Purpose To relate the concept of the retinal neurovascular unit and its alterations in diabetes to the pathophysiology of diabetic retinopathy. Methods Case illustrations and conceptual frameworks are presented that illustrate adaptive and maladaptive “dis-integration” of the retinal neurovascular unit with the progression of diabetes. Results Retinopathy treatment should address pathophysiologic processes rather than pathologic lesions as is current practice. Conclusions Future improvements in the treatment of diabetic retinopathy requires deeper understanding of the cellular and molecular changes induced by diabetes, coupled with the use of quantitative phenotyping methods that assess the pathophysiologic processes.

PurposeTo test whether quantitative functional tests and optical coherence tomography (OCT)-defined structure can serve as effective tools to diagnose and monitor early diabetic neuroretinal disease.MethodsFifty-seven subjects with diabetes (23 without diabetic retinopathy [no DR], 19 with mild nonproliferative diabetic retinopathy [mild NPDR], 15 with moderate to severe [moderate NPDR]), and 18 controls underwent full ophthalmic examination, fundus photography, spectral-domain optical coherence tomography (SD-OCT), e-ETDRS (Early Treatment Diabetic Retinopathy Study) acuity, and the quick contrast sensitivity function (qCSF) method. Perimetry testing included short-wavelength automated perimetry (SWAP), standard automated perimetry (SAP), frequency doubling perimetry (FDP), and rarebit perimetry (RBP).ResultsETDRS acuity and RBP were not sensitive for functional differences among subjects with diabetes. AULCSF, a metric of qCSF, was reduced in diabetics with moderate compared to mild NPDR (P = 0.03), and in subjects with no DR compared to controls (P = 0.04). SWAP and SAP mean deviation (MD) and foveal threshold (FT) were reduced in moderate compared to mild NPDR (SWAP, MD P = 0.002, FT P = 0.0006; SAP, MD P = 0.02, FT P = 0.007). FDP 10-2 showed reduced MD in moderate compared to mild NPDR (P = 0.02), and FDP 24-2 revealed reduced pattern standard deviation (PSD) in mild NPDR compared to no DR (P = 0.02). Structural analysis revealed thinning of the ganglion cell layer and inner plexiform layer (GCL+IPL) of moderate NPDR subjects compared to controls. The thinner GCL+IPL correlated with impaired retinal function.ConclusionsThis multimodal testing analysis reveals insights into disruption of the neuroretina in diabetes and may accelerate the testing of novel therapies.

PurposeHigh-quality, wide-field retinal imaging is a valuable method for screening preventable, vision-threatening diseases of the retina. Smartphone-based retinal cameras hold promise for increasing access to retinal imaging, but variable image quality and restricted field of view can limit their utility. We developed and clinically tested a smartphone-based system that addresses these challenges with automation-assisted imaging.MethodsThe system was designed to improve smartphone retinal imaging by combining automated fixation guidance, photomontage, and multicolored illumination with optimized optics, user-tested ergonomics, and touch-screen interface. System performance was evaluated from images of ophthalmic patients taken by nonophthalmic personnel. Two masked ophthalmologists evaluated images for abnormalities and disease severity.ResultsThe system automatically generated 100° retinal photomontages from five overlapping images in under 1 minute at full resolution (52.3 pixels per retinal degree) fully on-phone, revealing numerous retinal abnormalities. Feasibility of the system for diabetic retinopathy (DR) screening using the retinal photomontages was performed in 71 diabetics by masked graders. DR grade matched perfectly with dilated clinical examination in 55.1% of eyes and within 1 severity level for 85.2% of eyes. For referral-warranted DR, average sensitivity was 93.3% and specificity 56.8%.ConclusionsAutomation-assisted imaging produced high-quality, wide-field retinal images that demonstrate the potential of smartphone-based retinal cameras to be used for retinal disease screening.Translational RelevanceEnhancement of smartphone-based retinal imaging through automation and software intelligence holds great promise for increasing the accessibility of retinal screening.