Abstract
The global burden of diabetic retinopathy (DR) continues to worsen and DR remains a leading cause of vision loss worldwide. Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) acquired at a single visit from a patient with DR. The proposed DL models were designed to predict future DR progression, defined as 2-step worsening on the Early Treatment Diabetic Retinopathy Diabetic Retinopathy Severity Scale, and were trained against DR severity scores assessed after 6, 12, and 24 months from the baseline visit by masked, well-trained, human reading center graders. The performance of one of these models (prediction at month 12) resulted in an area under the curve equal to 0.79. Interestingly, our results highlight the importance of the predictive signal located in the peripheral retinal fields, not routinely collected for DR assessments, and the importance of microvascular abnormalities. Our findings show the feasibility of predicting future DR progression by leveraging CFPs of a patient acquired at a single visit. Upon further development on larger and more diverse datasets, such an algorithm could enable early diagnosis and referral to a retina specialist for more frequent monitoring and even consideration of early intervention. Moreover, it could also improve patient recruitment for clinical trials targeting DR.
Highlights
Vision loss due to diabetic eye disease is on the rise and it is expected to reach epidemic proportions globally in the few decades
The Deep learning (DL) models were able to predict 2-step or more Early Treatment Diabetic Retinopathy Study (ETDRS) Diabetic Retinopathy Severity Scale (DRSS) worsening at 6, 12, and 24 months with an area under the curve (AUC) of 0.68 ± 0.13, 0.79 ± 0.05, and 0.77 ± 0.04, respectively
This work demonstrates the feasibility of developing a DL algorithm to identify patients who will experience diabetic retinopathy (DR) worsening by two or more ETDRS DRSS steps over the 2 years, based solely on color fundus photographs (CFPs) acquired at a single visit
Summary
Vision loss due to diabetic eye disease is on the rise and it is expected to reach epidemic proportions globally in the few decades. The current state of DR screening in the real world, based on assessment of color fundus photographs (CFPs, see Fig.1a) by a retina specialist or a trained grader, leaves a large proportion of patients undiagnosed and receiving medical help too late, in part due to low adherence and access to retina screening visits.[3,4] In-person expert examinations are impractical and unsustainable given the pandemic size of the diabetic population.[5,6,7] Notwithstanding, early detection and prevention of DR progression are essential to mitigate the rising threat of DR. In ophthalmology, groundbreaking work has recently been conducted on the automation of DR grading[15,16,17] and prediction of cardiovascular risk factors[18] by DCNN analysis of CFPs
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