Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study

npj Digital Medicine

Volumn 2, Issue 1, 2019, Pages

  Ting, Daniel S W ^a,b   Cheung, Carol Y ^c   Nguyen, Quang ^a   Sabanayagam, Charumathi ^a,b   Lim, Gilbert ^b   Lim, Zhan Wei ^b   Tan, Gavin S W ^a   Soh, Yu Qiang ^a   Schmetterer, Leopold ^a,d,e   Wang, Ya Xing ^f   Jonas, Jost B ^f,g   Varma, Rohit ^h   Lee, Mong Li ^b   Hsu, Wynne ^b   Lamoureux, Ecosse ^a   Cheng, Ching Yu ^a,b   Wong, Tien Yin ^a  

^a SINGAPORE EYE RESEARCH INSTITUTE   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^e MEDICAL UNIVERSITY OF VIENNA   (Austria)

^f CAPITAL MEDICAL UNIVERSITY   (China)

^g MEDICAL FACULTY MANNHEIM HEIDELBERG UNIVERSITY   (Germany)

^h UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD PRESSURE; DEEP LEARNING; DIAGNOSIS; EYE MOVEMENTS; EYE PROTECTION; EYE TRACKING; PHOTOGRAPHY; RISK ASSESSMENT; RISK PERCEPTION;

CARDIO-VASCULAR RISK FACTORS; CONDITION; DIABETIC RETINOPATHY; DIAGNOSTIC PERFORMANCE; EPIDEMIOLOGICAL STUDIES; FUNDUS PHOTOGRAPHS; HUMAN ASSESSORS; MULTI-SITE; RISK FACTORS; SYSTEMIC RISKS;

GRADING;

HEMOGLOBIN A1C; HIGH DENSITY LIPOPROTEIN CHOLESTEROL; LOW DENSITY LIPOPROTEIN CHOLESTEROL; TRIACYLGLYCEROL;

ADULT; ARTICLE; AUSTRALIA; CARDIOVASCULAR RISK; CHINA; CHOLESTEROL BLOOD LEVEL; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; DEEP LEARNING; DIABETIC RETINOPATHY; ELECTRIC POTENTIAL; ETHNIC DIFFERENCE; FEMALE; HONG KONG; HUMAN; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; OPHTHALMOLOGIST; PHOTOGRAPHY; PREVALENCE; PRIORITY JOURNAL; RETINA IMAGE; SINGAPORE; SYSTOLIC BLOOD PRESSURE; UNITED STATES;

EID: 85089606034     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-019-0097-x     Document Type: Article

References (52)

