Did you know that India has the dubious honour of being the Diabetes Capital of the World?1. The estimates show that India's diabetes burden is increasing, and it is doing so at a rapid clip. The International Diabetes Federation Atlas 2019 estimated that there are roughly 77 million cases of diabetes in the adult population of India as of 2019. It also predicts that this number will climb to 101 million in 2030 and to 134 million in 20452. The disease burden of diabetes doesn't come from diabetes alone, but the various complications that go hand in hand with it.

"Artificial intelligence is around us, and it will change medicine, including ophthalmology. Come and learn about recent developments in different subfields of ophthalmology, based on AI technology!" Andrzej Grzybowski, Professor of Ophthalmology and Chair of the Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland, and Head of the Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań, Poland, talks about the inspiration behind the virtual event, the impressive speaker list, and his own work in the field. When did you first decide to organize this online event; what was the inspiration behind it? I have thought about it for some time. However, the final argument for going ahead with the event was to receive the support from the Polish Ministry of Science and Education.

We also review Notal Vision's ForseeHome device and the company's Home OCT AI-enable platform for monitoring AMD. Kester Nahen, PhD, is the chief executive officer at Notal Vision. We'd love to hear from you! Send your comments/questions to Dr. Mali at eyecareinsider@healio.com.

Artificial Intelligence (AI) is necessary to address the large and growing deficit in retina and healthcare access globally. And mobile AI diagnostic platforms running in the Cloud may effectively and efficiently distribute such AI capability. Here we sought to evaluate the feasibility of Cloud-based mobile artificial intelligence for detection of retinal disease. And to evaluate the accuracy of a particular such system for detection of subretinal fluid (SRF) and macula edema (ME) on OCT scans. A multicenter retrospective image analysis was conducted in which board-certified ophthalmologists with fellowship training in retina evaluated OCT images of the macula. They noted the presence or absence of ME or SRF, then compared their assessment to that obtained from Fluid Intelligence, a mobile AI app that detects SRF and ME on OCT scans. Investigators consecutively selected retinal OCTs, while making effort to balance the number of scans with retinal fluid and scans without. Exclusion criteria included poor scan quality, ambiguous features, macula holes, retinoschisis, and dense epiretinal membranes. Accuracy in the form of sensitivity and specificity of the AI mobile App was determined by comparing its assessments to those of the retina specialists. At the time of this submission, five centers have completed their initial studies. This consists of a total of 283 OCT scans of which 155 had either ME or SRF ("wet") and 128 did not ("dry"). The sensitivity ranged from 82.5% to 97% with a weighted average of 89.3%. The specificity ranged from 52% to 100% with a weighted average of 81.23%. CONCLUSION: Cloud-based Mobile AI technology is feasible for the detection retinal disease. In particular, Fluid Intelligence (alpha version), is sufficiently accurate as a screening tool for SRF and ME, especially in underserved areas. Further studies and technology development is needed.

Dr Ramasamy Kim is looking at the inside of an eyeball. There is nothing particularly surprising about that: he is head of retina services at an eye hospital in southern India. The image on his computer screen shows the first blush of a condition linked to diabetes that affects millions of Indians – and can lead to blindness. The diagnosis was made not by him, or any other doctor, but by an algorithm. Over the past five years, Kim and his team at the Aravind eye hospital in Madurai have examined about 15,000 images from across the country showing the interior surface of the eyeball, known as the fundus.

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema. However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates as a proxy for DME on color fundus photographs, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict DME grades derived from OCT exams. Our "OCT-DME" model had an AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the OCT-DME model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retina specialists. In addition, we used saliency and other techniques to examine how the model is making its prediction. The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging.