The role of artificial intelligence in modern ophthalmology

Abstract

Currently, artificial intelligence is actively being introduced into various spheres of life, and medicine is no exception. In ophthalmology, the use of artificial intelligence is very promising, given that the diagnosis and therapeutic monitoring of eye diseases often depend heavily on the correct interpretation of images. The use of artificial intelligence in ophthalmology focuses on eye diseases that lead to vision loss, such as age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Over the past few years, artificial intelligence has reached tremendous successes in the practice of ophthalmology. Many studies have shown that artificial intelligence performance is equal to and even exceeds the capabilities of ophthalmologists in many diagnostic and prognostic tasks. However, there is still a lot of work to be done before introducing artificial intelligence into routine clinical practice. Issues such as real-world performance, generalizability, and interpretability of artificial intelligence systems are still poorly understood and will require more attention in future research. Most artificial intelligence-based systems are used in developed countries, and some require further study. High costs and a shortage in doctors and equipment in some regions of the Russian Federation and rural areas make it difficult to screen for eye diseases. Although the field of artificial intelligence is underdeveloped, we hope that artificial intelligence will play an important role in the future of ophthalmology by making healthcare more efficient, accurate and accessible, especially in regions where staffing problems exist.

Keywords

artificial intelligence / ophthalmology / cataract / age-related macular degeneration / diabetic retinopathy / glaucoma / diagnosis / treatment

References

[1] Khusanov UA, Kudratillaev MB, Siddikov BN, Dovletova SB. Artificial intelligence in medicine. Science and Education. 2023;4(5): 772–782.

[2] Хусанов У.А., Кудратиллаев М.Б., Сиддиков Б.Н., Довлетова С.Б. Искусственный интеллект в медицине // Science and Education. 2023. Т. 4, № 5. С. 772–782.

[3] Gaifullin EO. Artificial intelligence in medicine. Ceteris paribus. 2023;(5):118–122. EDN: GQXLIT

[4] Гайфуллин Е.О. Искусственный интеллект в медицине // Ceteris paribus. 2023. № 5. С. 118–122. EDN: GQXLIT

[5] Phene S, Dunn RC, Hammel N, et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology. 2019;126(12):1627–1639. doi: 10.1016/j.ophtha.2019.07.024

[6] Phene S., Dunn R.C., Hammel N., et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs // Ophthalmology. 2019. Vol. 126, N. 12. P. 1627–1639. doi: 10.1016/j.ophtha.2019.07.024

[7] Li Z, Guo C, Lin D, et al. Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images. Br J Ophthalmol. 2021;105(11):1548–1554. doi: 10.1136/bjophthalmol-2020-317327

[8] Li Z., Guo C., Lin D., et al. Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images // Br J Ophthalmol. 2021. Vol. 105, N. 11. P. 1548–1554. doi: 10.1136/bjophthalmol-2020-317327

[9] Dow ER, Keenan TDL, Lad EM, et al. Collaborative community for ophthalmic imaging executive committee and the working group for artificial intelligence in age-related macular degeneration. From data to deployment: The collaborative community on ophthalmic imaging roadmap for artificial intelligence in age-related macular degeneration. Ophthalmology. 2022;129(5):43–59. doi: 10.1016/j.ophtha.2022.01.002

[10] Dow E.R., Keenan T.D.L., Lad E.M., et al. Collaborative community for ophthalmic imaging executive committee and the working group for artificial intelligence in age-related macular degeneration. From data to deployment: The collaborative community on ophthalmic imaging roadmap for artificial intelligence in age-related macular degeneration // Ophthalmology. 2022. Vol. 129, N. 5. P. 43–59. doi: 10.1016/j.ophtha.2022.01.002

[11] Ting DSJ, Foo VH, Yang LWY, et al. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology. Br J Ophthalmol. 2021;105(2):158–168. doi: 10.1136/bjophthalmol-2019-315651

[12] Ting D.S.J., Foo V.H., Yang L.W.Y., et al. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology // Br J Ophthalmol. 2021. Vol. 105, N. 2. P. 158–168. doi: 10.1136/bjophthalmol-2019-315651

[13] Li Z, Jiang J, Chen K, et al. Preventing corneal blindness caused by keratitis using artificial intelligence. Nat Commun. 2021;12(1):3738. doi: 10.1038/s41467-021-24116-6

[14] Li Z., Jiang J., Chen K., et al. Preventing corneal blindness caused by keratitis using artificial intelligence // Nat Commun. 2021. Vol. 12, N. 1. ID 3738. doi: 10.1038/s41467-021-24116-6

