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Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening
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AIM: To evaluate the sensitivity and specificity of "fundus on phone' (FOP) camera, a smartphone based retinal imaging system, as a screening tool for diabetic retinopathy (DR) detection and DR severity in comparison with 7-standard field digital retinal photography. DESIGN: Single-site, prospective, comparative, instrument validation study. METHODS: 301 patients (602 eyes) with type 2 diabetes underwent standard seven-field digital fundus photography with both Carl Zeiss fundus camera and indigenous FOP at a tertiary care diabetes centre in South India. Grading of DR was performed by two independent retina specialists using modified Early Treatment of Diabetic Retinopathy Study grading system. Sight threatening DR (STDR) was defined by the presence of proliferative DR(PDR) or diabetic macular edema. The sensitivity, specificity and image quality were assessed. RESULTS: The mean age of the participants was 53.5 ±9.6 years and mean duration of diabetes 12.5±7.3 years. The Zeiss camera showed that 43.9% had non-proliferative DR(NPDR) and 15.3% had PDR while the FOP camera showed that 40.2% had NPDR and 15.3% had PDR. The sensitivity and specificity for detecting any DR by FOP was 92.7% (95%CI 87.8-96.1) and 98.4% (95%CI 94.3-99.8) respectively and the kappa (ĸ) agreement was 0.90 (95%CI-0.85-0.95 p<0.001) while for STDR, the sensitivity was 87.9% (95%CI 83.2-92.9), specificity 94.9% (95%CI 89.7-98.2) and ĸ agreement was 0.80 (95%CI 0.71-0.89 p<0.001), compared to conventional photography. CONCLUSION: Retinal photography using FOP camera is effective for screening and diagnosis of DR and STDR with high sensitivity and specificity and has substantial agreement with conventional retinal photography.
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“Remote grading of retinal images is widely utilized, with 18 out of 30 studies relying on this approach [10, 15, 16, 18, 21–27, 29–35, 37]”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [16]
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“Image acquisition was often performed by trained non-physician staff (technicians, optometrists, nurses, paramedics), while final grading was typically carried out by ophthalmologists or retinal specialists (22/30 studies) [8–13, 15, 16, 18, 21, 22, 24, 26, 27, 30–37]”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [16]
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“Studies using AI software, such as the Iowa Diabetic Retinopathy detection software [15] and EyeArt [21] show high sensitivity up to 95,8% (95% CI 92,9–98,7) and diagnostic accuracy [16].”
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“The predominant method for managing these remotely graded images is ‘store and forward’ (14 out of 30 studies) [15, 16, 18, 22–24, 26–30, 33, 35, 37], with seven studies adopting the ‘real-time’ processing method [8, 10, 11, 25, 31, 32, 34]”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [16]
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“A study conducted in India using the “Fundus on Phone” system also demonstrated lower costs compared to conventional fundus cameras [16].”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [16]
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Cites
Lang, G. E., Vaiser, A., Snyder, W. B., & Lynn, J. R. (2007) · Developments in ophthalmology · doi:10.1159/isbn.978-3-318-01439-6 · Cited by 5,607
Standards of medical care in diabetes—2013
American Diabetes Association (2013) · Diabetes Care · doi:10.2337/dc13-s011 · Cited by 4,369
Photocoagulation for Diabetic Macular Edema
(1985) · Archives of Ophthalmology · doi:10.1001/archopht.1985.01050120030015 · Cited by 2,541
* (1991) · Ophthalmology · doi:10.1016/s0161-6420(13)38012-9 · Cited by 2,086
