Validation of Computer-Aided Diagnosis of Diabetic Retinopathy from Retinal Photographs of Diabetic Patients from Telecamps
DOI:
https://doi.org/10.30953/tmt.v6.300Keywords:
algorithm, screening, retinal photographs, diabetes, diabetic retinopathyAbstract
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.
Downloads
References
Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010; 87(1): 4–14. https://doi.org/10.1016/j.diabres.2009.10.007
Hazin R, Barazi MK, Summerfield M. Challenges to establishing nationwide diabetic retinopathy screening programs. Curr Opin Ophthalmol 2011; 22(3): 174–79. https://doi.org/10.1097/ICU.0b013e32834595e8
Hazin R, Colyer M, Lum F, Barazi MK. Revisiting Diabetes 2000: challenges in establishing nationwide diabetic retinopathy prevention programs. Am J Ophthalmol 2011; 152(5): 723–29. https://doi.org/10.1016/j.ajo.2011.06.022
Sheppler CR, Lambert WE, Gardiner SK, Becker TM, Mansberger SL. Predicting adherence to diabetic eye examinations: development of the compliance with Annual Diabetic Eye Exams Survey. Ophthalmology 2014; 121(6): 1212–19. https://doi.org/10.1016/j.ophtha.2013.12.016
Murthy GV, Gupta SK, Bachani D, Tewari HK, John N. Human resources and infrastructure for eye care in India: current status. Natl Med J India 2004; 17(3): 128–34.
Das T, Pappuru RR. Telemedicine in diabetic retinopathy: access to rural India. Indian J Ophthalmol 2016; 64(1): 84–6. https://doi.org/10.4103/0301-4738.178151
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. Br J Ophthalmol 2012; 96(6): 783–7. https://doi.org/10.1136/bjophthalmol-2011-301378
Das T, Raman R, Ramasamy K, Rani PK. Telemedicine in diabetic retinopathy: current status and future directions. Middle East Afr J Ophthalmol 2015; 22(2): 174–8. https://doi.org/10.4103/0974-9233.154391
Gilbert CE, Babu RG, Gudlavalleti AS, Anchala R, Shukla R, Ballabh PH, et al. Eye care infrastructure and human resources for managing diabetic retinopathy in India: the India 11-city 9-state study. Indian J Endocrinol Metab 2016; 20 (Suppl 1): S3–10. https://doi.org/10.4103/2230-8210.179768
Hussain R, Rajesh B, 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 J Ophthalmol 2016; 64(4): 272–6. https://doi.org/10.4103/0301-4738.182937
Dandona L, Dandona R, Naduvilath TJ, McCarty CA, Rao GN. Population based assessment of diabetic retinopathy in an urban population in southern India. Br J Ophthalmol 1999; 83(8): 937–40. https://doi.org/10.1136/bjo.83.8.937
Vashist P, Singh S, Gupta N, Saxena R. Role of early screening for diabetic retinopathy in patients with diabetes mellitus: an overview. Indian J Commun Med 2011; 36(4): 247–52. https://doi.org/10.4103/0970-0218.91324
Agarwal S, Raman R, Kumari RP, Deshmukh H, Paul PG, Gnanamoorthy P, et al. Diabetic retinopathy in type II diabetics detected by targeted screening versus newly diagnosed in general practice. Ann Acad Med Singap 2006; 35(8): 531–5.
Yogesan K, Goldschmidt L, Cuadros, J. (Eds.). Digital teleretinal screening. Teleophthalmology in practice. 2nd ed. Berlin: Springer-Verlag; 2012.
Raman R, Gupta A, Sharma T. Telescreening for diabetic retinopathy. In Ryan SJ, ed. Retina, Elsevier: Health Sciences Division, vol 2, 5th ed. 2012; pp. 1006–11.
Kolomeyer AM, Szirth BC, Shahid KS, Pelaez G, Nayak NV, Khouri AS. Software-assisted analysis during ocular health screening. Telemed J E Health 2013; 19(1): 2–6. https://doi.org/10.1089/tmj.2012.0070
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. Telemed J E Health 2012; 18(5): 382–7. https://doi.org/10.1089/tmj.2011.0190
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 Epidemiol 2005; 12(2): 143–53. https://doi.org/10.1080/09286580590932734
Mohan V, Deepa M, Pradeepa R, Prathiba V, Datta M, Sethuraman R, et al. Prevention of diabetes in rural India with a telemedicine intervention. J Diabetes Sci Technol 2012; 6(6): 1355–64. https://doi.org/10.1177/193229681200600614
Zimmer-Galler IE, Kimura AE, Gupta S. Diabetic retinopathy screening and the use of telemedicine. Curr Opin Ophthalmol 2015; 26(3): 167–72. https://doi.org/10.1097/ICU.0000000000000142
Klein R, Klein BE, Moss SE, Davis MD, DeMets DL. The Wisconsin Epidemiologic study of diabetic retinopathy. VI. Retinal photocoagulation. Ophthalmology 1987; 94(7): 747–53. https://doi.org/10.1016/s0161-6420(87)33525-0
Hudson SM, Contreras R, Kanter MH, Munz SJ, Fong DS. Centralized reading center improves quality in a real-world setting. Ophthalmic Surg Lasers Imaging Retina 2015; 46(6): 624–9. https://doi.org/10.3928/23258160-20150610-05
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; 264: 1504–5. https://doi.org/10.3233/SHTI190506
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): 2402–10. https://doi.org/10.1001/jama.2016.17216
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 Ophthalmol 2019; 137(9): 987–93. https://doi.org/10.1001/jamaophthalmol.2019.2004
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 Ophthalmol 2019; 137(10): 1182–8. https://doi.org/10.1001/jamaophthalmol.2019.2923
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 Technol Assess 2016; 20(92): 1–72. https://doi.org/10.3310/hta20920
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. https://doi.org/10.1016/j.ophtha.2017.05.026
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Sheila John, Sangeetha Srinivasan, Natarajan Sundaram
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to Telehealth and Medicine Today (THMT).
THMT is published under a Creative Commons Attribution-NonCommercial 4.0 International License.
