The far majority of clinical trials in diabetes exclude patients with active retinal disease, as interventions that lower glucose rapidly can temporarily worsen retinopathy. This was originally shown in type 1 diabetes  but more recently also in type 2 diabetes [2, 3]. Screening for diabetic retinopathy before inclusion in a clinical trial is relatively cumbersome, also because it often involves a separate visit to an ophthalmologist. The use of a fundus camera with offline interpretation by an ophthalmologist has gained widespread use in clinical practice, but not so much in the field of clinical trials.
In this blog I will briefly describe four recent studies on artificial intelligence approaches to automate the interpretation of retinal images. I will conclude with an outlook on how this may facilitate the screening of potential trial participants for diabetic retinopathy. But first a brief introduction to deep learning, the methodology applied in all these papers.