Evaluating the accuracy of Artificial Intelligence (AI)-integrated, Smartphone-based screening for Diabetic Retinopathy: Systematic Review
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Published
Mar 5, 2025
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Abstract
Background: Diabetic retinopathy (DR is the most common microvascular complication of diabetes that can cause vision problems and blindness that poses a significant health risk and financial burden, increasing the needs to effectively screen and manage diabetic eye disease. The current method of screening for diabetic eye disease relies on human experts to analyze the results. Alternatively, recent advancements in artificial intelligence (AI) especially deep learning (DL) and retinal imaging using smartphones offer a promising solution for both patients and ophthalmologists, potentially improving patient compliance and making telemedicine more efficient for DR screening.
Purpose : To represent on accuracy of AI‑integrated process in smartphone-based DR screening and to compare the various study methods and settings used to achieve this accuracy.
Method: Literature search on current DR screening programs was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework on Google Scholar, Scopus, Web of Science, PubMed, Medline, and Embase with most recent search was updated on June 1st, 2024. Key information was extracted from the studies included author names, journal, year of publication, country, sensitivity, specificity, positive and negative predictive values (if available), study methods, and settings.
Result: The study identification process resulting in 9 selected studies. The performance metrics reported included intergrader/intramodality agreement, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The sensitivity of AI in detecting DR ranged from 77-100%, while specificity ranged from 61.4 - 95.5%. PPV and NPV were reported less frequently, with ranges of 48.1 - 92.92% and 91.3 - 99.46%, respectively. Intergrader agreement was within range ĸ= 0.45 – 0.91.
Conclusion: The studies reviewed in this paper collectively represents the potential of smartphone based integrated with AI in revolutionizing DR screening. The high sensitivity and specificity achieved by various AI algorithms, often exceeding the standards set by regulatory bodies like the FDA and ETDRS, highlight their accuracy in detecting DR and its severity levels. The accessibility and user-friendliness of smartphone-based retinal imaging further enhance the coverage of DR screening, particularly in underserved areas with limited resources and internet connectivity.
Keywords
artificial intelligence, smartphone, diabetic retinopathy, screening
Article Details
How to Cite
SAMARA, Shofia Medina; LAKSONO, Ariyoga Kun.
Evaluating the accuracy of Artificial Intelligence (AI)-integrated, Smartphone-based screening for Diabetic Retinopathy: Systematic Review.
International Journal of Retina, [S.l.], v. 8, n. 1, p. 72, mar. 2025.
ISSN 2614-8536.
Available at: <https://www.ijretina.com/index.php/ijretina/article/view/294>. Date accessed: 28 mar. 2025.
doi: https://doi.org/10.35479/ijretina.2025.vol008.iss001.294.
Section
Review
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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42. Tele-diabetology to screen for diabetes and associated complications in rural India: the Chunampet Rural Diabetes Prevention Project Model. Mohan V, Prathiba V, Pradeepa R. s.l. : J Diabetes SciTechnol., 2014, Vols. 8:256–61.
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47. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. Cuadros J, Bresnick G. s.l. : Diabetes Sci Technol., 2009, Vols. ;3(3):509-516.
48. Predictors of photographic quality with a handheld nonmydriatic fundus camera used for screening of vision-threatening diabetic retinopathy. Davila JR, Sengupta SS, Niziol LM, et al. s.l. : Ophthalmologica, 2017, Vols. 238:89-99.
49. Barriers to and enablers of diabetic retinopathy screening attendance: a systematic review of published and grey literature. Graham-Rowe E, Lorencatto F, Lawrenson JG, et al. s.l. : Diabet Med, 2018, Vols. 35:1308–19.
50. Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early treatment diabetic retinopathy study research group. s.l. : Ophthalmology, 1991, Vols. 98:766-85.
51. The validity of diabetic retinopathy screening using nonmydriatic fundus camera and optical coherence tomography in comparison to clinical examination. Sarah Aljefri, Fadwa Al Adel. Riyadh, Saudi Araia : Saudi J Ophthalmol, 2020, Vols. 34:266-72.
