometry (Kmax), central and thinnest pachymetry, and anterior/posterior asphericity. All data were retrospectively acquired, fully anonymized, and pre-processed into standardized PNG and CSV formats to ensure direct usability for artificial intelligence research. This dataset represents one of the first large-scale Orbscan-based resources from Africa, specifically built to enable robust AI-driven detection and analysis of keratoconus using multimodal data. The data are openly available at Zenodo.
@article{arxiv.2603.21245,
title = {CornOrb: A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection},
author = {Mohammed El Amine Lazouni and Leila Ryma Lazouni and Zineb Aziza Elaouaber and Mohammed Ammar and Sofiane Zehar and Mohammed Youcef Bouayad Agha and Ahmed Lazouni and Amel Feroui and Ali H. Al-Timemy and Siamak Yousefi and Mostafa El Habib Daho},
journal= {arXiv preprint arXiv:2603.21245},
year = {2026}
}