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

2026-03-22

AI Summary

This study addresses the current lack of large-scale, multimodal, and publicly available corneal topography datasets from African populations for keratoconus detection. To bridge this gap, the authors collected four types of corneal topographic images and structured clinical parameters from 1,454 eyes (including 889 normal and 565 keratoconic eyes) using the Orbscan IIz device. After anonymization and standardization, the data were released publicly on the Zenodo platform in PNG image and CSV formats. This dataset represents the first large-scale, multimodal, open-access Orbscan dataset derived specifically from an African cohort and is readily suitable for training artificial intelligence models, thereby significantly advancing research on automated keratoconus detection.

Technology Category

• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• keratoconus detection
• multimodal dataset
• corneal topography
• Orbscan
• AI-driven diagnosis

Innovation

• multimodal dataset
• Orbscan corneal topography
• keratoconus detection
• AI in ophthalmology
• publicly available data