on behalf of the ICMR–INDIAB Collaborative Study Group, Anjana, R. M., Pradeepa, R., Deepa, M., Datta, M., Sudha, V., Ranjit Unnikrishnan, I., Bhansali, A., Joshi, S., Joshi, P. P., et al. (2011) · Diabetologia · doi:10.1007/s00125-011-2291-5 · Cited by 924
Self-citationFong, D. S., Aiello, L. P., Ferris, F. L., & Klein, R. (2004) · Diabetes Care · doi:10.2337/diacare.27.10.2540 · Cited by 694
Rema, M., Premkumar, S., Anitha, B., Deepa, R., Pradeepa, R., & Mohan, V. (2005) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.05-0019 · Cited by 523
Self-citationPrevalence of Diabetic Retinopathy in Various Ethnic Groups: A Worldwide Perspective
Sivaprasad, S., Gupta, B., Crosby‐Nwaobi, R., & Evans, J. R. (2012) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2012.01.004 · Cited by 396
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Vujosevic, S., Aldington, S. J., Silva, P., Hernández, C., Scanlon, P., Peto, T., & Simó, R. (2020) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(19)30411-5 · Cited by 671
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
Li, J.-P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., et al. (2021) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2020.100900 · Cited by 626
Rajalakshmi, R., Subashini, R., Anjana, R. M., & Mohan, V. (2018) · Eye · doi:10.1038/s41433-018-0064-9 · Cited by 462
Self-citationDiabetes mellitus and its complications in India
Ranjit Unnikrishnan, I., Anjana, R. M., & Mohan, V. (2016) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2016.53 · Cited by 408
Self-citationNatarajan, S., Jain, A., Krishnan, R., Rogye, A., & Sivaprasad, S. (2019) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2019.2923 · Cited by 230
Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application
Bellemo, V., Lim, G., Rim, T. H., Tan, G. S. W., Cheung, C. Y.-L., Sadda, S. R., He, M., Tufail, A., Lee, M. L., Hsu, W., et al. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1189-3 · Cited by 184
Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy
Raman, R., Srinivasan, S., Virmani, S., Sivaprasad, S., Rao, C., & Rajalakshmi, R. (2018) · Eye · doi:10.1038/s41433-018-0269-y · Cited by 163
Self-citationTreatment of diabetic retinopathy: Recent advances and unresolved challenges
Stewart, M. W. (2016) · World Journal of Diabetes · doi:10.4239/wjd.v7.i16.333 · Cited by 166
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Galiero, R., Pafundi, P. C., Nevola, R., Rinaldi, L., Acierno, C., Caturano, A., Salvatore, T., Adinolfi, L. E., Costagliola, C., & Sasso, F. C. (2020) · Journal of Diabetes Research · doi:10.1155/2020/9036847 · Cited by 156
Hacisoftaoglu, R. E., Karakaya, M., & Sallam, A. B. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.04.009 · Cited by 151
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Review of retinal cameras for global coverage of diabetic retinopathy screening
Rajalakshmi, R., Prathiba, V., Arulmalar, S., & Usha, M. (2020) · Eye · doi:10.1038/s41433-020-01262-7 · Cited by 104
Self-citationLim, G., Bellemo, V., Xie, Y., Lee, X. Q., Yip, M. Y. T., & Ting, D. S. W. (2020) · Eye and Vision · doi:10.1186/s40662-020-00182-7 · Cited by 96
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Kim, T. N., Myers, F. B., Reber, C. D., Loury, P., Loumou, P., Webster, D., Echanique, C., Li, P., Davila, J. R., Maamari, R. N., et al. (2018) · Translational Vision Science & Technology · doi:10.1167/tvst.7.5.21 · Cited by 94
Holmberg, O., Köhler, N., Martins, T. G. D. S., Siedlecki, J., Herold, T., Keidel, L. F., Asani, B., Schiefelbein, J. B., Priglinger, S. G., Kortuem, K., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00247-1 · Cited by 88
Bassi, A., John, O., Praveen, D., Maulik, P. K., Panda, R., & Jha, V. (2018) · JMIR mhealth and uhealth · doi:10.2196/11440 · Cited by 92
Sosale, B., Aravind, S. R., Murthy, H., Narayana, S., Sharma, U., GOWDA, S. G. V., & Naveenam, M. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000892 · Cited by 85
