A Deep Learning System (DLS) is an advanced form of artificial intelligence (AI) that can start with a mix of images from different groups and then learn to recognize unique features that organize each image into their own group. DLS functions have been widely adopted for use in multiple technologies, including face and voice recognition, and are also being applied in medicine.
The ability of a DLS to recognize disease features in a medical image could aid physicians with clinical diagnoses, and the number of studies exploring medical AI systems has exploded over the last few years, with less than 50 in 2015 to more than 200 just a year later in 2016. Medical AI is already being developed or used in fields like pathology or neurology, but there is also a need for AI-based diagnostic systems in other fields like ophthalmology, where potential is especially great because images are acquired through fairly standardized methods across clinics.
A deep learning system to detect diabetic retinopathy
Diabetic eye diseases are a leading cause of blindness around the world, and research teams are developing DLS that could enhance the ease, efficiency, and accuracy of diagnosing diabetic retinopathy, including at different stages and with diabetic macular edema.
The Google Brain AI research team created a DLS that detects diabetic retinopathy and diabetic macular edema from fundus photographs, and which was also incorporated into clinics associated with the Aravind Eye Care System in India as part of an initiative to increase global access to eye care for patients with diabetes.1,2
Rohit Varma, MD, MPH is the founding director of the USC Roski Eye Institute and a glaucoma specialist. He also holds the Grace and Emery Beardsley chair in Ophthalmology, and is a professor of Preventive Medicine at the Keck School of Medicine of University of Southern California in Los Angeles, California. Dr. Varma can be reached via e-mail at [email protected]
1. Gulshan V, Peng, L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402-2410.
2. Simonite T. Google’s AI eye doctor gets ready to go to work in India. Wired Magazine. June 8, 2017. www.wired.com/2017/06/googles-ai-eye-doctor-gets-ready-go-work-india/. Accessed July 23, 2018.
3. FDA permits marketing of IDx-DR for automated detection of diabetic retinopathy in primary care [press release]. IDx. April 12, 2018. www.eyediagnosis.net/single-post/2018/04/12/FDA-permits-marketing-of-IDx-DR-for-automated-detection-of-diabetic-retinopathy-in-primary-care. Accessed July 23, 2018.
4. Ting DSW, Cheung C Y-L, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318:2211-2223.
5. Ting DSW. Telemedicine and artificial intelligence using deep learning systems for tele-retinal diabetic retinopathy screening program. Paper presented at: The Association for Research in Vision and Ophthalmology (ARVO) 2018 Annual Meeting. April 29, 2018; Honolulu, HI.