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Purpose: Recent machine learning methods have produced models which can grade retinal fundus images for diabetic retinopathy (DR) with doctor-level accuracy. The impact of these models on DR diagnosis in assisted-read settings has not yet been measured. We investigated whether surfacing model predictions and explanatory saliency maps (" masks") to doctors improved DR grading accuracy, speed, and confidence.

Methods: We recruited 9 ophthalmologists to read 1,806 cases each for DR severity. Readers graded 45 fundus images centered around the macula. The image sample was representative of the diabetic population, and was adjudicated by 3 retina specialists (1 also a reader) for ground truth grades.