Das, T., Takkar, B., Sivaprasad, S., Thanksphon, T., Taylor, H. R., Wiedemann, P., Németh, J., Nayar, P. D., Rani, P. K., & Khandekar, R. B. (2021) · Eye · doi:10.1038/s41433-021-01572-4 · Cited by 85
Karakaya, M., & Hacisoftaoglu, R. E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03587-2 · Cited by 83
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Raman, R., Ramasamy, K., Rajalakshmi, R., Sivaprasad, S., & Natarajan, S. (2020) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_667_20 · Cited by 77
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Wake, D., Gibb, F., Kar, P., Kennon, B., Klonoff, D., Rayman, G., Rutter, M., Sainsbury, C., & Semple, R. (2020) · European Journal of Endocrinology · doi:10.1530/eje-20-0377 · Cited by 58
Sosale, B., Sosale, A., Murthy, H., Sengupta, S., & Naveenam, M. (2020) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1203_19 · Cited by 69
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Lanzetta, P., Sarao, V., Scanlon, P. H., Barratt, J., Porta, M., Bandello, F. M., & Loewenstein, A. (2020) · Acta Diabetologica · doi:10.1007/s00592-020-01506-8 · Cited by 69
Huemer, J. C., Wagner, S. K., & Sim, D. A. (2020) · Clinical ophthalmology · doi:10.2147/opth.s261629 · Cited by 63
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Wintergerst, M. W. M., Mishra, D. K. C., Hartmann, L., Shah, P. N., Konana, V. K., Sagar, P., Berger, M., Murali, K., Holz, F. G., Shanmugam, M. P., et al. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.05.025 · Cited by 55
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Nagra, M., Vianya-Estopà, M., & Wolffsohn, J. S. (2020) · Contact Lens and Anterior Eye · doi:10.1016/j.clae.2020.04.002 · Cited by 51
Kim, Y., & Chao, D. L. (2019) · Clinical ophthalmology · doi:10.2147/opth.s190922 · Cited by 51
Wintergerst, M. W. M., Petrak, M., Li, J. Q., Larsen, P. P., Berger, M., Holz, F. G., Finger, R. P., & Krohne, T. U. (2019) · Scientific Reports · doi:10.1038/s41598-019-56155-x · Cited by 49
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Rhee, S. Y., Kim, C., Shin, D. W., & Steinhubl, S. R. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0088 · Cited by 47
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Desideri, L. F., Rutigliani, C., Corazza, P., Nastasi, A., Roda, M., Nicolò, M., Traverso, C. E., & Vagge, A. (2022) · Journal of Optometry · doi:10.1016/j.optom.2022.08.001 · Cited by 50
Wintergerst, M. W. M., Brinkmann, C. K., Holz, F. G., & Finger, R. P. (2018) · Scientific Reports · doi:10.1038/s41598-018-28585-6 · Cited by 43
Prathiba, V., Rajalakshmi, R., Arulmalar, S., Usha, M., Subhashini, R., Gilbert, C. E., Anjana, R., & Mohan, V. (2020) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1937_19 · Cited by 43
Self-citationDetection of retinal abnormalities in fundus image using CNN deep learning networks
Akil, M., Elloumi, Y., & Kachouri, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819740-0.00002-4 · Cited by 43
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Self-citationUllah, W., Pathan, S. K., Panchal, A., Anandan, S., Saleem, K., Sattar, Y., Ahmad, E., Mukhtar, M., & Nawaz, H. (2020) · Medicine · doi:10.1097/md.0000000000020306 · Cited by 42
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Pujari, A., Saluja, G., Agarwal, D., Selvan, H., & Sharma, N. (2020) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-020-04917-z · Cited by 44
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Liu, Y., & Swearingen, R. (2017) · Current Diabetes Reports · doi:10.1007/s11892-017-0911-2 · Cited by 42
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Wintergerst, M. W. M., Jansen, L. G., Holz, F. G., & Finger, R. P. (2020) · JMIR mhealth and uhealth · doi:10.2196/17480 · Cited by 40
Update on Screening for Sight-Threatening Diabetic Retinopathy
Scanlon, P. H. (2019) · Ophthalmic Research · doi:10.1159/000499539 · Cited by 40
