Journal of Current Glaucoma Practice

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VOLUME 16 , ISSUE 2 ( May-August, 2022 ) > List of Articles

EDITORIAL

Glaucoma Screening: Is AI the Answer?

Shibal Bhartiya

Citation Information : Bhartiya S. Glaucoma Screening: Is AI the Answer?. J Curr Glaucoma Pract 2022; 16 (2):71-73.

DOI: 10.5005/jp-journals-10078-1380

License: CC BY-NC 4.0

Published Online: 30-08-2022

Copyright Statement:  Copyright © 2022; The Author(s).


Abstract

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  1. Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 2014;121(11):2081–2090. DOI: 10.1016/j.ophtha.2014.05.013
  2. https://www.nhp.gov.in/world-glaucoma-week-2021. Accessed on 23 06.2022
  3. Stein JD, Khawaja AP, Weizer JS. Glaucoma in adults-screening, diagnosis, and management: a review. JAMA 2021;325(2):164–174. DOI: 10.1001/jama.2020.21899
  4. Olawoye O, Azuara-Blanco A, Chan VF, et al. A review to populate a proposed cost-effectiveness analysis of glaucoma screening in Sub-Saharan Africa. Ophthalmic Epidemiol 2022;29(3):328–338. DOI: 10.1080/09286586.2021.1939887
  5. Tang J, Liang Y, O'Neill C, et al. Cost-effectiveness and cost-utility of population-based glaucoma screening in China: a decision-analytic Markov model. Lancet Glob Health 2019;7(7):e968–e978. DOI: 10.1016/S2214-109X(19)30201-3
  6. John D, Parikh R. Cost-effectiveness of community screening for glaucoma in rural India: a decision analytical model. Public Health 2018;155:142–151. DOI: 10.1016/j.puhe.2017.11.004
  7. Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103(2):167–175. DOI: 10.1136/bjophthalmol-2018-313173
  8. Thompson AC, Jammal AA, Medeiros FA. A review of deep learning for screening, diagnosis, and detection of glaucoma progression. Transl Vis Sci Technol 2020;9(2):42. DOI: 10.1167/tvst.9.2.42
  9. Hatt S, Wormald R, Burr J. Screening for prevention of optic nerve damage due to chronic open angle glaucoma. Cochrane Database Syst Rev 2006;18;2006(4):CD006129.
  10. Salazar H, Misra V, Swaminathan SS. Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management. Curr Opin Ophthalmol 2021;32(2):105–117. DOI: 10.1097/ICU.0000000000000741
  11. Hood DC, De Moraes CG. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 2018;125(8):1207–1208. DOI: 10.1016/j.ophtha.2018.04.020
  12. Ting DSW, Cheung CY, Lim G, 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;318(22):2211–2223. DOI: 10.1001/jama.2017.18152
  13. Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma. PLoS ONE 2017;12(5): e0177726. DOI: 10.13039/501100002467
  14. An G, Omodaka K, Hashimoto K, et al. Glaucoma diagnosis with machine learning based on optical coherence tomography and color fundus images. J Healthc Eng 2019;2019:4061313. DOI: 10.1155/2019/4061313
  15. Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res 2019;72:100759. DOI: 10.1016/j.preteyeres.2019.04.003
  16. Tan NY, Friedman DS, Stalmans I, et al. Glaucoma screening: where are we and where do we need to go? Curr Opin Ophthalmol 2020;31(2):91–100. DOI: 10.1097/ICU.0000000000000649
  17. Xiao X, Xue L, Ye L, et al. Health care cost and benefits of artificial intelligence-assisted population-based glaucoma screening for the elderly in remote areas of China: a cost-offset analysis. BMC Public Health 2021;21(1):1065. DOI: 10.1186/s12889-021-11097-w
  18. Abdullah YI, Schuman JS, Shabsigh R, et al. Ethics of artificial intelligence in medicine and ophthalmology. Asia Pac J Ophthalmol (Phila) 2021;10(3):289–298. DOI: 10.1097/APO.0000000000000397
  19. Ienca M, Ferretti A, Hurst S, et al. Considerations for ethics review of big data health research: a scoping review. PLoS One 2018;13(10):e0204937. DOI: 10.1371/journal.pone.0204937
  20. Schiff D, Borenstein J. How should clinicians communicate with patients about the roles of artificially intelligent team members? AMA J Ethics 2019;21(2):E138–145. DOI: 10.1001/amajethics.2019.138
  21. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25(1):44–56. DOI: 10.1038/s41591-018-0300-7
  22. Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res 2019;72:100759. DOI: 10.1016/j.preteyeres.2019.04.003
  23. Mirzania D, Thompson AC, Muir KW. Applications of deep learning in detection of glaucoma: a systematic review. Eur J Ophthalmol 2021;31(4):1618–1642. DOI: 10.1177/1120672120977346
  24. Nundy S, Montgomery T, Wachter RM. Promoting trust between patients and physicians in the era of artificial intelligence. JAMA 2019;322(6):497–498. DOI: 10.1001/jama.2018.20563
  25. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med 2019;25(1):30–36. DOI: 10.1038/s41591-018-0307-0
  26. Cohen, I. Bernard. “Faraday and Franklin's ‘Newborn Baby.’” Proceedings of the American Philosophical Society 1987;131(2):177–182. JSTOR, http://www.jstor.org/stable/986790. Accessed 21 Jul. 2022.
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