1. Collaboration, N. C. D. R. F. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 387, 1513–1530 (2016).
2. Ting, D. S., Cheung, G. C. & Wong, T. Y. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin. Exp. Ophthalmol. 44, 260–277 (2016).
3. Cheung, N., Mitchell, P. & Wong, T. Y. Diabetic retinopathy. Lancet 376, 124–136 (2010).
4. Flaxman, S. R. et al. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob. Health 5, e1221–e1234 (2017).
5. Yau, J. W. et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35, 556–564 (2012).
6. Diabetes, C. et al. Effect of intensive diabetes therapy on the progression of diabetic retinopathy in patients with type 1 diabetes: 18 years of follow-up in the DCCT/EDIC. Diabetes 64, 631–642 (2015).
7. Mohamed, Q., Gillies, M. C. & Wong, T. Y. Management of diabetic retinopathy: a systematic review. JAMA 298, 902–916 (2007).
8. Group, A. C. et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med 358, 2560–2572 (2008).
9. Photocoagulation treatment of proliferative diabetic retinopathy. Clinical application of Diabetic Retinopathy Study (DRS) findings, DRS Report Number 8. The Diabetic Retinopathy Study Research Group. Ophthalmology 88, 583–600 (1981).
10. Writing Committee for the Diabetic Retinopathy Clinical Research Network. Panretinal photocoagulation vs intravitreous ranibizumab for proliferative diabetic retinopathy: a randomized clinical trial. JAMA 314, 2137–2146 (2015).
11. Antonetti, D. A., Klein, R. & Gardner, T. W. Diabetic retinopathy. N. Engl. J. Med 366, 1227–1239 (2012).
12. The DCCT Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329, 977–986 (1993).
13. Wang, L. Z. et al. Availability and variability in guidelines on diabetic retinopathy screening in Asian countries. Br. J. Ophthalmol. 101, 1352–1360 (2017).
14. Burgess, P. I. et al. Epidemiology of diabetic retinopathy and maculopathy in Africa: a systematic review. Diabet. Med. 30, 399–412 (2013).
15. Klein, R. & Klein, B. E. Blood pressure control and diabetic retinopathy. Br. J. Ophthalmol. 86, 365–367 (2002).
16. Group, D. E. R. et al. Frequency of evidence-based screening for retinopathy in type 1 diabetes. N. Engl. J. Med. 376, 1507–1516 (2017).
17. Nathan, D. M., Bebu, I. & Lachin, J. M. Frequency of evidence-based screening for diabetic retinopathy. N. Engl. J. Med. 377, 195 (2017).
18. Group, A. S. et al. Effects of medical therapies on retinopathy progression in type 2 diabetes. N. Engl. J. Med. 363, 233–244 (2010).
19. Wong, T. Y. et al. Guidelines on diabetic eye care: The International Council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology 125, 1608–1622 (2018).
20. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444, 10.1038/nature14539 (2015).
21. Wong, T. Y. & Bressler, N. M. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 316, 2366–2367 (2016).
22. Chen, C., Seff, A., Kornhauser, A. & Xiao, J. DeepDriving: learning affordance for direct perception in autonomous driving. Proc. IEEE International Conference on Computer Vision (ICCV), 2722–2730 (2015).
23. Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
24. Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).
25. Ting, D. S. W. et al. AI for medical imaging goes deep. Nat. Med 24, 539–540 (2018).
26. 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).
27. 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).
28. Ting, D. S. W., Wu, W. C. & Toth, C. Deep learning for retinopathy of prematurity screening. Br. J. Ophthalmol. https://doi.org/10.1136/bjophthalmol-2018-313290 (2018).
29. Ting, D. S. W. et al. Artificial intelligence and deep learning in ophthalmology. Br. J. Ophthalmol. https://doi.org/10.1136/bjophthalmol-2018-313173 (2018).
30. De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
31. Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017).
32. Hwang, E. J. et al. Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clin. Infect. Dis., 10.1093/cid/ciy967 (2018).
33. Ting, D. S. W., Tan, T. E. & Lim, C. C. T. Development and Validation of a Deep Learning System for Detection of Active Pulmonary Tuberculosis on Chest Radiographs: Clinical and Technical Considerations. Clin. Infect. Dis. 10.1093/cid/ciy969 (2018).
34. Titano, J. J. et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat. Med 24, 1337–1341 (2018).
35. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
36. Gargeya, R. & Leng, T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124, 962–969 (2017).
37. Abramoff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Med. 39, 1–8 (2018).
38. Obermeyer, Z. & Lee, T. H. Lost in thought - the limits of the human mind and the future of medicine. N. Engl. J. Med. 377, 1209–1211 (2017).
39. Char, D. S., Shah, N. H. & Magnus, D. Implementing machine learning in health care - addressing ethical challenges. N. Engl. J. Med 378, 981–983 (2018).
40. Chen, J. H. & Asch, S. M. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N. Engl. J. Med 376, 2507–2509 (2017).
41. Wong, T. Y. et al. Prevalence and risk factors for diabetic retinopathy: the Singapore Malay Eye Study. Ophthalmology 115, 1869–1875 (2008).
42. Tan, G. S. et al. Ethnic differences in the prevalence and risk factors of diabetic retinopathy: the Singapore Epidemiology of Eye Diseases Study. Ophthalmology 125, 529–536 (2018).
43. Thomas, R. L. et al. Incidence of diabetic retinopathy in people with type 2 diabetes mellitus attending the Diabetic Retinopathy Screening Service for Wales: retrospective analysis. BMJ 344, e874 (2012).
44. Jones, C. D., Greenwood, R. H., Misra, A. & Bachmann, M. O. Incidence and progression of diabetic retinopathy during 17 years of a population-based screening program in England. Diabetes Care 35, 592–596 (2012).
45. Xu, J. et al. Ten-year cumulative incidence of diabetic retinopathy. The Beijing Eye Study 2001/2011. PLoS ONE 9, e111320 (2014).
46. Wilkinson, C. P. et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110, 1677–1682 (2003).
47. Nguyen, H. V. et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology 123, 2571–2580 (2016).
48. Jonas, J. B., Xu, L. & Wang, Y. X. The Beijing eye study. Acta Ophthalmol. 87, 247–261 (2009).
49. Varma, R. African American Eye Disease Study (AFEDS). http://grantome.com/grant/NIH/U10-EY023575-03. Accessed on 6 Jan 2019.
50. Tang, F. Y. et al. Determinants of quantitative optical coherence tomography angiography metrics in patients with diabetes. Sci. Rep. 7, 2575 (2017).
51. Lamoureux, E. L. et al. Methodology and early findings of the Diabetes Management Project: a cohort study investigating the barriers to optimal diabetes care in diabetic patients with and without diabetic retinopathy. Clin. Exp. Ophthalmol. 40, 73–82 (2012).
52. Paternoster, R., Brame, R., Mazerolle, P. & Piquero, A. Using the correct statistical test for the equality of regression coefficient. Criminology 36, 859–866 (1998).