REFERENCES
- International Diabetes Federation. IDF diabetes atlas. 9th ed. Brussels, Belgium: International Diabetes Federation; 2019.
- Tan GS, Cheung N, Simó R, Cheung GCM, Wong TY. Diabetic macular oedema. Lancet Diabetes Endocrinol. 2017;5(2):143–155. doi:https://doi.org/10.1016/S2213-8587(16)30052-3.
- Leasher JL, Bourne RR, Flaxman SR, et al. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990 to 2010. Diabetes Care. 2016;39(9):1643–1649. doi:https://doi.org/10.2337/dc15-2171.
- The Diabetic Retinopathy Study Research Group. Photocoagulation treatment of proliferative diabetic retinopathy. Clinical application of diabetic retinopathy study (DRS) findings, DRS report number 8. Ophthalmology. 1981;88(7): 583–600.
- Early Treatment Diabetic Retinopathy Study Research Group. Early photocoagulation for diabetic retinopathy: ETDRS report number 9. Ophthalmology. 1991;98(5):766–785. doi:https://doi.org/10.1016/S0161-6420(13)38011-7.
- Fields MM, Chevlen E. Screening for disease: making evidence-based choices. Clin J Oncol Nurs. 2006;10(1):73–76. doi:https://doi.org/10.1188/06.CJON.73-76.
- Solomon SD, Chew E, Duh EJ, et al. Diabetic retinopathy: a position statement by the American diabetes association. Diabetes Care. 2017;40(3):412–418. doi:https://doi.org/10.2337/dc16-2641.
- Wong TY, Sun J, Kawasaki R, et al. Guidelines on diabetic eye care. Ophthalmology. 2018;125(10):1608–1622. doi:https://doi.org/10.1016/j.ophtha.2018.04.007.
- National Health and Research Council. Guidelines for the management of diabetic retinopathy. Canberra, Commonwealth of Australia; 2008.
- McKay R, McCarty CA, Taylor HR. Diabetic retinopathy in Victoria, Australia: the visual impairment project. British J Ophthalmol. 2000;84(8):865–870. doi:https://doi.org/10.1136/bjo.84.8.865.
- Lee PP, Feldman ZW, Ostermann J, Brown DS, Sloan FA. Longitudinal rates of annual eye examinations of persons with diabetes and chronic eye diseases. Ophthalmology. 2003;110(10):1952–1959. doi:https://doi.org/10.1016/S0161-6420(03)00817-0.
- Soto-Pedre E, Hernaez-Ortega MC. Screening coverage for diabetic retinopathy using a three-field digital non-mydriatic fundus camera. Prim Care Diabetes. 2008;2(3):141–146. doi:https://doi.org/10.1016/j.pcd.2008.04.003.
- Copeland BJ (2019, November 19). Artificial intelligence. Retrieved January 2020, from https://www.britannica.com/technology/artificial-intelligence
- CBS News. (2011, February 16). Computer crushes the competition on ‘jeopardy!’. https://www.cbsnews.com/news/computer-crushes-the-competition-on-jeopardy/
- Bishop C. Pattern recognition and machine learning. New York, NY: Springer; 2006.
- Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi:https://doi.org/10.1038/nature14539.
- Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res. 2018;67:1–29. doi:https://doi.org/10.1016/j.preteyeres.2018.07.004.
- Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135(11):1170–1176. doi:https://doi.org/10.1001/jamaophthalmol.2017.3782.
- Dastin J (2018, October 9). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
- Osareh A, Shadgar B, Markham R. A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images. IEEE Trans Inf Technol Biomed. 2009;13(4):535–545. doi:https://doi.org/10.1109/TITB.2008.2007493.
- Early Treatment Diabetic Retinopathy Study Research Group. Grading diabetic retinopathy from stereoscopic color fundus photographs—an extension of the modified airlie house classification: ETDRS report number 10. Ophthalmology. 1991;98(5):786–806. doi:https://doi.org/10.1016/S0161-6420(13)38012-9.
- Silva PS, El-Rami H, Barham R, et al. Hemorrhage and/or microaneurysm severity and count in ultrawide field images and early treatment diabetic retinopathy study photography. Ophthalmology. 2017;124(7):970–976. doi:https://doi.org/10.1016/j.ophtha.2017.02.012.
- Yannuzzi LA, Rohrer KT, Tindel LJ, et al. Fluorescein angiography complication survey. Ophthalmology. 1986;93(5):611–617. doi:https://doi.org/10.1016/S0161-6420(86)33697-2.
- Frame AJ, Undrill PE, Cree MJ, et al. A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Comput Biol Med. 1998;28(3):225–238. doi:https://doi.org/10.1016/S0010-4825(98)00011-0.
- Ashraf M, Sampani K, Abdelal O, et al. Disparity of microaneurysm count between ultrawide field colour imaging and ultrawide field fluorescein angiography in eyes with diabetic retinopathy. British J Ophthalmol. 2020;104:1762–1767. doi:https://doi.org/10.1136/bjophthalmol-2019-315807. [epub ahead of print].