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53. Upaya Kesehatan di Indonesia : Tantangan dan Harapan. Darmawansyah. s.l. : Jurnal AKK,, 2013, Vols. Vol 2 No 2, Mei 2013.
54. Efficacy of smartphone based retinal photography by undergraduate students in screening and early diagnosing diabetic retinopathy. Jéssica Deponti Gobbi, João Pedro Romero Braga, Moises M. Lucena, Victor C. F. Bellanda, Miguel V. S. Frasson, Daniel Ferraz, et al. Brazil : International Journal of Retina and Vitreous, 2022, Vol. (2022) 8:35.
2. Diabetes in Asia: epidemiology, risk factors,. Chan JC, Malik V, Jia W, et al. s.l. : JAMA, 2009, Vols. 301:2129–2140.
3. Prospective evaluation of an artificial intelligence enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Heydon P, et al. s.l. : Br J Ophthalmol, 2021, Vols. 105:723–728.
4. Public Health England. Diabetic Eye Screening: Programme Overview. [Online] 2014. [Cited: June 1, 2024.] https://www.gov.uk/guidance/diabetic-eye-screening-programme-overview.
5. The English national screening programme for diabetic retinopathy 2003-2016. PH., Scanlon. s.l. : Acta Diabetol, 2017, Vols. 54:515–525.
6. Barriers for adherence to diabetic retinopathy screening among Saudi adults. Alwazae M, Al Adel F, Alhumud A, Almutairi A, Alhumidan A,. s.l. : Cureus, 2019, Vol. 11:e6454.
7. istics. Eppley SE, Mansberger SL, Ramanathan S, Lowry EA. Ophthalmology, Characteristics associated with adherence to annual dilated eye examinations among US patients with diagnosed diabetes. : s.n., 2019, Vols. 126:1492–1499.
8. Knowledge, attitude, and practice pattern towards diabetic retinopathy screening among general practitioners in primary health centres in Jakarta, the capital of Indonesia. Yeni Dwi Lestari, Gitalisa Andayani Adriono,corresponding author Rizka Ratmilia, Christy Magdalena, Ratna Sitompul. s.l. : BMC Prim Care , 2023, Vol. 2023; 24: 114.
9. Artificial intelligence for diabetic retinopathy. Sicong Li, Ruiwei Zhao, Haidong Zou. s.l. : Chinese Medical Journal, 2022, Vols. 135:253–260.
10. Transforming diabetes care through artificial intelligence: The future is here. Dankwa Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. s.l. : Popul Health Manag, 2019, Vols. 22:229 42.
11. Performance of an artificial intelligence automated system for diabetic eye screening in a large English population. Sarah Meredith, Mark van Grinsven, Jonne Engelberts. s.l. : Diabetic Medicine, 2023, Vol. 40:e15055. .
12. Use of offline artificial intelligence in a smartphone based fundus camera for community screening of diabetic retinopathy. Jain A, Krishnan R, Rogye A, Natarajan S. Mumbai : Indian J Ophthalmol, 2021, Vols. 2021;69:3150-4.
13. Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone. Sundaram Natarajan, et al. Mumbai : JAMA Ophthalmology, 2019, Vols. 2019;137(10):1182-1188.
14. Sensitivity and specificity of smartphone based retinal imaging for diabetic retinopathy: A comparative study. Sengupta S, Sindal MD, Baskaran P, Pan U, Venkatesh R. s.l. : Ophthalmol Retina, 2019, Vols. 019;3:146 53.
15. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Ramachandran Rajalakshmi, et al. s.l. : Springer Nature, 2018, Vols. 32:1138–1144.
16. Medios– An offline, smartphone based artificial intelligence algorithm for the diagnosis of diabetic retinopathy. Bhavana Sosale, et al. Mumbai : Wolters Kluwer, Indian Journal of Ophthalmology, 2020, Vol. Volume 68 Issue 2.
17. Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography. Tyson N. Kim, et al. Michigan, USA : Springer Nature, 2020, Vols. 35:334–342.
18. Simple, Mobile-based Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study. Sosale B, Aravind SR, Murthy H, et al. Bangalore : BMJ Open Diab Res Care, 2020, Vol. 8:e000892. doi:10.1136/bmjdrc.
19. Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera. Fernando Korn Malerbi, et al. Sao Paolo : Journal of Diabetes Science and Technology, 2022, Vols. Vol. 16(3) 716–.
20. Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study. John J. Wroblewski, MD, Ermilo Sanchez-Buenfil, MD, Miguel Inciarte, MD, Jay Berdia, MD, Lewis Blake, PhD, Simon Wroblewski, BS, et al. s.l. : Journal of Diabetes Science and Technology, 2023.
21. Proposed International Clinical Diabetic Retinopathy and diabetic macular edema disease severity scales. Wilkinson CP, Ferris FL III, Klein RE, Lee PP, Agardh CD, Davis M Global Diabetic Retinopathy Project Group, et al. s.l. : Ophthalmology., 2003, Vols. 110:1677–82.
22. Guidance on standard feature based grading forms to be used in the NHS diabetic eye screening programme. . Core NDESP team. 2013, Vol. Diabetic Eye Screening Feature Based Grading.
23. NHS Diabetic Eye Screening Programme grading definitions for referable disease. Public Health England. January 2017.
24. Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. Early Treatment Diabetic Retinopathy Study Research Group. 1991;98:786–806., s.l. : Ophthalmology, 1991, Vol. ETDRS report number 10.
25. Classification of diabetic retinopathy: Past, present and future. Zhengwei Yang, Tien-En Tan, Yan Shao, Tien Yin Wong, and Xiaorong Li. s.l. : Front Endocrinol, 2022, Vol. 13: 1079217.
26. Remidio. Medios DR. [Online] [Cited: June 1st, 2024.] https://www.remidio.com/products/medios-dr.
27. EYENUK. EyeArt® AI Eye Screening System. [Online] [Cited: June 1st, 2024.] https://www.eyenuk.com/en/products/eyeart/.
28. Remidio. Hand Held Fundus Camera | FOP NM-10. [Online] [Cited: June 1, 2024.] https://www.remidio.com/products/fop.
29. A mobile phone-based retinal camera for portable wide field imaging. Maamari RN, Keenan JD, Fletcher DA, Margolis TP. s.l. : Br J Ophthalmol, 2014, Vols. 2014;98:438–41.
30. F., Chollet. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. [Online] 2017. [Cited: June 18, 2024.] https://openaccess.thecvf.com/content_cvpr_2017/papers/Chollet_Xception_Deep_Learning_CVPR_2017_paper.pdf..
31. Phelcom. Eyer. [Online] [Cited: June 1, 2024.] https://phelcom.com/products/eyer/.
32. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. Abràmoff MD, Lavin PT, Birch M, et al. s.l. : NPJ Digit Med, 2018, Vol. 2018;1:39.
33. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. s.l. : JAMA, 2016, Vols. 316:2402–10.
34. Screening for diabetic retinopathy: new perspectives and challenges. Vujosevic S, Aldington SJ, Silva P, et al. s.l. : Lancet Diabetes Endocrinol., 2020, Vols. 8:337-347.
35. Validation of smartphone based retinal photography for diabetic retinopathy screening. Rajalakshmi R, Arulmalar S, Usha M, Prathiba V, Kareemuddin KS, Anjana RM, et al. s.l. : PLoS ONE., 2015, Vol. 0:e0138285.
36. Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease. Toy BC, Myung DJ, He L, Pan CK, Chang RT, Polkinhorne A, et al. s.l. : Retin Philos Pa., 2016, Vols. 36:1000–8.
37. Comparison of smartphone ophthalmoscopy with slit-lamp biomicroscopy for grading diabetic retinopathy. Russo A, Morescalchi F, Costagliola C, Delcassi L, Semeraro F. s.l. : Am J Ophthalmol, 2015, Vols. 159:360–364.e1.
38. Comparison Among Methods of Retinopathy Assessment (CAMRA) study: smartphone, nonmydriatic, and mydriatic photography. Ryan ME, Rajalakshmi R, Prathiba V, Anjana RM, Ranjani H, Narayan KMV, et al. s.l. : Ophthalmology., 2015, Vols. 122:2038–43.
