Machine Learning Algorithms for the Analysis of Age-Related Macular Degeneration Based on Optical Coherence Tomography: a Systematic Review
Paper #3570 received 7 Dec 2022; revised manuscript received 6 Mar 2023; accepted for publication 8 Mar 2023; published online 28 Apr 2023.
1. World Health Organisation, “World report on vision,” World Health Organisation, Switzerland (2019) (accessed 14 November 2022). [https://www.who.int/publications-detail/world-report-on-vision]. ISBN 978-92-4-151657-0.
2. R. Klein, B. E. K. Klein, S. C. Tomany, S. M. Meuer, and G.-H. Huang, “Ten-year incidence and progression of age-related maculopathy: The Beaver Dam eye study,” Ophthalmology 109(10), 1767–1779 (2002).
3. T. A. C. D. Guimaraes, M. D. Varela, M. Georgiou, and M. Michaelides, “Treatments for dry age-related macular degeneration: therapeutic avenues, clinical trials and future directions,” British Journal of Ophthalmology 106(3), 297–304 (2021).
4. N. M. Schultz, S. Bhardwaj, C. Barclay, L. Gaspar, and J. Schwartz, “Global Burden of Dry Age-Related Macular Degeneration: A Targeted Literature Review,” Clinical Therapeutics 43(10), 1792–1818 (2021).
5. B. Swenor, V. Varadaraj, M. J. Lee, H. Whitson, and P. Ramulu, “World Health Report on Vision: Aging Implications for Global Vision and Eye Health,” Innovation in Aging 4(1), 807–808 (2020).
6. L. S. Lim, P. Mitchell, J. M. Seddon, F. G. Holz, and T. Y. Wong, “Age-related macular degeneration,” The Lancet 379(9827), 1728–1738 (2012).
7. N. M. Bressler, S. B. Bressler, and S. L. Fine, “Age-related macular degeneration,” Survey of Ophthalmology 32(6), 375–413 (1988).
8. J. R. Evans, “Risk Factors for Age-related Macular Degeneration,” Progress in Retinal and Eye Research 20(2), 227–253 (2001).
9. P. Mitchell, G. Liew, B. Gopinath, and T. Y. Wong, “Age-related macular degeneration,” The Lancet 392(10153), 1147–1159 (2018).
10. J. Ambati, B. J. Fowler, “Mechanisms of Age-Related Macular Degeneration,” Neuron 75(1), 26–39 (2012).
11. R. Klein, T. Peto, A. Bird, and M. R. Vannewkirk, “The epidemiology of age-related macular degeneration,” American Journal of Ophthalmology 137(3), 486–495 (2004).
12. T. J. Heesterbeek, L. Lorés-Motta, C. B. Hoyng, Y. T. E. Lechanteur, and A. I. den Hollander, “Risk factors for progression of age-related macular degeneration,” Ophthalmic and Physiological Optics 40(2), 140–170 (2020).
13. M. P. Rozing, J. A. Durhuus, M. K. Nielsen, Y. Subhi, T. B. Kirkwood, R. G. Westendorp, and T. L. Sorensen, “Age-related macular degeneration: A two-level model hypothesis,” Progress in Retinal and Eye Research 76, 100825 (2020).
14. C. T. Supuran, “Agents for the prevention and treatment of age-related macular degeneration and macular edema: a literature and patent review,” Expert Opinion on Therapeutic Patents 29(10), 761–767 (2019).
15. D. W. Lem, P. G. Davey, D. L. Gierhart, and R. B. Rosen, “A Systematic Review of Carotenoids in the Management of Age-Related Macular Degeneration,” Antioxidants 10(8), 1255 (2021).
16. M. Gil-Martínez, P. Santos-Ramos, M. Fernández-Rodríguez, M. J. Abraldes, M. J. Rodríguez-Cid, M. Santiago-Varela, A. Fernández-Ferreiro, and F. Gómez-Ulla, “Pharmacological advances in the treatment of age-related macular degeneration,” Current Medicinal Chemistry 27(4), 583–598(2020).
