Diagnostic Accuracy of An Artificial Intelligence Based mHEALTH Intervention for Cataract Detection: A Multi-Center Prospective Study In Tamil Nadu, India
DOI:
https://doi.org/10.55489/njcm.160920255469Keywords:
Accuracy, AI, Artificial Intelligence, Cataract, e-Paarvai, mHealthAbstract
Background: E-Paarvai is an AI-based mHealth initiative piloted in Tamil Nadu, which enables frontline health workers to screen for cataract using a smartphone camera. The study aims to evaluate the diagnostic accuracy and reliability of e-Paarvai for cataract detection in primary care settings.
Methodology: This prospective study was performed in 2022 in seven Upgraded Primary Health Centers across Tuticorin, India. Outpatients (age ≥ 50 years) without bilateral aphakia/pseudophakia, recruited by consecutive sampling, were each screened for cataract by e-Paarvai and an Ophthalmic assistant. Estimates of accuracy and reliability were reported along with their 95% confidence intervals (CI).
Results: Among 337 participants (674 eyes) included in the analysis, 55 (16%) had unilateral and 168 (50%) participants had bilateral cataract on clinical eye examination. E-paarvai had a sensitivity and specificity of 83% and 53% at the subject level and 73% and 70.3% at the eye level respectively. Assuming 65% prevalence for cataract, PPV was 76% and 82%, while NPV was 62% and 58% in the per-subject and per-eye analysis respectively. The test-retest agreement was substantial with Kappa of 0.63.
Conclusions: E-paarvai has an undeniable potential to improve detection and yield of cataract when implemented as a mass strategy in an eye care resource limited population.
References
World Health Organization. Blindness and vision impairment. Geneva: WHO; 2023. Available at: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment [Accessed Apr 2nd, 2025]
National Programme for Control of Blindness and Visual Impairment. National Blindness and Visual Impairment Survey India 2015-2019: A Summary Report. New Delhi, India: Ministry of Health and Family Welfare; 2019. Available at: https://npcbvi.mohfw.gov.in/writeReadData/mainlinkFile/File341.pdf [Accessed Apr 2nd, 2025]
National Association of Software and Service Companies. AI Gamechangers: Accelerating India with Innovation. India: NASSCOM; 2021. Available at: https://nasscom.in/ai-gamechangers2021/pdf/AI-Gamechangers0582021.pdf [Accessed Apr 2nd, 2025]
O’hEineachain R. Automating Cataract Detection and Care Decisions. European Society of Cataract and Refractive Surgeons. London (UK)- June 30, 2021 Available at: https://www.escrs.org/eurotimes/automating-cataract-detection-and-care-decisions [Accessed Apr 2nd, 2025]
Nasirzonouzi M. Automated cataract grading using smartphone images [MS thesis]. Canada: UniversityofWaterloo; 2020. Available from: https://uwspace.uwaterloo.ca/items/78e0fd2e-d8b6-4eb8-bf5a-85f99d6d5057
Lin H, Li R, Liu Z, Chen J, Yang Y, Chen H, et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine 2019;9:52-59. DOI: https://doi.org/10.1016/j.eclinm.2019.03.001 PMid:31143882 PMCid:PMC6510889
Hu S, Wu H, Luan X, Wang Z, Adu M, Wang X, et al. Portable Handheld Slit-Lamp Based on a Smartphone Camera for Cataract Screening. J Ophthalmol 2020;2020:1037689. DOI: https://doi.org/10.1155/2020/1037689 PMid:32832134 PMCid:PMC7421715
H. Yazu, E. Shimizu, S. Okuyama, T. Katahira, N. Aketa, R. Yokoiwa, et al. Evaluation of nuclear cataract with smartphone attachable slit-lamp device. Diagnostics 2020;10(8):576. DOI: https://doi.org/10.3390/diagnostics10080576 PMid:32784828 PMCid:PMC7459829