[15] Keenan TDL, Chen Q, Agrón E, et al. AREDS Deep Learning Research Group. DeepLensNet: Deep learning automated diagnosis and quantitative classification of cataract type and severity. Ophthalmology. 2022;129(5):571–584. doi: 10.1016/j.ophtha.2021.12.017

[16] Keenan T.D.L., Chen Q., Agrón E., et al. AREDS Deep Learning Research Group. DeepLensNet: Deep learning automated diagnosis and quantitative classification of cataract type and severity // Ophthalmology. 2022. Vol. 129, N. 5. P. 571–584. doi: 10.1016/j.ophtha.2021.12.017

[17] Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina. 2018;2(1):24–30. doi: 10.1016/j.oret.2017.03.015

[18] Schmidt-Erfurth U., Bogunovic H., Sadeghipour A., et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration // Ophthalmol Retina. 2018. Vol. 2, N. 1. P. 24–30. doi: 10.1016/j.oret.2017.03.015

[19] Keskinbora K, Güven F. Artificial intelligence and ophthalmology. Turk J Ophthalmol. 2020;50(1):37–43. doi: 10.4274/tjo.galenos.2020.78989

[20] Keskinbora K., Güven F. Artificial intelligence and ophthalmology // Turk J Ophthalmol. 2020. Vol. 50, N. 1. P. 37–43. doi: 10.4274/tjo.galenos.2020.78989

[21] Bali J, Bali O. Artificial intelligence in ophthalmology and healthcare: An updated review of the techniques in use. Indian J Ophthalmol. 2021;69(1):8–13. doi: 10.4103/ijo.IJO_1848_19

[22] Bali J., Bali O. Artificial intelligence in ophthalmology and healthcare: An updated review of the techniques in use // Indian J Ophthalmol. 2021. Vol. 69, N. 1. P. 8–13. doi: 10.4103/ijo.IJO_1848_19

[23] Moraru AD, Costin D, Moraru RL, Branisteanu CB. Artificial intelligence and deep learning in ophthalmology — present and future (Review). Exp Ther Med. 2020;20(4):3469–3473. doi: 10.3892/etm.2020.9118

[24] Moraru A.D., Costin D., Moraru R.L., Branisteanu C.B. Artificial intelligence and deep learning in ophthalmology — present and future (Review) // Exp Ther Med. 2020. Vol. 20, N. 4. P. 3469–3473. doi: 10.3892/etm.2020.9118

[25] Ahuja AS, Wagner IV, Dorairaj S, et al. Artificial intelligence in ophthalmology: A multidisciplinary approach. Integr Med Res. 2022;11(4):100888. doi: 10.1016/j.imr.2022.100888

[26] Ahuja A.S., Wagner I.V., Dorairaj S., et al. Artificial intelligence in ophthalmology: A multidisciplinary approach // Integr Med Res. 2022. Vol. 11, N. 4. ID 100888. doi: 10.1016/j.imr.2022.100888

[27] Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10(3):257–273. doi: 10.1007/s12194-017-0406-5

[28] Suzuki K. Overview of deep learning in medical imaging // Radiol Phys Technol. 2017. Vol. 10, N. 3. P. 257–273. doi: 10.1007/s12194-017-0406-5

[29] Lee CS, Brandt JD, Lee AY. Big Data and artificial intelligence in ophthalmology: Where are we now? Ophthalmol Sci. 2021;1(2):100036. doi: 10.1016/j.xops.2021.100036

[30] Lee C.S., Brandt J.D., Lee A.Y. Big Data and artificial intelligence in ophthalmology: Where are we now? // Ophthalmol Sci. 2021. Vol. 1, N. 2. ID 100036. doi: 10.1016/j.xops.2021.100036

[31] Garri DD, Saakyan SV, Khoroshilova-Maslova IP, et al. Мethods of machine learning in ophthalmology: Review. Ophthalmology in Russia. 2020;17(1):20–31. EDN: RSCNAV doi: 10.18008/1816-5095-2020-1-20-31

[32] Гарри Д.Д., Саакян С.В., Хорошилова-Маслова И.П., и др. Методы машинного обучения в офтальмологии. Обзор литературы // Офтальмология. 2020. Т. 17, № 1. С. 20–31. EDN: RSCNAV doi: 10.18008/1816-5095-2020-1-20-31

[33] Dedov II, Shestakova MV, Vikulova OK, et al. Diabetes mellitus in the Russian Federation: dynamics of epidemiological indicators according to the Federal Register of Diabetes Mellitus for the period 2010–2022. Diabetes mellitus. 2023;26(2):104–123. EDN: DVDJWJ doi: 10.14341/DM13035