17. S. K. Dubey, R. Pradhan, S. Hejmady, G. Singhvi, H. Choudhury, B. Gorain, and P. Kesharwani, “Emerging innovations in nano-enabled therapy against age-related macular degeneration: A paradigm shift,” International Journal of Pharmaceutics 600, 120499 (2021).
18. Y. P. Yang, Y. J. Hsiao, K. J. Chang, S. Foustine, Y. K. Ko, Y. C. Tsai, H. Y. Tai, Y. C. Ko, S. H. Chiou, T. C. Lin, S. J. Chen, Y. Chien, and D. K.Hwang, “Pluripotent Stem Cells in Clinical Cell Transplantation: Focusing on Induced Pluripotent Stem Cell-Derived RPE Cell Therapy in Age-Related Macular Degeneration,” International Journal of Molecular Sciences 23(22), 13794 (2022).
19. T. A. C. De Guimaraes, M. V. Daich, M. Georgiou, and M. Michaelides, “Treatments for dry age-related macular degeneration: therapeutic avenues, clinical trials and future directions,” British Journal of Ophthalmology 106(3), 297–304 (2022).
20. A. Sarkar, S. Dyawanapelly, “Nanodiagnostics and Nanotherapeutics for age-related macular degeneration,” Journal of Controlled Release 329, 1262–1282 (2021).
21. K. W. Graham, U. Chakravarthy, R. E. Hogg, K. A. Muldrew, I. S. Young, and F. Kee, “Identifying features of early and late age-related macular degeneration: A comparison of multicolor versus traditional color fundus photography,” Retina 38(9), 1751–1758 (2018).
22. B. Lumbroso and M. Rispoli, Practical handbook of OCT (Retina, Choroid, Glaucoma), Jaypee Brothers Medical Publishers, New Delhi, India (2012). ISBN: 978-93-5025-758-6.
23. B. M. Aznabaev, T. R. Mukhamadeev, and T. I. Dibaev, Optical coherence tomoghraphy + angiography of the eye in the diagnosis, therapy and surgery of eye diseases, August Borg, Moscow (2019). [in Russian]. ISBN: 978-5-901053-71-3.
24. A. V. Arus, “The Role of Imaging in Age-Related Macular Degeneration,” in Visual Impairment and Blindness-What We Know and What We Have to Know, G. L. Giudice, A. Catalá(Eds.), IntechOpen, UK (2020).
25. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
26. C. V. Regatieri, L. Branchini, and J. S. Duker, “The role of spectral-domain OCT in the diagnosis and management of neovascular age-related macular degeneration,” Ophthalmic Surgery, Lasers and Imaging Retina 42(4), S56–S66 (2011).
27. E. H. Lim, J. I. Han, C. G. Kim, S. W. Cho, and T. G. Lee, “Characteristic Findings of Optical Coherence Tomography in Retinal Angiomatous Proliferation,” Korean Journal of Ophthalmology 27(5), 351–360 (2013).
28. E. Corbelli, R. Sacconi, A. Rabiolo, S. Mercuri, A. Carnevali, L. Querques, F. Bandello, and G. Querques, “Optical Coherence Tomography Angiography in the Evaluation of Geographic Atrophy Area Extension,” Investigative Ophthalmology and Visual 58(12), 5201–5208 (2017).
29. J. Walther, M. Gaertner, P. Cimalla, A. Burkhardt, L. Kirsten, S. Meissner, and E. Koch, “Optical coherence tomography in biomedical research,” Analytical and Bioanalytical Chemistry 400, 2721–2743 (2011).
30. L. Rampasek, A. Goldenberg, “TensorFlow: Biology’s Gateway to Deep Learning?” Cell Systems 2(1), 12–14 (2016).
31. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, “Deep learning applications and challenges in big data analytics,” Journal of Big Data 2(1), 1–21 (2015).
32. D. Shen, G. Wu, and H. I. Suk, “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering 19, 221–248 (2017).
33. B. Juba, H. S. Le, “Precision-Recall versus accuracy and the role of large data sets,” Proceedings of the AAAI Conference on Artificial Intelligence 33(1), 4039–4048 (2019).
34. J. Latif, C. Xiao, A. Imran, and S. Tu, “Medical imaging using machine learning and deep learning algorithms: A review,” in 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 1–5 (2019).