- Niemeijer M, Ginneken BV, Staal J, Suttorp-Schulten M, Abramoff M. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging. 2005;24(5):584–592. doi:https://doi.org/10.1109/TMI.2005.843738.
- Rosas-Romero R, Martínez-Carballido J, Hernández-Capistrán J, Uribe-Valencia LJ. A method to assist in the diagnosis of early diabetic retinopathy: image processing applied to detection of microaneurysms in fundus images. Comput Med Imaging Graph. 2015;44:41–53. doi:https://doi.org/10.1016/j.compmedimag.2015.07.001.
- Bhaskaranand M, Ramachandra C, Bhat S, et al. Automated diabetic retinopathy screening and monitoring using retinal fundus image analysis. J Diabetes Sci Technol. 2016;10(2):254–261. doi:https://doi.org/10.1177/1932296816628546.
- Singer DE, Nathan DM, Fogel HA, Schachat AP. Screening for diabetic retinopathy. Ann Intern Med. 1992;116(8):660–671. doi:https://doi.org/10.7326/0003-4819-116-8-660.
- Sinthanayothin C, Boyce JF, Williamson TH, et al. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Med. 2002;19(2):105–112. doi:https://doi.org/10.1046/j.1464-5491.2002.00613.x.
- Jaya T, Dheeba J, Singh NA. Detection of hard exudates in colour fundus images using fuzzy support vector machine-based expert system. J Digit Imaging. 2015;28(6):761–768. doi:https://doi.org/10.1007/s10278-015-9793-5.
- Early Treatment Diabetic Retinopathy Study Research Group. Fundus photographic risk factors for progression of diabetic retinopathy: ETDRS report number 12. Ophthalmology. 1991;98(5):823–833. doi:https://doi.org/10.1016/S0161-6420(13)38014-2.
- Goatman KA, Fleming AD, Philip S, Williams GJ, Olson JA, Sharp PF. Detection of new vessels on the optic disc using retinal photographs. IEEE Trans Med Imaging. 2011;30(4):972–979. doi:https://doi.org/10.1109/TMI.2010.2099236.
- Jelinek HF, Cree MJ, Leandro JJG, Soares JVB, Cesar RM, Luckie A. Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy. J Opt Soc Am A. 2007;24(5):1448–1456. doi:https://doi.org/10.1364/JOSAA.24.001448.
- Cuadros J, Bresnick G. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J Diabetes Sci Technol. 2009;3(3):509–516. doi:https://doi.org/10.1177/193229680900300315.
- Nguyen H, Tan G, Tapp R, et al. Cost-effectiveness of a National telemedicine diabetic retinopathy screening program in Singapore. Ophthalmology. 2016;123(12):2571–2580. doi:https://doi.org/10.1016/j.ophtha.2016.08.021.
- Niemeijer M, Ginneken BV, Cree MJ, et al. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging. 2010;29(1):185–195. doi:https://doi.org/10.1109/TMI.2009.2033909.
- Kauppi T, Kalesnykiene V, Kamarainen J-K, et al., DIARETDB1 diabetic retinopathy database and evaluation protocol, In Proc of the 11th Conf. on Medical Image Understanding and Analysis (Aberystwyth, Wales, 2007)
- Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the messidor database. Image Anal Stereol. 2014;33(3):231–234. doi:https://doi.org/10.5566/ias.1155.
- Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Npj Digital Med. 2018;1(39). doi:https://doi.org/10.1038/s41746-018-0040-6.
- 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(22):2402–2410. doi:https://doi.org/10.1001/jama.2016.17216.
- Ting DSW, Cheung CY-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(22):2211–2223. doi:https://doi.org/10.1001/jama.2017.18152.
- Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124:962–969.
- Abràmoff MD, Folk JC, Han DP, et al. Automated Analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013;131(3):351–357. doi:https://doi.org/10.1001/jamaophthalmol.2013.1743.
- Abràmoff MD, Lou Y, Erginay A, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Visual Sci. 2016;57(13):5200–5206. doi:https://doi.org/10.1167/iovs.16-19964.
- Bhaskaranand M, Ramachandra C, Bhat S, et al. The value of automated diabetic retinopathy screening with the eye art system: a study of more than 100,000 consecutive encounters from people with diabetes. Diabetes Technol Ther. 2019;21(11):635–643. doi:https://doi.org/10.1089/dia.2019.0164.
- Arcadu F, Benmansour F, Maunz A, Willis J, Haskova Z, Prunotto M. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. Npj Digital Med. 2019;2(92). doi:https://doi.org/10.1038/s41746-019-0172-3.
- Aiello LP, Odia I, Glassman AR, et al. Comparison of early treatment diabetic retinopathy study standard 7-field imaging with ultrawide-field imaging for determining severity of diabetic retinopathy. JAMA Ophthalmol. 2019;137(1):65–73. doi:https://doi.org/10.1001/jamaophthalmol.2018.4982.
- Silva PS, Cavallerano JD, Haddad NMN, et al. Peripheral lesions identified on ultrawide field imaging predict increased risk of diabetic retinopathy progression over 4 years. Ophthalmology. 2015;122:949–956.