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Imaging and Informatics in ROP

I will briefly summarize the history of the "Imaging and Informatics in ROP" (i-ROP) consortium, originally started by Michael Chiang, and how a focus on building a multidisciplinary team with expertise in informatics and data science laid the groundwork for a number of advances in the field. Downstream work led to innovations in basic machine learning methodology, understanding of clinical diagnostic patterns and inter-observer variability which influenced the most recent international classification of ROP, a novel potential genetic association, disease epidemiology in low and middle income countries, the potential for racial/ethnic bias in AI, federated learning, and more with translational applications beyond ROP.  I will touch on a number of topics very superficially and then can discuss in more detail whatever is most interesting in the Q&A.