[34] Дедов И.И., Шестакова М.В., Викулова О.К., и др. Сахарный диабет в Российской Федерации: динамика эпидемиологических показателей по данным Федерального регистра сахарного диабета за период 2010–2022 гг. // Сахарный диабет. 2023. Т. 26, № 2. С. 104–123. EDN: DVDJWJ doi: 10.14341/DM13035

[35] Lev IV, Milyusin VE, Yastrebtsev MD. Diabetic retinopathy among ophthalmological complications of diabetes mellitus and other ophthalmopathology. Current problems of health care and medical statistics. 2023;(1):240–251. EDN: CQRRHL doi: 10.24412/2312-2935-2023-1-240-251

[36] Лев И.В., Милюсин В.Е., Ястребцев М.Д. Диабетическая ретинопатия среди офтальмологических осложнений сахарного диабета и другой офтальмопатологии // Современные проблемы здравоохранения и медицинской статистики. 2023. № 1. С. 240–251. EDN: CQRRHL doi: 10.24412/2312-2935-2023-1-240-251

[37] Burton MJ, Ramke J, Marques AP, et al. The lancet global health commission on global eye health: vision beyond 2020. Lancet Glob Health. 2021;9(4):489–551. doi: 10.1016/S2214-109X(20)30488-5

[38] Burton M.J., Ramke J., Marques A.P., et al. The lancet global health commission on global eye health: vision beyond 2020 // Lancet Glob Health. 2021. Vol. 9, N. 4. P. 489–551. doi: 10.1016/S2214-109X(20)30488-5

[39] Odilov MYu. Improving the treatment of diabetic retinopathy. Research Journal of Trauma and Disability Studies. 2023;2(10):86–90.

[40] Одилов М.Ю. Совершенствование лечения диабетической ретинопатии // Research Journal of Trauma and Disability Studies. 2023. Т. 2, № 10. С. 86–90.

[41] Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. doi: 10.1001/jama.2016.17216

[42] Gulshan V., Peng L., Coram M., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs // JAMA. 2016. Vol. 316, N. 22. P. 2402–2410. doi: 10.1001/jama.2016.17216

[43] 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

[44] Ting D.S.W., Cheung C.Y., 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. Vol. 318, N. 22. P. 2211–2223. doi: 10.1001/jama.2017.18152

[45] Tang F, Luenam P, Ran AR, et al. Detection of diabetic retinopathy from ultra-widefield scanning laser ophthalmoscope images: A multicenter deep learning analysis. Ophthalmol Retina. 2021;5(11): 1097–1106. doi: 10.1016/j.oret.2021.01.013

[46] Tang F., Luenam P., Ran A.R., et al. Detection of diabetic retinopathy from ultra-widefield scanning laser ophthalmoscope images: A multicenter deep learning analysis // Ophthalmol Retina. 2021. Vol. 5, N. 11. P. 1097–1106. doi: 10.1016/j.oret.2021.01.013

[47] Engelmann J, McTrusty AD, MacCormick IJC, et al. Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning. Nat Mach Intell. 2022;4:1143–1154. doi: 10.1038/s42256-022-00566-5

[48] Engelmann J., McTrusty A.D., MacCormick I.J.C., et al. Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning // Nat Mach Intell. 2022. Vol. 4. P. 1143–1154. doi: 10.1038/s42256-022-00566-5

[49] Demidova TY, Kozhevnikov AA. Diabetic retinopathy: history, modern approaches to management, prospective views of prevention and treatment. Diabetes mellitus. 2020;23(1):95–105. EDN: ECFMZS doi: 10.14341/DM10273

[50] Демидова Т.Ю., Кожевников А.А. Диабетическая ретинопатия: история, современные подходы к ведению, перспективные взгляды на профилактику и лечение // Сахарный диабет. 2020. Т. 23, № 1. С. 95–105. EDN: ECFMZS doi: 10.14341/DM10273

[51] Dai L, Wu L, Li H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun. 2021;12(1):3242. doi: 10.1038/s41467-021-23458-5

[52] Dai L., Wu L., Li H., et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum // Nat Commun. 2021. Vol. 12, N. 1. ID 3242. doi: 10.1038/s41467-021-23458-5

[53] Bora A, Balasubramanian S, Babenko B, et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit Health. 2021;3(1):E10–E19. doi: 10.1016/S2589-7500(20)30250-8

