Objective: To validate an algorithm previously developed by the Healthcare Technology Innovation Centre, IIT Madras, India for screening of diabetic retinopathy (DR), in fundus images of diabetic patients from telecamps to examine the screening performance for DR. Design: Photographs of patients with diabetes were examined using the automated algorithm for the detection of DR Setting: Mobile Teleophthalmology camps were conducted in two districts in Tamil Nadu, India from Jan 2015 to May 2017. Participants: 939 eyes of 472 diabetic patients were examined at Mobile Teleophthalmology camps in Thiruvallur and Kanchipuram districts, Tamil Nadu, India,. Fundus images were obtained (40-45-degree posterior pole in each eye) for all patients using a nonmydriatic fundus camera by the fundus photographer. Main Outcome Measures: Fundus images were evaluated for presence or absence of DR using a computer-assisted algorithm, by an ophthalmologist at a tertiary eye care centre (reference standard) and by a fundus photographer. Results: The algorithm demonstrated 85% sensitivity and 80% specificity in detecting DR compared to ophthalmologist. The area under the receiver operating characteristic curve was 0.69 (95%CI=0.65 to 0.73). The algorithm identified 100% of vision-threatening retinopathy just like the ophthalmologist. When compared to the photographer, the algorithm demonstrated 81% sensitivity and 78% specificity. The sensitivity of the photographer to detect DR was found to be 86% and specificity was 99% in detecting DR compared to ophthalmologist. Conclusions: The algorithm can detect the presence or absence of DR in diabetic patients. All findings of vision-threatening retinopathy could be detected with reasonable accuracy and will help to reduce the workload for human graders in remote areas.
Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice. 2010;87(1):4–14.
2.
Hazin R, Barazi MK, Summerfield M. Challenges to establishing nationwide diabetic retinopathy screening programs. Current Opinion in Ophthalmology. 2011;22(3):174–9.
3.
Hazin R, Colyer M, Lum F, Barazi MK. Revisiting Diabetes 2000: Challenges in Establishing Nationwide Diabetic Retinopathy Prevention Programs. American Journal of Ophthalmology. 2011;152(5):723–9.
4.
Sheppler CR, Lambert WE, Gardiner SK, Becker TM, Mansberger SL. Predicting Adherence to Diabetic Eye Examinations. Ophthalmology. 2014;121(6):1212–9.
5.
Murthy G, Gupta S, Bachani D, Tewari H, John N. Human resources and infrastructure for eye care in India: current status. Natl Med J India. 2004;(3):128–34.
6.
Das T, Pappuru R. Telemedicine in diabetic retinopathy: Access to rural India. Indian Journal of Ophthalmology. 2016;64(1):84.
7.
John S, Srinivasan S, Sundaram N. Validation of Computer-Aided Diagnosis of Diabetic Retinopathy from Retinal Photographs of Diabetic Patients from Telecamps. Telehealth and Medicine Today. 2021;
8.
Resnikoff S, Felch W, Gauthier TM, Spivey B. The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners. British Journal of Ophthalmology. 2012;96(6):783–7.
9.
Das T, Raman R, Ramasamy K, Rani P. Telemedicine in diabetic retinopathy: Current status and future directions. Middle East African Journal of Ophthalmology. 2015;22(2):174.
10.
Murthy GVS, Gilbert C, Babu Rg, Gudlavalleti AV, Anchala R, Shukla R, et al. Eye care infrastructure and human resources for managing diabetic retinopathy in India: The India 11-city 9-state study. Indian Journal of Endocrinology and Metabolism. 2016;20(7):3.
11.
Rajesh B, Hussain R, Giridhar A, Gopalakrishnan M, Sadasivan S, James J, et al. Knowledge and awareness about diabetes mellitus and diabetic retinopathy in suburban population of a South Indian state and its practice among the patients with diabetes mellitus: A population-based study. Indian Journal of Ophthalmology. 2016;64(4):272.
12.
Dandona L, Dandona R, Naduvilath TJ, McCarty CA, Rao GN. Population based assessment of diabetic retinopathy in an urban population in southern India. British Journal of Ophthalmology. 1999;83(8):937–40.
13.
Vashist P, Singh S, Gupta N, Saxena R. Role of early screening for diabetic retinopathy in patients with diabetes mellitus: An overview. Indian Journal of Community Medicine. 2011;36(4):247.
14.
Agarwal S, Raman R, Kumari R, Deshmukh H, Paul P, Gnanamoorthy P. Diabetic retinopathy in type II diabetics detected by targeted screening versus newly diagnosed in general practice. Ann Acad Med Singap. 2006;(8):531–5.
15.
Yogesan K, Goldschmidt L;, Raman R, Gupta A, Sharma T. Telescreening for diabetic retinopathy. Health Sciences Division. 2012;1006–11.
16.
Kolomeyer AM, Szirth BC, Shahid KS, Pelaez G, Nayak NV, Khouri AS. Software-Assisted Analysis During Ocular Health Screening. Telemedicine and e-Health. 2013;19(1):2–6.
17.
John S, Sengupta S, Reddy SJ, Prabhu P, Kirubanandan K, Badrinath SS. The Sankara Nethralaya Mobile Teleophthalmology Model for Comprehensive Eye Care Delivery in Rural India. Telemedicine and e-Health. 2012;18(5):382–7.
18.
Agarwal S, Raman R, Paul PG, Rani PK, Uthra S, Gayathree R, et al. Sankara Nethralaya—Diabetic Retinopathy Epidemiology and Molecular Genetic Study (SN—DREAMS 1): Study Design and Research Methodology. Ophthalmic Epidemiology. 2005;12(2):143–53.
19.
Mohan V, Deepa M, Pradeepa R, Prathiba V, Datta M, Ravikumar S, et al. Prevention of Diabetes in Rural India with a Telemedicine Intervention. Journal of Diabetes Science and Technology. 2012;6(6):1355–64.
20.
Zimmer-Galler IE, Kimura AE, Gupta S. Diabetic retinopathy screening and the use of telemedicine. Current Opinion in Ophthalmology. 2015;26(3):167–72.
21.
Klein R, Klein BEK, Moss SE, Davis MD, DeMets DL. The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Ophthalmology. 1987;94(7):747–53.
22.
Hudson SM, Contreras R, Kanter MH, Munz SJ, Fong DS. Centralized Reading Center Improves Quality in a Real-World Setting. Ophthalmic Surgery, Lasers and Imaging Retina. 2015;46(6):624–9.
23.
John S, Srinivasan S, Raman R, Ram K, Sivaprakasam M. Validation of a customized algorithm for the detection of diabetic retinopathy from single-field fundus photographs in a tertiary eye care hospital. Stud Health Technol Inform. 2019;1504–5.
24.
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(22):2402.
25.
Gulshan V, Rajan RP, Widner K, Wu D, Wubbels P, Rhodes T, et al. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmology. 2019;137(9):987.
26.
Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S. Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone. JAMA Ophthalmology. 2019;137(10):1182.
27.
Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technology Assessment. 2016;20(92):1–72.
28.
Rathi S, Tsui E, Mehta N, Zahid S, Schuman JS. The Current State of Teleophthalmology in the United States. Ophthalmology. 2017;124(12):1729–34.
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