The impact of artificial intelligence in screening for diabetic retinopathy in India
Author:
Publisher
Springer Science and Business Media LLC
Subject
Ophthalmology
Link
http://www.nature.com/articles/s41433-019-0626-5.pdf
Reference8 articles.
1. Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Meta-Analysis For Eye Disease (META-EYE) Study Group. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556–64.
2. Rema M, Premkumar S, Anitha B, Deepa R, Pradeepa R, Mohan V. Prevalence of diabetic retinopathy in urban India: The Chennai urban rural epidemiology study (CURES) eye study, I. Investig Ophthalmol Vis Sci. 2005;46:2328–33.
3. Namperumalswamy P, Nirmalan PK, Ramaswamy K. Developing a screening program to detect sight threatening retinopathy in south India. Diabetes Care. 2003;26:1831–35.
4. Raman R, Srinivasan S, Virmani S, Sivaprasad S, Rao C, Rajalakshmi R. Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye. 2019;33:97–109.
5. Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphonebased fundus photography using artificial intelligence. Eye. 2018;32:1138–44.
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