The Validity of Artificial Intelligence (AI) for Diabetic Retinopathy Screening in Asia: A Systematic Review
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Published
Sep 27, 2023
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Abstract
Introduction: One of the major diabetes mellitus (DM) complications is diabetic retinopathy (DR) which may lead to visual impairment. Unfortunately, estimated that more than one-third of diabetic patients in Asia have never undergone an ophthalmological examination. Screening for DR is an essential aspect of DM management. With advanced technological development, the use of artificial intelligence (AI) for DR screening have been increasing in popularity, especially since Deep Learning (DL) technologies in AI can process a large amount of data automatically without human intervention. Therefore, this review aims to evaluate the validity of AI as DR screening tool in Asian countries compared to ophthalmologist DR grading as a reference standard.
Methods: A comprehensive search was conducted using relevant search terms from the electronic database (Pubmed, Cochrane, and EMBASE). Included studies were selected based on predefined inclusion criteria.
Result: Twelve studies reported a good sensitivity of AI for detecting Referable DR (RDR). The lowest sensitivity was 79.2 %, and the highest was 100 %. For specificity, eleven studies reported good specificity, with only one study reporting a low value, with only 68.8% for detecting RDR.
Conclusion: The AI can detect DR by screening large amounts of retina images quickly without the assistance of a trained retina specialist. In this review of local Asia population settings, AI has a good result for detecting RDR in almost all studies.
Keywords
artificial intelligence, deep learning, diabetic retinopathy
Article Details
How to Cite
DWI LESTARI, Yeni; JAYANEGARA, Andi Putra.
The Validity of Artificial Intelligence (AI) for Diabetic Retinopathy Screening in Asia: A Systematic Review.
International Journal of Retina, [S.l.], v. 6, n. 2, p. 104, sep. 2023.
ISSN 2614-8536.
Available at: <https://ijretina.com/index.php/ijretina/article/view/241>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.35479/ijretina.2023.vol006.iss002.241.
Section
Article Review
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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40. Scanlon PH, Foy C, Malhotra R, et al. The influence of age, duration of diabetes, cataract, and pupil size on image quality in digital photographic retinal screening. Am J Ophthalmol 2006;141:603.
2. Shah AR, Gardner TW. Diabetic retinopathy: research to clinical practice. Clin Diabetes Endocrinol. 2017;3:9.
3. Sasongko MB, Wardhana FS, Febryanto GA, Agni AN, Supanji S, et al. The estimated healthcare cost of diabetic retinopathy in Indonesia and its projection for 2025. Br J Ophthalmol. 2020 Apr;104(4):487-492.
4. Ruta LM, Magliano DJ, Lemesurier R, et al. Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries. Diabet Med. 2013;30:387–398.
5. Pareja-Ríos A, Bonaque-González S, Serrano-García M, Cabrera-López F, Abreu-Reyes P, Marrero-Saavedra MD. Tele-ophthalmology for diabetic retinopathy screening: 8 years of experience. Arch Soc Esp Oftalmol. 2017 Feb;92(2):63–70.
6. Solanki K, Bhaskaranand M, Bhat S, Ramachandra C, Cuadros J, Nittala MG, et al. Automated diabetic retinopathy screening:large-scale study on consecutive patient visits in a primary care setting. EASD 2016.
7. Burgess PI, MacCormick IJ, Harding SP, et al. Epidemiology of diabetic retinopathy and maculopathy in Africa: a systematic review. Diabet Med 2013; 30: 399–412.
8. Bascaran C, Mwangi N, D’Esposito F, Gordon I, Ulloa JAL, Mdala S, et al. Effectiveness of task-shifting for the detection of diabetic retinopathy in low- and middle-income countries: a rapid review protocol. Syst Rev. 2021 Jan 4;10(1):4.
9. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020 Mar 25;368:m689.
10. Abràmoff M, Lou Y, Erginay A, Clarida W, Amelon R, Folk J et al. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Investigative Opthalmology & Visual Science. 2016;57(13):5200.
11. Ruamviboonsuk P, Cheung CY, Zhang X, Raman R, Park SJ, Ting DSW. Artificial Intelligence in Ophthalmology: Evolutions in Asia. Asia Pac J Ophthalmol (Phila). 2020 Apr;9(2):78–84.
12. Lu L, Ren P, Lu Q, Zhou E, Yu W, Huang J, et al. Analyzing fundus images to detect diabetic retinopathy (DR) using deep learning system in the Yangtze River delta region of China. Ann Transl Med. 2021 Feb;9(3):226.
