Malavika Bhaskaranand

Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis

Background: Diabetic retinopathy (DR)—a common complication of diabetes—is the leading cause of vision loss among the working-age population in the western world. DR is largely asymptomatic, but if detected at early stages the progression to vision loss can be significantly slowed. With the increasing diabetic population there is an urgent need for automated DR screening and monitoring. To address this growing need, in this article we discuss an automated DR screening tool and extend it for automated estimation of microaneurysm (MA) turnover, a potential biomarker for DR risk. Methods: The DR screening tool automatically analyzes color retinal fundus images from a patient encounter for the various DR pathologies and collates the information from all the images belonging to a patient encounter to generate a patient-level screening recommendation. The MA turnover estimation tool aligns retinal images from multiple encounters of a patient, localizes MAs, and performs MA dynamics analysis to evaluate new, persistent, and disappeared lesion maps and estimate MA turnover rates. Results: The DR screening tool achieves 90% sensitivity at 63.2% specificity on a data set of 40u2009542 images from 5084 patient encounters obtained from the EyePACS telescreening system. On a subset of 7 longitudinal pairs the MA turnover estimation tool identifies new and disappeared MAs with 100% sensitivity and average false positives of 0.43 and 1.6 respectively. Conclusions: The presented automated tools have the potential to address the growing need for DR screening and monitoring, thereby saving vision of millions of diabetic patients worldwide.

Automated detection of diabetic retinopathy lesions on ultrawidefield pseudocolour images

We examined the sensitivity and specificity of an automated algorithm for detecting referral‐warranted diabetic retinopathy (DR) on Optos ultrawidefield (UWF) pseudocolour images.