Jain, A., Krishnan, R., Rogye, A., & Natarajan, S. (2021) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_3808_20 · Cited by 39
Salongcay, R. P., Aquino, L. A. C., Salva, C. M. G., Saunar, A. V., Alog, G. P., Sun, J. K., Pető, T., & Silva, P. S. (2022) · Ophthalmology Retina · doi:10.1016/j.oret.2022.03.002 · Cited by 38
Vilela, M. A. P., Valença, F. M., Barreto, P. K. M., Amaral, C. E. V., & Pellanda, L. C. (2018) · Clinical ophthalmology · doi:10.2147/opth.s182022 · Cited by 36
Pradeepa, R., Rajalakshmi, R., & Mohan, V. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2019.0038 · Cited by 37
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Pei, X., Yao, X., Yang, Y., Zhang, H., Xia, M., Huang, R., Wang, Y., & Li, Z. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109190 · Cited by 31
Hainsworth, D. P., Gangula, A., Ghoshdastidar, S., Kannan, R., & Upendran, A. (2020) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2020.01.032 · Cited by 28
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Rajalakshmi, R., UmaSankari, G., Prathiba, V., Anjana, R. M., Ranjit Unnikrishnan, I., Venkatesan, U., Jebarani, S., Shanthirani, C. S., Sivaprasad, S., & Mohan, V. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0025 · Cited by 22
Self-citationSmartphone Assisted Fundus Fundoscopy/Photography
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Cho, K., Rege, A., Jing, Y., Chaurasia, A. R., Guruprasad, A., Arthur, E., & DeBuc, D. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-76407-5 · Cited by 22
Murthy, G. V. S., Gilbert, C. E., Babu, R., Gudlavalleti, A., Anchala, R., Shukla, R., Ballabh, P., Vashist, P., Ramachandra, S. S., Allagh, K. P., et al. (2016) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/2230-8210.179768 · Cited by 21
Imaging diabetic retinal disease: clinical imaging requirements
Schreur, V., Larsen, M. B., Sobrin, L., Bhavsar, A. R., den Hollander, A. I., Klevering, B. J., Hoyng, C. B., de Jong, E. K., Grauslund, J., & Pető, T. (2022) · Acta Ophthalmologica · doi:10.1111/aos.15110 · Cited by 21
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Latif, S., Khan, M. Y., Qayyum, A., Qadir, J., Usman, M., Ali, S. M., Abbasi, Q. H., & Imran, M. A. (2018) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-5225-5270-3.ch011 · Cited by 20
Upadhyaya, S., Rao, D. P., Kavitha, S., Ganeshrao, S. B., Negiloni, K., Bhandary, S., Savoy, F. M., & Venkatesh, R. (2024) · Ophthalmology Glaucoma · doi:10.1016/j.ogla.2024.09.002 · Cited by 19
Pei, X., & Li, Z. (2025) · BMJ Public Health · doi:10.1136/bmjph-2024-001353 · Cited by 25
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Shroff, S., Rao, D. P., Savoy, F. M., Shruthi, S., Hsu, C., Pradhan, Z. S., Jayasree, P. V., Sivaraman, A., Sengupta, S., Shetty, R., et al. (2022) · Journal of Glaucoma · doi:10.1097/ijg.0000000000002147 · Cited by 18
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Metadata
- Authors
- Rajalakshmi, R., Arulmalar, S., Usha, M., Prathiba, V., Kareemuddin, K. S., Anjana, R. M., & Mohan, V.
- Affiliations
- Madras Diabetes Research Foundation (IN)
- Corresponding author
- Mohan, Viswanathan
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-09-24
- Volume
- 10
- Issue
- 9
- Pages
- e0138285
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4581835
- OpenAlex
- W2179023931
- Cited by
- 173
- References
- 30
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 15 · 2023: 13 · 2022: 19 · 2021: 23 · 2020: 39 · 2019: 18 · 2018: 16 · 2017: 4 · 2016: 4
- FWCI
- 6.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy, diabetic retinopathy screening, type 2 diabetes, diabetic macular edema, proliferative diabetic retinopathy, diagnostic accuracy, image quality, low-cost screening
- MeSH terms
- Smartphone, Adult, Aged, Diabetic Retinopathy, Female, Humans, Male, Mass Screening, Middle Aged, Photography, Sensitivity and Specificity, Severity of Illness Index, Ophthalmoscopes, Young Adult
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