I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
Nooshin Mojab, Vahid Noroozi, Abdullah Aleem, Manoj P. Nallabothula,, Joseph Baker, Dimitri T. Azar, Mark Rosenblatt, RV Paul Chan, Darvin Yi,, Philip S. Yu, Joelle A. Hallak

TL;DR
This paper introduces I-ODA, a comprehensive multi-modal longitudinal ophthalmic dataset from real-world clinical settings, aiming to enhance AI applications in ophthalmology and facilitate clinical translation.
Contribution
The paper presents a large, diverse, multi-modal ophthalmic dataset with detailed infrastructure for collection, annotation, and anonymization, addressing data scarcity in real-world ophthalmic AI research.
Findings
Contains 3.7 million images from 33,876 patients over 12 years
Includes 12 imaging modalities for comprehensive analysis
Demonstrates the complexity and variability of real-world ophthalmic data
Abstract
Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to develop and validate artificial intelligence (AI) based algorithms on real-world data enabling clinical translations. However, despite the exponential growth in AI based applications in healthcare, specifically in ophthalmology, translations to clinical settings remain challenging. Limited access to adequate and diverse real-world data inhibits the development and validation of translatable algorithms. In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different…
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Taxonomy
TopicsRetinal Imaging and Analysis · Retinal and Optic Conditions · Glaucoma and retinal disorders