35. B. Lee, M. D'Souza, E. L. Singman, J. Wang, F. A. Woreta, M. V. Boland, and D. Srikumaran, “Integration of a Physician Assistant Into an Ophthalmology Consult Service in an Academic Setting,” American Journal of Ophthalmology 190, 125–133 (2018).
36. S. Pandey, V. Sharma, “Robotics and ophthalmology: Are we there yet?” Indian Journal of Ophthalmology 67(7), 988 (2019).
37. B. Goutam, M. F. Hashmi, Z. W. Geem, and N. D. Bokde, “A Comprehensive Review of Deep Learning Strategies in Retinal Disease Diagnosis Using Fundus Images,” IEEE Access 10, 57796–57823 (2022).
38. L. Dong, Q. Yang, R. H. Zhang, and W. B. Wei, “Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis,” EclinicalMedicine 35, 100875 (2021).
39. R. Kapoor, B. T. Whigham, and L. A. Al-Aswad, “Artificial Intelligence and Optical Coherence Tomography Imaging,” The Asia-Pacific Journal of Ophthalmology 8(2), 187–194 (2019).
40. K. Jin, J. Ye, “Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives,” Advances in Ophthalmology Practice and Research 100078, (2022).
41. M. H. Sarhan, M. A. Nasseri, and D. Zapp, “Machine Learning Techniques for Ophthalmic Data Processing: A Review,” IEEE Journal of Biomedical and Health Informatics 24(12), 3338–3350 (2020).
42. R. Cheung, J. Chun, T. Sheidow, M. Motolko, and M. S. Malvankar-Mehta, “Diagnostic accuracy of current machine learning classifiers for age-related macular degeneration: a systematic review and meta-analysis,” Eye 36(5), 994–1004 (2022).
43. F. G. Venhuizen, B.V. Ginneken, F. V. Asten, M. J. J. P. V. Grinsven, S. Fauser, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Automated staging of age-related macular degeneration using optical coherence tomography,” Investigative Ophthalmology and Visual Science 58(4), 2318–2328 (2017).
44. B. I. Dodo, Y. Li, D. Kaba, and X. Liu, “Retinal Layer Segmentation in Optical Coherence Tomography Images,” IEEE Access 7, 152388–152398 (2019).
45. A. Chakravarty, J. Sivaswamy, “A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field,” Computer Methods and Programs in Biomedicine 165, 235–250 (2018).
46. U. Schmidt-Erfurth, S. M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu, B. S. Gerendas, A. Osborne, and H. Bogunović, “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence,” Investigative Ophthalmology Visual Science 59(8), 3199–3208 (2018).
47. G. Moraes, D. J. Fu, M. Wilson, H. Khalid, S. K. Wagner, E. Korot, D. Ferraz, L. Faes, C. J. Kelly, T. Spitz, P. J. Patel, K. Balaskas, T. D. L. Keenan, P. A. Keane, and R. Chopra,“Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning,” Ophthalmology 128(5), 693–705 (2021).
48. P. Seeböck, S. M. Waldstein, S. Klimscha, H. Bogunovic, T. Schlegl, B. S. Gerendas, R. Donner, U. Schmidt-Erfurth, and G. Langs, “Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data,” IEEE Transactions on Medical Imaging 38(4), 1037–1047 (2019).
49. H. Bogunović, A. Montuoro, M. Baratsits, M. G. Karantonis, S. M. Waldstein, F. Schlanitz, and U. Schmidt-Erfurth, “Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging,” Investigative Ophthalmology & Visual Science 58(6), BIO141-BIO150 (2017).
50. J. Kugelman, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Automatic segmentation of OCT retinal boundaries using recurrent neural networks and graph search,” Biomedical Optics Express 9(11), 5759–5777 (2018).
51. T. D. L. Keenan, T. E. Clemons, A. Domalpally, M. J. Elman, M. Havilio, E. Agrón, G. Benyamini, and E .Y. Chew, “Retinal specialist versus artificial intelligence detection of retinal fluid from OCT: age-related eye disease study 2: 10-year follow-on study,” Ophthalmology 128(1), 100–109 (2021).