[54] Bora A., Balasubramanian S., Babenko B., et al. Predicting the risk of developing diabetic retinopathy using deep learning // Lancet Digit Health. 2021. Vol. 3, N. 1. P. E10–E19. doi: 10.1016/S2589-7500(20)30250-8

[55] Arcadu F, Benmansour F, Maunz A, et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med. 2019;2:92. doi: 10.1038/s41746-019-0172-3

[56] Arcadu F., Benmansour F., Maunz A., et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients // NPJ Digit Med. 2019. Vol. 2. ID 92. doi: 10.1038/s41746-019-0172-3

[57] Movsisyan AB, Kuroedov AV, Arkharov MA, et al. Epidemiological analysis primary open-angle glaucoma incidence and prevalence in Russia. Russian journal of clinical ophthalmology. 2022;22(1):3–10. EDN: VIMPIU doi: 10.32364/2311-7729-2022-22-1-3-10

[58] Мовсисян А.Б., Куроедов А.В., Архаров М.А., и др. Эпидемиологический анализ заболеваемости и распространённости первичной открытоугольной глаукомы в Российской Федерации // Клиническая офтальмология. 2022. Т. 22, № 1. С. 3–10. EDN: VIMPIU doi: 10.32364/2311-7729-2022-22-1-3-10

[59] Tham Y-C, 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

[60] Tham Y.-C., Li X., Wong T.Y., et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis // Ophthalmology. 2014. Vol. 121, N. 11. P. 2081–2090. doi: 10.1016/j.ophtha.2014.05.013

[61] Movsisyan AB, Kuroyedov AV. Making a diagnosis of glaucoma at the present time. Russian journal of clinical ophthalmology. 2023;23(1): 47–53. EDN: BFGXMR doi: 10.32364/2311-7729-2023-23-1-47-53

[62] Мовсисян А.Б., Куроедов А.В. Диагностика глаукомы на современном этапе // Клиническая офтальмология. 2023. Т. 23, № 1. С. 47–53. EDN: BFGXMR doi: 10.32364/2311-7729-2023-23-1-47-53

[63] Li Z, Keel S, Liu C, et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41(12):2509–2516. doi: 10.2337/dc18-0147

[64] Li Z., Keel S., Liu C., et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs // Diabetes Care. 2018. Vol. 41, N. 12. P. 2509–2516. doi: 10.2337/dc18-0147

[65] Machekhin VA, Fabrikantov OL, L’vov VA. Applications of optical coherence tomography in glaucoma. The Russian annals of ophthalmology. 2019;135(2):130–137. EDN: XUXVBD doi: 10.17116/oftalma2019135021130

[66] Мачехин В.А., Фабрикантов О.Л., Львов В.А. Возможности оптической когерентной томографии при глаукоме // Вестник офтальмологии. 2019. Т. 135, № 2. С. 130–137. EDN: XUXVBD doi: 10.17116/oftalma2019135021130

[67] Ran AR, Cheung CY, Wang X, et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis. Lancet Digit Health. 2019;1(4):172–182. doi: 10.1016/S2589-7500(19)30085-8

[68] Ran A.R., Cheung C.Y., Wang X., et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis // Lancet Digit Health. 2019. Vol. 1, N. 4. P. 172–182. doi: 10.1016/S2589-7500(19)30085-8

[69] Fu H, Baskaran M, Xu Y, et al. A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images. Am J Ophthalmol. 2019;203:37–45. doi: 10.1016/j.ajo.2019.02.028

[70] Fu H., Baskaran M., Xu Y., et al. A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images // Am J Ophthalmol. 2019. Vol. 203. P. 37–45. doi: 10.1016/j.ajo.2019.02.028

[71] Yousefi S, Kiwaki T, Zheng Y, et al. Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol. 2018;193:71–79. doi: 10.1016/j.ajo.2018.06.007

[72] Yousefi S., Kiwaki T., Zheng Y., et al. Detection of longitudinal visual field progression in glaucoma using machine learning // Am J Ophthalmol. 2018. Vol. 193. P. 71–79. doi: 10.1016/j.ajo.2018.06.007

[73] Wang M, Shen LQ, Pasquale LR, et al. An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis. Invest Ophthalmol Vis Sci. 2019;60(1): 365–375. doi: 10.1167/iovs.18-25568

[74] Wang M., Shen L.Q., Pasquale L.R., et al. An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis // Invest Ophthalmol Vis Sci. 2019. Vol. 60, N. 1. P. 365–375. doi: 10.1167/iovs.18-25568

