Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography

1.

IDF diabetes atlas - 2017 Atlas. 2018. http://diabetesatlas.org/resources/2017-atlas.html.

2.

UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet Lond Engl. 1998;352:837–53.

3.

Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M. Diabetes Control and Complications Trial Research Group et al. 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. 1993;329:977–86.

4.

Lachin JM, White NH, Hainsworth DP, Sun W, Cleary PA, Nathan DM. Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group 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. 2015;64:631–42.

5.

Antonetti DA, Klein R, Gardner TW. Diabetic retinopathy. N. Engl J Med. 2012;366:1227–39. Mar 29.

6.

Ajlan RS, Silva PS, Sun JK. Vascular endothelial growth factor and diabetic retinal disease. Semin Ophthalmol. 2016;31:40–8.

7.

Osaadon P, Fagan XJ, Lifshitz T, Levy J. A review of anti-VEGF agents for proliferative diabetic retinopathy. Eye. 2014;28:510–20

8.

Gündüz K, Bakri SJ. Management of proliferative diabetic retinopathy. Compr Ophthalmol Update. 2007;8:245–56.

9.

Boyer DS, Yoon YH, Belfort R, Bandello F, Maturi RK, Augustin AJ, et al. Three-year, randomized, sham-controlled trial of dexamethasone intravitreal implant in patients with diabetic macular edema. Ophthalmology. 2014;121:1904–14.

10.

Early Treatment Diabetic Retinopathy Study Research Group Early photocoagulation for diabetic retinopathy. ETDRS report number 9.Ophthalmology . 1991;98:766–85.

11.

Early Treatment Diabetic Retinopathy Study research group. Photocoagulation for diabetic macular edema. Early Treatment Diabetic Retinopathy Study report number 1. Arch Ophthalmol Chic Ill 1960. 1985;103:1796–806.

12.

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;88:583–600.

13.

Ferris FL. How effective are treatments for diabetic retinopathy? JAMA. 1993;269:1290–1.

14.

Murchison AP, Hark L, Pizzi LT, Dai Y, Mayro EL, Storey PP, et al. Non-adherence to eye care in people with diabetes. BMJ Open Diabetes Res Care. 2017;5. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5574424/.

15.

Tao X, Li J, Zhu X, Zhao B, Sun J, Ji L, et al. Association between socioeconomic status and metabolic control and diabetes complications: a cross-sectional nationwide study in Chinese adults with type 2 diabetes mellitus. Cardiovasc Diabetol. 2016;15:61.

16.

Wang SY, Andrews CA, Herman WH, Gardner TW, Stein JD. Incidence and risk factors for developing diabetic retinopathy among youths with type 1 or type 2 diabetes throughout the United States. Ophthalmology. 2017;124:424–30.

17.

Association AD. Standards of medical care for patients with diabetes mellitus. Diabetes Care 1989;12:365–8.

18.

Brechner RJ, Cowie CC, Howie LJ, Herman WH, Will JC, Harris MI. Ophthalmic examination among adults with diagnosed diabetes mellitus. JAMA 1993;270:1714–8.

19.

Fisher MD, Rajput Y, Gu T, Singer JR, Marshall AR, Ryu S, et al. Evaluating adherence to dilated eye examination recommendations among patients with diabetes, combined with patient and provider perspectives. Am Health Drug Benefits. 2016;9:385–93.

20.

Zhang X, Beckles GL, Chou C-F, Saaddine JB, Wilson MR, Lee PP, et al. Socioeconomic disparity in use of eye care services among US adults with age-related eye diseases: National Health Interview Survey, 2002 and 2008. JAMA Ophthalmol. 2013;131:1198–206.

21.

Fathy C, Patel S, Sternberg P, Kohanim S. Disparities in adherence to screening guidelines for diabetic retinopathy in the United States: a comprehensive review and guide for future directions. Semin Ophthalmol. 2016;31:364–77.

22.

Paz SH, Varma R, Klein R, Wu J, Azen SP. Los Angeles Latino Eye Study Group Noncompliance with vision care guidelines in Latinos with type 2 diabetes mellitus: the Los Angeles Latino Eye Study. Ophthalmology. 2006;113:1372–7.

23.

Ellish NJ, Royak-Schaler R, Passmore SR, Higginbotham EJ. Knowledge, attitudes, and beliefs about dilated eye examinations among African-Americans. Invest Ophthalmol Vis Sci. 2007;48:1989–94.