52. S. K. Sodhi, A. Pereira, J. D. Oakley, J. Golding, C. Trimboli, D. B. Russakoff, and N. Choudhry, “Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study,” PLoS One 17(2), e0262111 (2022).
53. F. G. Venhuizen, B. V. Ginneken, B. Liefers, F. V. Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomedical Optics Express 9(4), 1545–1569 (2018).
54. Z. Akkus, A. Galimzianova, A. Hoogi, D. L. Rubin, and B. J. Erickson, “Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions,” Journal of Digital Imaging 30, 449–459 (2017).
55. M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Archive for Clinical and Experimental Ophthalmology 260(6), 1941–1946 (2017).
56. P. K. Upadhyay, S. Rastogi, and K. V. Kumar, “Coherent convolution neural network based retinal disease detection using optical coherence tomographic images,” Journal of King Saud University-Computer and Information Sciences 34(10), 9688–9695 (2022).
57. A. O. Tvenning, S. R. Hanssen, D. Austeng, and T. S. Morken, “Deep learning identify retinal nerve fibre and choroid layers as markers of age-related macular degeneration in the classification of macular spectral-domain optical coherence tomography volumes,” Acta Ophthalmologica 100(8), 937–945 (2022).
58. T. H. Rim, A. Y. Lee, D. S. Ting, K. Teo, B. K. Berzler, Z. L. Teo, T. K. Yoo, G. Lee, Y. Kim, A. C. Lin, S. E. Kim, Y. C. Tham, S. S. Kim, C. Cheng, T. Y. Wong, and C. M. G. Cheung, “Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm,” British Journal of Ophthalmology 105(8), 1133–1139 (2020).
59. P. Prahs, V. Radeck, C. Mayer, Y. Cvetkov, N. Cvetkova, H. Helbig, and D. Märker, “OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications,” Graefe’s Archive for Clinical and Experimental Ophthalmology 256(1), 91–98 (2017).
60. S. Kuwayama, Y. Ayatsuka, D. Yanagisono, T. Uta, H. Usui, A. Kato, N. Takase, Y. Ogura, and T. Yasukawa, “Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images,” Journal of Ophthalmology 2019, 6319581 (2019).
61. G. Zhang, D. J. Fu, B. Liefers, L. Faes, S. Glinton, S. Wagner, R. Struyven, N. Pontikos, P. A. Keane, and K. Balaskas, “Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study,” The Lancet Digital Health 3(10), e665–e675 (2021).
62. Z. Chen, D. Li, H. Shen, H. Mo, Z. Zeng, and H. Wei, “Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration,” Optics and Laser Technology 122, 105830 (2020).
63. A. Haq, A. Fariza, and N. Ramadijanti, “Development of vulnerable web application based on owasp api security risks,” in 2021 International Electronics Symposium (IES), Surabaya, Indonesia, 343–348 (2021).
64. H. Lee, K. E. Kang, H. Chung, and H. C. Kim, “Automated Segmentation of Lesions Including Subretinal Hyperreflective Material in Neovascular Age-related Macular Degeneration,” American Journal of Ophthalmology 191, 64–75 (2018).
65. T. K. Yoo, J. Y. Choi, J. G. Seo, B. Ramasubramanian, S. Selvaperumal, and D. W. Kim, “The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment,” Medical and Biological Engineering and Computing 57, 677–687 (2018).
66. P. M. Maloca, A. Y. Lee, E. R. de Carvalho, M. Okada, K. Fasler, I. Leung, B. Hormann, P. Kaiser, S. Suter, P. W. Hasler, J. Zarranz-Ventura, C. Egan, T. C. Heeren, and H. P. N. Scholl, “Validation of automated artificial intelligence segmentation of optical coherence tomography images,” PLoS One 14(8), e0220063 (2019).
67. S. Saha, M. Nassisi, M. Wang, S. Lindenberg, Y. kanagasingam, S. Sadda, and Z. J. Hu, “Automated detection and classification of early AMD biomarkers using deep learning,” Scientific Reports 9(1), 10990 (2019).
68. L. Varga, A. Kovács, T. Grósz, G. Thury, F. Hadarits, R. Dégi, and J. Dombi, “Automatic segmentation of hyperreflective foci in OCT images,” Computer Methods and Programs in Biomedicine 178, 91–103 (2019).
