A deep learning system for predicting time to progression of diabetic retinopathy
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Biochemistry, Genetics and Molecular Biology,General Medicine
Link
https://www.nature.com/articles/s41591-023-02742-5.pdf
Reference5 articles.
1. Wong, T. Y. et al. Diabetic retinopathy. Nat. Rev. Dis. Primers 2, 16012 (2016). A review article that discusses DR epidemiology, pathophysiology, diagnosis, and treatment options, as well as its effect on quality of life.
2. Stratton, I. M. et al. UKPDS 50: risk factors for incidence and progression of retinopathy in type II diabetes over 6 years from diagnosis. Diabetologia 44, 156–163 (2001). This paper reports the high variability in risk of DR progression and the need for good glycemic control and assiduous treatment of hypertension.
3. Bora, A. et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit. Health 3, e10–e19 (2021). This paper reports a deep learning system for predicting DR development from fundus images, which are independent of and more informative than available risk factors.
4. Liu, H. et al. Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis. Lancet Glob. Health 11, e456–e465 (2023). This paper reports that AI coupled with teleophthalmology is cost-effective in both rural and urban China, which presents an opportunity to promote equity in eye health.
5. Rajpurkar, P. et al. AI in health and medicine. Nat. Med. 28, 31–38 (2022). A review article that presents the advances, challenges and potential in the field of medical AI.
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