24.

Gibson DM. Eye care availability and access among individuals with diabetes, diabetic retinopathy, or age-related macular degeneration. JAMA Ophthalmol. 2014;132:471–7.

25.

Brown M, Kuhlman D, Larson L, Sloan K, Ablah E, Konda K, et al. Does availability of expanded point-of-care services improve outcomes for rural diabetic patients? Prim Care. Diabetes. 2013;7:129–34.

26.

Rosenberg JB, Friedman IB, Gurland JE. Compliance with screening guidelines for diabetic retinopathy in a large academic children's hospital in the Bronx. J Diabetes Complications. 2011;25:222–6.

27.

DeBuc DC. The role of retinal imaging and portable screening devices in tele-ophthalmology applications for diabetic retinopathy management. Curr Diab Rep. 2016;16:132.

28.

Mansberger SL, Sheppler C, Barker G, Gardiner SK, Demirel S, Wooten K, et al. Long-term comparative effectiveness of telemedicine in providing diabetic retinopathy screening examinations: a randomized clinical trial. JAMA Ophthalmol. 2015;133:518–25.

29.

Jani PD, Forbes L, Choudhury A, Preisser JS, Viera AJ, Garg S. Evaluation of diabetic retinal screening and factors for ophthalmology referral in a telemedicine network. JAMA Ophthalmol. 2017;135:706–14.

30.

Kirkizlar E, Serban N, Sisson JA, Swann JL, Barnes CS, Williams MD. Evaluation of telemedicine for screening of diabetic retinopathy in the Veterans Health Administration. Ophthalmology. 2013;120:2604–10.

31.

Zimmer-Galler IE, Kimura AE, Gupta S. Diabetic retinopathy screening and the use of telemedicine. Curr Opin Ophthalmol. 2015;26:167–72.

32.

Liew G, Michaelides M, Bunce C. A comparison of the causes of blindness certifications in England and Wales in working age adults (16–64 years), 1999–2000 with 2009–2010. BMJ Open. 2014;4:e004015.

33.

Toy BC, Myung DJ, He L, Pan CK, Chang RT, Polkinhorne A, et al. Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease. Retin Philos Pa. 2016;36:1000–8.

34.

Russo A, Mapham W, Turano R, Costagliola C, Morescalchi F, Scaroni N, et al. Comparison of smartphone ophthalmoscopy with slit-lamp biomicroscopy for grading vertical cup-to-disc ratio. J Glaucoma. 2016;25:e777–81.

35.

Muiesan ML, Salvetti M, Paini A, Riviera M, Pintossi C, Bertacchini F, et al. Ocular fundus photography with a smartphone device in acute hypertension. J Hypertens. 2017;35:1660–5.

36.

Toslak D, Ayata A, Liu C, Erol MK, Yao X. Wide-field smartphone fundus video camera based on miniaturized indirect ophthalmoscopy. Retin Philos Pa. 2018;38:438–41.

37.

Giardini ME, Livingstone IAT, Jordan S, Bolster NM, Peto T, Burton M, et al. A smartphone based ophthalmoscope. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf. 2014;2014:2177–80.

38.

3D printed smartphone indirect lens adapter for rapid, high quality retinal imaging. J Mobile Technol Med. 2018. https://www.journalmtm.com/2014/3d-printed-smartphone-indirect-lens-adapter-for-rapid-high-quality-retinal-imaging.

39.

Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172:1122–31.e9.

40.

Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria. Transl Res J Lab Clin Med. 2018;194:36–55.

41.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–10.

42.

Raju M, Pagidimarri V, Barreto R, Kadam A, Kasivajjala V, Aswath A. Development of a deep learning algorithm for automatic diagnosis of diabetic retinopathy. Stud Health Technol Inform. 2017;245:559–63.

43.

Walton OB, Garoon RB, Weng CY, Gross J, Young AK, Camero KA, et al. Evaluation of Automated Teleretinal Screening Program for Diabetic Retinopathy. JAMA Ophthalmol. 2016;134:204–9.

44.

Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. 2018;125:1199–206.

45.

Abràmoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013;131:351–7.

46.

Patel TP, Aaberg MT, Paulus YM, Lieu P, Dedania VS, Qian CX, et al. Smartphone-based fundus photography for screening of plus-disease retinopathy of prematurity. Graefes Arch Clin Exp Ophthalmol Albrecht Von Graefes Arch Klin Exp Ophthalmol. 2019;257:2579–85.