69. D. B. Russakoff, A. Lamin, J. D. Oakley, A. M. Dubis, and S. Sivaprasad, “Deep Learning for Prediction of AMD Progression: A Pilot Study,” Investigative Ophthalmology and Visual Science 60(2), 712–722 (2019).
70. M. Wu, X. Cai, Q. Chen, Z. Ji, S. Niu, T. Leng, D. L. Rubin, and H. Park, “Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging,” Computer Methods and Programs in Biomedicine 182, 105101 (2019).
71. V. Das, S. Dandapat, and P. K. Bora, “Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images,” Biomedical Signal Processing and Control 54, 101605 (2019).
72. L. Fang, Y. Jin, L. Huang, S. Guo, G. Zhao, and X. Chen, “Iterative fusion convolutional neural networks for classification of optical coherence tomography images,” Journal of Visual Communication and Image Representation 59, 327–333 (2019).
73. J. Qiu, Y. Sun, “Self-supervised iterative refinement learning for macular OCT volumetric data classification,” Computers in Biology and Medicine 111, 103327 (2019).
74. U. Schmidt-Erfurth, W. D. Vogl, L. M. Jampol, and H. Bogunović, “Application of Automated Quantification of Fluid Volumes to Anti-VEGF Therapy of Neovascular Age-Related Macular Degeneration,” Ophthalmology 127(9), 1211–1219 (2020).
75. S. M. Waldstein, W. D. Vogl, H. Bogunovic, A. Sadeghipour, S. Riedl, and U. Schmidt-Erfurth, “Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography,” JAMA Ophthalmology 138(7), 740–747 (2020).
76. K. K. Bhatia, M. S. Graham, L. Terry, A. Wood, P. Tranos, S. Trikha, and N. Jaccard, “Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data,” Retina 40(8), 1549–1557 (2019).
77. J. Yim, R. Chopra, T. Spitz, J. Winkens, A. Obika, C. Kelly, H. Askham, M. Lukic, J. Huemer, K. Fasler, G. Moraes, C. Meyer, M. Wilson, J. Dixon, C. Hughes, G. Rees, P. T. Khaw, A. Karthikesalingam, D. King, D. Hassabis, M. Suleyman, T. Back, J. R. Ledsam, P. A. Keane, and J. D. Fauw,“Predicting conversion to wet age-related macular degeneration using deep learning,” Nature Medicine 26(6), 892–899 (2020).
78. I. Banerjee, L. de Sisternes, J. A. Hallak, T. Leng, A. Osborne, P. J. Rosenfeld, G. Gregori, M. Durbin, and D. Rubin, “Prediction of age-related macular degeneration disease using a sequential deep learning approach on longitudinal SD-OCT imaging biomarkers,” Scientific Reports 10(1), 15434 (2020).
79. X. He, L. Fang, H. Rabbani, X. Chen, and Z. Liu, “Retinal optical coherence tomography image classification with label smoothing generative adversarial network,” Neurocomputing 405, 37–47 (2020).
80. X. Wang, F. Tang, H. Chen, L. Luo, Z. Tang, An-Ran Ran, C. Y. Cheung, and P. A. Heng, “UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification,” IEEE Journal of Biomedical and Health Informatics 24(12), 3431–3442 (2020).
81. X. Zhao, X. Zhang, B. Lv, L. Meng, C. Zhang, Y. Liu, C. Lv, G. Xie, and Y. Chen, “Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network,” Graefe’s Archive for Clinical and Experimental Ophthalmology 259(11), 3261–3269 (2021).
82. D. J. Fu, L. Faes, S. K. Wagner, G. Moraes, R. Chopra, P. J. Patel, K. Balaskas, T. D. L. Keenan, L. M. Bachmann, and P. A. Keane,“Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning,” Ophthalmology Retina 5(11), 1074–1084 (2021).
83. B. Liefers, P. Taylor, A. Alsaedi, C. Bailey, K. Balaskas, N. Dhingra, C. A. Egan, F. G. Rodrigues, C. G. Gonzalo, T. F. C. Heeren, A. Lotery, P. L. Müller, A. Olvera-Barrios, B. Paul, R. Schwartz, D. S. