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Data from clinical real-world settings is characterized by variability inquality, machine-type, setting, and source. One of the primary goals of medicalcomputer vision is to develop and validate artificial intelligence (AI) basedalgorithms on real-world data enabling clinical translations. However, despitethe exponential growth in AI based applications in healthcare, specifically inophthalmology, translations to clinical settings remain challenging. Limitedaccess to adequate and diverse real-world data inhibits the development andvalidation of translatable algorithms. In this paper, we present a newmulti-modal longitudinal ophthalmic imaging dataset, the Illinois OphthalmicDatabase Atlas (I-ODA), with the goal of advancing state-of-the-art computervision applications in ophthalmology, and improving upon the translatablecapacity of AI based applications across different clinical settings. Wepresent the infrastructure employed to collect, annotate, and anonymize imagesfrom multiple sources, demonstrating the complexity of real-world retrospectivedata and its limitations. I-ODA includes 12 imaging modalities with a total of3,668,649 ophthalmic images of 33,876 individuals from the Department ofOphthalmology and Visual Sciences at the Illinois Eye and Ear Infirmary of theUniversity of Illinois Chicago (UIC) over the course of 12 years.

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications