  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 clinical settings. We
present the infrastructure employed to collect, annotate, and anonymize images
from multiple sources, demonstrating the complexity of real-world retrospective
data and its limitations. I-ODA includes 12 imaging modalities with a total of
3,668,649 ophthalmic images of 33,876 individuals from the Department of
Ophthalmology and Visual Sciences at the Illinois Eye and Ear Infirmary of the
University of Illinois Chicago (UIC) over the course of 12 years.