39. Accuracy of diabetic retinopathy screening by trained non-physician graders using non-mydriatic fundus camera. Bhargava M, Cheung CY, Sabanayagam C, Kawasaki R, Harper CA, Lamoureux EL, et al. s.l. : Singapore Med J., 2012, Vols. 53:715–9.
40. The sensitivity and specificity of non-mydriatic digital stereoscopic retinal imaging in detecting diabetic retinopathy. Raman R, Padmaja RK, Sharma T. s.l. : Diabetes Care, 2007, Vol. 30:1.
41. The cost-utility of telemedicine to screen for diabetic retinopathy in India. Rachapelle S, Legood R, Alavi Y, Lindfield R, Sharma T, Kuper H, et al. s.l. : Ophthalmology., 2013, Vols. 120:566–73.
42. Tele-diabetology to screen for diabetes and associated complications in rural India: the Chunampet Rural Diabetes Prevention Project Model. Mohan V, Prathiba V, Pradeepa R. s.l. : J Diabetes SciTechnol., 2014, Vols. 8:256–61.
43. Prevalence of diabetic retinopathy and blindness in indonesian adults with type 2 diabetes. Sasongko MB, Widyaputri F, Agni AN, Wardhana FS, Kotha S, Gupta P, et al. s.l. : Am J Ophthalmol, 2017, Vols. 2017;181:79–87.
44. Performance of a deeplearning algorithm vs manual grading for detecting diabetic retinopathy in India. Gulshan V, Rajan RP, Widner K, et al. s.l. : JAMA Ophthalmol, 2019, Vols. 137(9):987-993.
45. Automated diabetic retinopathy screening and monitoring using retinal fundus image analysis. Bhaskaranand M, Ramachandra C, Bhat S, et al. 10(2):254-261, s.l. : J Diabetes Sci Technol., Vol. 2016.
46. Estimating prevalence from the results of a screening test. Rogan WJ, Gladen B. s.l. : Am J Epidemiol., 1978, Vols. 107(1):71-76.
47. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. Cuadros J, Bresnick G. s.l. : Diabetes Sci Technol., 2009, Vols. ;3(3):509-516.
48. Predictors of photographic quality with a handheld nonmydriatic fundus camera used for screening of vision-threatening diabetic retinopathy. Davila JR, Sengupta SS, Niziol LM, et al. s.l. : Ophthalmologica, 2017, Vols. 238:89-99.
49. Barriers to and enablers of diabetic retinopathy screening attendance: a systematic review of published and grey literature. Graham-Rowe E, Lorencatto F, Lawrenson JG, et al. s.l. : Diabet Med, 2018, Vols. 35:1308–19.
50. Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early treatment diabetic retinopathy study research group. s.l. : Ophthalmology, 1991, Vols. 98:766-85.
51. The validity of diabetic retinopathy screening using nonmydriatic fundus camera and optical coherence tomography in comparison to clinical examination. Sarah Aljefri, Fadwa Al Adel. Riyadh, Saudi Araia : Saudi J Ophthalmol, 2020, Vols. 34:266-72.
52. Badan Pusat Statistik. Jumlah Penduduk Pertengahan Tahun (Ribu Jiwa), 2022-2024. [Online] 2024. [Cited: June 1, 2024.] https://www.bps.go.id/id/statistics-table/2/MTk3NSMy/jumlah-penduduk-pertengahan-tahun--ribu-jiwa-.html.
53. Upaya Kesehatan di Indonesia : Tantangan dan Harapan. Darmawansyah. s.l. : Jurnal AKK,, 2013, Vols. Vol 2 No 2, Mei 2013.
54. Efficacy of smartphone based retinal photography by undergraduate students in screening and early diagnosing diabetic retinopathy. Jéssica Deponti Gobbi, João Pedro Romero Braga, Moises M. Lucena, Victor C. F. Bellanda, Miguel V. S. Frasson, Daniel Ferraz, et al. Brazil : International Journal of Retina and Vitreous, 2022, Vol. (2022) 8:35.