47.

Patel TP, Kim TN, Yu G, Dedania VS, Lieu P, Qian CX, et al. Smartphone-based, rapid, wide-field fundus photography for diagnosis of pediatric retinal diseases. Transl Vis Sci Technol. 2019;8:29.

48.

Ramachandran N, Hong SC, Sime MJ, Wilson GA. Diabetic retinopathy screening using deep neural network. Clin Exp Ophthalmol. 2018;46:412–6.

49.

Xu K, Feng D, Mi H Deep. Convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Mol Basel Switz. 2017;22:2054.

50.

Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda S, et al. Automated diabetic retinopathy screening and monitoring using retinal fundus image analysis. J Diabetes Sci Technol. 2016;10:254–61.

51.

Ting DSW, Cheung CY-L, Lim G, Tan GSW, Quang ND, Gan A, 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;318:2211–23.

52.

Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200–6.

53.

Krause J, Gulshan V, Rahimy E, Karth P, Widner K, Corrado GS, et al. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology. 2018;125:1264–72.

54.

Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124:962–9.

55.

Beagley J, Guariguata L, Weil C, Motala AA. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract. 2014;103:150–60.

56.

Maamari RN, Keenan JD, Fletcher DA, Margolis TP. A mobile phone-based retinal camera for portable wide field imaging. Br J Ophthalmol. 2014;98:438–41.

57.

Kim TN, Myers F, Reber C, Loury PJ, Loumou P, Webster D, et al. A smartphone-based tool for rapid, portable, and automated wide-field retinal imaging. Transl Vis Sci Technol. 2018;7:21–21.

58.

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;98:786–806.

59.

Li P, Paulus Y, Davila J, Gosbee J, Margolis T, Fletcher D, et al. Usability testing of a smartphone-based retinal camera among first-time users in the primary care setting. BMJ Innov. 2019;5:120–6.

60.

British Diabetic Association. Retinal photographic screening for diabetic eye disease. A British Diabetic Association Report. London: British Diabetic Association; 1997.

61.

Scanlon PH. The English National Screening Programme for diabetic retinopathy 2003-2016. Acta Diabetol. 2017;54:515–25.

62.

Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye Lond Engl. 2018;32:1138–44.

63.

Russo A, Morescalchi F, Costagliola C, Delcassi L, Semeraro F. Comparison of smartphone ophthalmoscopy with slit-lamp biomicroscopy for grading diabetic retinopathy. Am J Ophthalmol. 2015;159:360–364.e1.

64.

Ryan ME, Rajalakshmi R, Prathiba V, Anjana RM, Ranjani H, Narayan KMV, et al. Comparison Among Methods of Retinopathy Assessment (CAMRA) study: smartphone, nonmydriatic, and mydriatic photography. Ophthalmology. 2015;122:2038–43.

65.

Vujosevic S, Benetti E, Massignan F, Pilotto E, Varano M, Cavarzeran F, et al. Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields. Am J Ophthalmol. 2009;148:111–8.

66.

Silva PS, Cavallerano JD, Haddad NMN, Kwak H, Dyer KH, Omar AF, et al. Peripheral lesions identified on ultrawide field imaging predict increased risk of diabetic retinopathy progression over 4 years. Ophthalmology. 2015;122:949–56.

67.

Sun JK, Aiello LP. The future of ultrawide field imaging for diabetic retinopathy: pondering the retinal periphery. JAMA Ophthalmol. 2016;134:247–8.

68.

Sim DA, Keane PA, Rajendram R, Karampelas M, Selvam S, Powner MB, et al. Patterns of peripheral retinal and central macula ischemia in diabetic retinopathy as evaluated by ultra-widefield fluorescein angiography. Am J Ophthalmol. 2014;158:144–153.e1.

69.

Aptel F, Denis P, Rouberol F, Thivolet C. Screening of diabetic retinopathy: effect of field number and mydriasis on sensitivity and specificity of digital fundus photography. Diabetes Metab. 2008;34:290–3.

70.

Murgatroyd H, Ellingford A, Cox A, Binnie M, Ellis JD, MacEwen CJ, et al. Effect of mydriasis and different field strategies on digital image screening of diabetic eye disease. Br J Ophthalmol. 2004;88:920–4.

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