Telemedicine-based diabetic retinopathy screening (DRS) in primary care settings has increased the screening rates of patients with diabetes. However, blindness from vision-threatening diabetic retinopathy (VTDR) is a persistent problem. This study examined the extent of patients' adherence to postscreening recommendations. A retrospective record review was conducted in primary care clinics of a large county hospital in the USA. All patients with diabetes detected with VTDR in two time periods, differing in record type used, were included in the study: 2012-2014, paper charts only; 2015-2017, combined paper charts/electronic medical records (EMRs), or EMRs only. Adherence rates for keeping initial ophthalmology appointments, starting recommended treatments, and keeping follow-up appointments were determined. Adequate records were available for 6046 patients; 408 (7%) were detected with VTDR and recommended for referral to ophthalmology. Only 5% completed a first ophthalmology appointment within recommended referral interval, 15% within twice the recommended interval, and 51% within 1 year of DRS. Patients screened in 2015-2017 were more likely to complete a first ophthalmology appointment than those in 2012-2014. Ophthalmic treatment was recommended in half of the patients, of whom 94% initiated treatment. A smaller percentage (41%) adhered completely to post-treatment follow-up. Overall, 28% of referred patients: (1) kept a first ophthalmology appointment; (2) were recommended for treatment; and (3) initiated the treatment. Most patients failing to keep first ophthalmology appointments continued non-ophthalmic medical care at the institution. EMRs provided more complete information than paper charts. Reducing vision impairment from VTDR requires greater emphasis on timely adherence to ophthalmology referral and follow-up. Prevention of visual loss from VTDR starts with retinopathy screening, but must include patient engagement, adherence monitoring, and streamlining ophthalmic referral and management. Revision of these processes has already been implemented at the study site, incorporating lessons from this investigation.

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p < 0.001). Our model can also detect the presence of intraretinal fluid (AUC: 0.81; 95% CI: 0.81-0.86) and subretinal fluid (AUC 0.88; 95% CI: 0.85-0.91). Using deep learning to make predictions via simple 2D images without sophisticated 3D-imaging equipment and with better than specialist performance, has broad relevance to many other applications in medical imaging.