1. Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

    Deep learning to detect optical coherence tomography-derived diabetic macular edema from retinal photographs: a multicenter validation study

    Purpose: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from two-dimensional color fundus photography (CFP), where the reference standard for retinal thickness and fluid presence is derived from three-dimensional optical coherence tomography (OCT). Design: Retrospective validation of a DLS across international datasets. Participants: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using datasets from Thailand, the United Kingdom (UK) and the United States and validated using 3,060 unique eyes from 1,582 patients across screening populations in Australia, India and ...

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