Author Interviews, Diabetes, Ophthalmology, Regeneron / 17.05.2019 Interview with: Robert L. Vitti, MD, MBA Vice President and Head, Ophthalmology Regeneron Pharmaceuticals Dr. Vitti discusses the recent announcement that the FDA has approved EYLEA to treat all stages of diabetic retinopathy. Can you provide additional background on this approval? Would you briefly explain diabetic retinopathy and it's impact on patients? Response: The FDA has approved EYLEA (aflibercept) Injection to treat all stages of diabetic retinopathy (DR). DR is the leading cause of blindness among working-aged American adults. Approximately 8 million people live with DR, a complication of diabetes characterized by damage to the blood vessels in the retina (per 2010 data). The disease generally starts as non-proliferative diabetic retinopathy (NPDR) and often has no warning signs or symptoms. Over time, NPDR often progresses to proliferative diabetic retinopathy (PDR), a stage in which abnormal blood vessels grow on the surface of the retina and into the vitreous cavity, potentially causing severe vision loss. (more…)
Author Interviews, Diabetes, JAMA, Ophthalmology / 13.01.2019 Interview with: Eugene Yu-Chuan Kang, MD. House Staff, Department of Ophthalmology Chang Gung Memorial Hospital Chang Gung University, School of Medicine What is the background for this study?   Response: More and more patients suffered from diabetes mellitus (DM) around the world, as well diabetic complications such as diabetic retinopathy (DR). DR is one of the major causes of blindness in working-age adults. In addition to the cost of treatment for patients with advanced DR, loss of visual function also yields a great burden to the family and society. For advanced DR, surgical interventions such as retinal laser, intravitreal injection, and vitrectomy are needed. However, those surgical interventions for severe DR can only retard or stop disease progression. If DR can be prevented or slowed by medical treatments, the burden of medical costs for treating severe DR may be decreased. Statin, an HMG-CoA reductase inhibitor, was discussed frequently in the recent years. Multiple functions of statins besides their lipid lowering effect were discovered. Previous investigations have reported that statin therapy could reduce mortality rate and decrease risk of cardiovascular diseases. In our study, we wanted to figure out if statin therapy may have any association between diabetic retinopathy.  (more…)
Author Interviews, Diabetes, JAMA, Ophthalmology, Technology / 13.12.2017 Interview with: Dr. Tien Yin Wong MD PhD Singapore Eye Research Institute, Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore Singapore What is the background for this study? What are the main findings? Response: Currently, annual screening for diabetic retinopathy (DR) is a universally accepted practice and recommended by American Diabetes Association and the International Council of Ophthalmology (ICO) to prevent vision loss. However, implementation of diabetic retinopathy screening programs across the world require human assessors (ophthalmologists, optometrists or professional technicians trained to read retinal photographs). Such screening programs are thus challenged by issues related to a need for significant human resources and long-term financial sustainability. To address these challenges, we developed an AI-based software using a deep learning, a new machine learning technology. This deep learning system (DLS) utilizes representation-learning methods to process large data and extract meaningful patterns. In our study, we developed and validated this using about 500,000 retinal images in a “real world screening program” and 10 external datasets from global populations. The results suggest excellent accuracy of the deep learning system with sensitivity of 90.5% and specificity of 91.6%, for detecting referable levels of DR and 100% sensitivity and 91.1% specificity for vision-threatening levels of DR (which require urgent referral and should not be missed). In addition, the performance of the deep learning system was also high for detecting referable glaucoma suspects and referable age-related macular degeneration (which also require referral if detected). The deep learning system was tested in 10 external datasets comprising different ethnic groups: Caucasian whites, African-Americans, Hispanics, Chinese, Indians and Malaysians (more…)