[75] Ivakhnenko OI, Neroyev VV, Zaytseva OV. Age-related macular degeneration and diabetic eye lesion. Socio-economic aspects. The Russian annals of ophthalmology. 2021;137(1):123–129. EDN: CDTVFR doi: 10.17116/oftalma2021137011123

[76] Ивахненко О.И., Нероев В.В., Зайцева О.В. Возрастная макулярная дегенерация и диабетическое поражение глаз. Социально-экономические аспекты заболеваемости // Вестник офтальмологии. 2021. Т. 137, № 1. С. 123–129. EDN: CDTVFR doi: 10.17116/oftalma2021137011123

[77] Yangieva N. Implementation of a program for mass prevention and early detection of age-related macular degeneration. in Library. 2020;20(1):676–681.

[78] Янгиева Н. Реализация программы массовой профилактики и раннего выявления возрастной макулярной дегенерации // in Library. 2020. Т. 20, № 1. С. 676–681.

[79] Peng Y, Dharssi S, Chen Q, et al. DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology. 2019;126(4):565–575. doi: 10.1016/j.ophtha.2018.11.015

[80] Peng Y., Dharssi S., Chen Q., et al. DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs // Ophthalmology. 2019. Vol. 126, N. 4. P. 565–575. doi: 10.1016/j.ophtha.2018.11.015

[81] Burlina PM, Joshi N, Pekala M, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135(11):1170–1176. doi: 10.1001/jamaophthalmol.2017.3782

[82] Burlina P.M., Joshi N., Pekala M., et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks // JAMA Ophthalmol. 2017. Vol. 135, N. 11. P. 1170–1176. doi: 10.1001/jamaophthalmol.2017.3782

[83] Yim J, Chopra R, Spitz T, et al. Predicting conversion to wet age-related macular degeneration using deep learning. Nat Med. 2020;26(6):892–899. doi: 10.1038/s41591-020-0867-7

[84] Yim J., Chopra R., Spitz T., et al. Predicting conversion to wet age-related macular degeneration using deep learning // Nat Med. 2020. Vol. 26, N. 6. P. 892–899. doi: 10.1038/s41591-020-0867-7

[85] Yan Q, Weeks DE, Xin H, et al. Deep-learning-based prediction of late age-related macular degeneration progression. Nat Mach Intell. 2020;2(2):141–150. doi: 10.1038/s42256-020-0154-9

[86] Yan Q., Weeks D.E., Xin H., et al. Deep-learning-based prediction of late age-related macular degeneration progression // Nat Mach Intell. 2020. Vol. 2, N. 2. P. 141–150. doi: 10.1038/s42256-020-0154-9

[87] Schlegl T, Waldstein SM, Bogunovic H, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125(4):549–558. doi: 10.1016/j.ophtha.2017.10.031

[88] Schlegl T., Waldstein S.M., Bogunovic H., et al. Fully automated detection and quantification of macular fluid in OCT using deep learning // Ophthalmology. 2018. Vol. 125, N. 4. P. 549–558. doi: 10.1016/j.ophtha.2017.10.031

[89] Egorov AE, Movsisyan AB, Glazko NG. State-of-the-art cataract surgery. Nuances and solutions. Russian Journal of Clinical Ophthalmology. 2020;20(3):142–147. EDN: CQPWJM doi: 10.32364/2311-7729-2020-20-3-142-147

[90] Егоров А.Е., Мовсисян А.Б., Глазко Н.Г. Современная хирургия катаракты. Нюансы и решения // Клиническая офтальмология. 2020. Т. 20, № 3. С. 142–147. EDN: CQPWJM doi: 10.32364/2311-7729-2020-20-3-142-147

[91] Cicinelli MV, Buchan JC, Nicholson M, et al. Cataracts. Lancet. 2023;401(10374):377–389. doi: 10.1016/S0140-6736(22)01839-6

[92] Cicinelli M.V., Buchan J.C., Nicholson M., et al. Cataracts // Lancet. 2023. Vol. 401, N. 10374. P. 377–389. doi: 10.1016/S0140-6736(22)01839-6

[93] Tham Y-C, Goh JHL, Anees A, et al. Detecting visually significant cataract using retinal photograph-based deep learning. Nat Aging. 2022;2(3):264–271. doi: 10.1038/s43587-022-00171-6

[94] Tham Y.-C., Goh J.H.L., Anees A., et al. Detecting visually significant cataract using retinal photograph-based deep learning // Nat Aging. 2022. Vol. 2, N. 3. P. 264–271. doi: 10.1038/s43587-022-00171-6

RIGHTS & PERMISSIONS

Eco-Vector