India's Techade: Digital Governance in a Post Pandemic World. 24th National Conference on e-Governance; 2022. Available at : https://www.nceg.gov.in/assets/pdf/Proceedings_of_the_24th_NCeG.pdf [Accessed Aug 20th, 2025]
Press Information Bureau. Update on National Programme for Control of Blindness and Visual Impairment (NPCBVI). New Delhi, India : Ministry of Health and Family Welfare; 2023. Available at: https://www.pib.gov.in/PressReleseDetailm.aspx?PRID=1944598 [Accessed Aug 20th, 2025]
Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 2016;6:e012799. DOI: https://doi.org/10.1136/bmjopen-2016-012799 PMid:28137831 PMCid:PMC5128957
National Programme for Control of Blindness. National Survey on Blindness and Visual Outcomes after Cataract Surgery (2001-2002). New Delhi, India: Ministry of Health and Family Welfare; 2002. Available at : https://www.aiims.edu/images/depart/RPC/reports%20for%20web/1.%20NPCB%20National%20Blindness%20Survey%202001-02.pdf [Accessed Apr 10th, 2025]
Mercaldo ND, Lau KF, Zhou XH. Confidence intervals for predictive values with an emphasis to case-control studies. Statist. Med 2007;26(10):2170-2183. DOI: https://doi.org/10.1002/sim.2677 PMid:16927452
Altman D. Diagnostic tests, 2nd ed. In: Statistics with confidence: confidence intervals and statistical guidelines. John Wiley & Sons; 2013. pp. 105-119.
Aarthi R, Roy G, Kar SS, Srinivasan R. Prevalence of cataract among adults above 50 years in a rural community of Villupuram, Tamil Nadu. International Journal of Advanced Medical and Health Research 2015;2(1):50-54. DOI: https://doi.org/10.4103/2349-4220.159170
Vashist P, Talwar B, Gogoi M, Maraini G, Camparini M, Ravindran RD, et al. Prevalence of cataract in an older population in India: the India study of age-related eye disease. Ophthalmology 2011;118(2):272-278. DOI: https://doi.org/10.1016/j.ophtha.2010.05.020 PMid:20801514 PMCid:PMC3146699
Mano AVM, Tiwari N, Khobragade V, Pareek M, Panchbhai A, Ghosh P, et al. Clinical validation of artificial intelligence-based cataract screening solution with smartphone images (Logy AI cataract screening module). Int J Adv Med 2024;11(2):71-77. DOI: https://doi.org/10.18203/2349-3933.ijam20240007
Vasan CS, Gupta S, Shekhar M, Nagu K, Balakrishnan L, Ravindran RD, et al. Accuracy of an artificial intelligence based mobile application for detecting cataracts: Results from a case. Indian J Ophthalmol 2023;71(8):2984-2989. DOI: https://doi.org/10.4103/IJO.IJO_3372_22 PMid:37530269 PMCid:PMC10538832
Umemneku Chikere CM, Wilson KJ, Allen AJ, Vale L. Comparative diagnostic accuracy studies with an imperfect reference standard - a comparison of correction methods. BMC Med Res Methodol 2021;21:67. DOI: https://doi.org/10.1186/s12874-021-01255-4 PMid:33845775 PMCid:PMC8040223
Genders TS, Spronk S, Stijnen T, Steyerberg EW, Lesaffre E, Hunink MM. Methods for Calculating Sensitivity and Specificity of Clustered Data: A Tutorial. Radiology 2012;265(3):910-916. DOI: https://doi.org/10.1148/radiol.12120509 PMid:23093680
Ying GS, Maguire MG, Glynn RJ, Rosner B. Calculating Sensitivity, Specificity, and Predictive Values for Correlated Eye Data. Invest Ophthalmol Vis Sci. 2020 Sep 1;61(11):29. DOI: https://doi.org/10.1167/iovs.61.11.29 PMid:32936302 PMCid:PMC7500131
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Copyright (c) 2025 Shruthee SG, Deepanchakravarthi Vellingiri, Kumaraswamy Rajagopal, Porchelvan Shanmugaiah
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