Valentina Bellemo1, Gilbert Lim1,2, Tyler Hyungtaek Rim1,3, Gavin S W Tan1,3, Carol Y Cheung4, SriniVas Sadda5, Ming-Guang He6, Adnan Tufail7, Mong Li Lee2, Wynne Hsu2, Daniel Shu Wei Ting8,9. 1. Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore. 2. School of Computing, National University of Singapore, Singapore, Singapore. 3. Duke-NUS Medical School, Singapore, Singapore. 4. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong. 5. Doheny Eye Institute, University of California, Los Angeles, CA, USA. 6. Center of Eye Research Australia, Melbourne, Victoria, Australia. 7. Moorfields Eye Hospital & Institute of Ophthalmology, UCL, London, UK. 8. Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore. daniel.ting.s.w@singhealth.com.sg. 9. Duke-NUS Medical School, Singapore, Singapore. daniel.ting.s.w@singhealth.com.sg.
Abstract
PURPOSE OF REVIEW: This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created. RECENT FINDINGS: Over the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies. Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.
PURPOSE OF REVIEW: This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created. RECENT FINDINGS: Over the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies. Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.
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