CornOrb: A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection

TL;DR

CornOrb is a large, publicly available multimodal dataset of Orbscan corneal topography images and clinical data from African patients, designed to facilitate AI research in keratoconus detection.

Contribution

This paper introduces CornOrb, one of the first large-scale Orbscan datasets from Africa, combining multimodal corneal maps and clinical annotations for AI-based keratoconus analysis.

Findings

Dataset includes 1,454 eyes with detailed corneal maps and clinical data.

Data is fully anonymized and standardized for AI research.

Open access at Zenodo promotes global research collaboration.

Abstract

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.