Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)
Link
http://www.nature.com/articles/s41746-019-0099-8.pdf
Reference26 articles.
1. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).
2. Krause, J. et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125, 1264–1272 (2018).
3. Ting, D. S. W. 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 318, 2211–2223 (2017).
4. Jenchitr, W. et al. The national survey of blindness low vision and visual impairment in thailand 2006–2007. Thai. J. Pub. Hlth. Ophthalmol. 21, 10 (2007).
5. Isipradit, S. et al. The First Rapid Assessment of Avoidable Blindness (RAAB) in Thailand. PLoS ONE 9, e114245 (2014).
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