13. He J, Cao T, Xu F, Wang S, Tao H, Wu T, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye (Lond). 2020 Mar;34(3):572-576.
14. Zhang Y, Shi J, Peng Y, Zhao Z, Zheng Q, Wang Z, et al. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study. BMJ Open Diabetes Res Care. 2020 Oct;8(1):e001596.
15. Ming S, Xie K, Lei X, Yang Y, Zhao Z, Li S, et al. Evaluation of a novel artificial intelligence-based screening system for diabetic retinopathy in community of China: a real-world study. Int Ophthalmol. 2021 Apr;41(4):1291-1299.
16. Rajalakshmi, R., Subashini, R., Anjana, R.M. et al. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32, 1138–1144 (2018).
17. 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 Oct 1;137(10):1182-1188.
18. 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 Sep 1;137(9):987-993.
19. Sosale B, Sosale AR, Murthy H, Sengupta S, Naveenam M. Medios– An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy. Indian J Ophthalmol. 2020 Feb;68(2):391–5.
20. Shah P, Mishra DK, Shanmugam MP, Doshi B, Jayaraj H, Ramanjulu R. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening. Indian J Ophthalmol. 2020 Feb;68(2):398-405.
21. Sosale B, Aravind SR, Murthy H, Narayana S, Sharma U, Gowda SGV, et al. Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study. BMJ Open Diabetes Res Care. 2020 Jan;8(1).
22. Ting DSW, Cheung CY, 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 Dec 12;318(22):2211-2223.
23. Hsieh Y-T, Chuang L-M, Jiang Y-D, Chang T-J, Yang C-M, Yang C-H, et al. Application of deep learning image assessment software VeriSeeTM for diabetic retinopathy screening. Journal of the Formosan Medical Association. 2021 Jan 1;120(1, Part 1):165–71.
24. Huemer J, Wagner SK, Sim DA. The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence. Clin Ophthalmol. 2020 Jul 20;14:2021-2035.
25. Cheung CY, Tang F, Ting DSW, Tan GSW, Wong TY. Artificial Intelligence in Diabetic Eye Disease Screening. Asia Pac J Ophthalmol (Phila). 2019.
26. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–9.
27. 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(8):1264–72.
28. Van Der Heijden AA, Abramoff MD, Verbraak F, van Hecke MV, Liem A, Nijpels G. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol. 2018;96(1):63–8.
29. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of anautonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1(1):39.
30. Wong TY, Bressler NM. Artificial Intelligence with Deep Learning Technology Looks Into Diabetic Retinopathy Screening. JAMA. 2016 Dec 13;316(22):2366-2367.
31. Paul Y, Hickok E, Sinha A, Tiwari U. Artificial Intelligence in the Healthcare Industry in India. The Centre for Internet and Society. 2018.
32. Kong X, Ai B, Kong Y, Su L, Ning Y, Howard N, et al. Artificial intelligence: a key to relieve China’s insufficient and unequally-distributed medical resources. Am J Transl Res. 2019 May 15;11(5):2632-2640.
33. Gao S, Jin X, Zhao Z et al. Validation and application of an artificial intelligence robot assisted diagnosis system for diabetic retinopathy. Chin J Exp Ophthalmol 2019;37(8):669–673.
34. 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.
35. Micheletti M, Hendrick AM, Khan FN, Ziemer D, Pasquel FJ. Current and next generation portable screening devices for diabetic retinopathy. J Diabetes Sci Technol. 2016;10(2):295–300.
36. Prathiba V, Rajalakshmi R, Arulmala S, Usha M, Subhashini R, Gilbert, et al. Accuracy of the smartphone-based nonmydriatic retinal camera in the detection of sight-threatening diabetic retinopathy, Indian Journal of Ophthalmology: February 2020.
37. Karakaya M, Hacisoftaoglu RE. Comparison of smartphone-based retinal imaging systems for diabetic retinopathy detection using deep learning. BMC Bioinformatics. 2020 Jul 6;21(Suppl 4):259.
38. London AJ. Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. Hastings Cent Rep. 2019 Jan;49(1):15-21.
39. Poon AIF, Sung JJY. Opening the black box of AI-Medicine. J Gastroenterol Hepatol. 2021 Mar;36(3):581-584.
40. Scanlon PH, Foy C, Malhotra R, et al. The influence of age, duration of diabetes, cataract, and pupil size on image quality in digital photographic retinal screening. Am J Ophthalmol 2006;141:603.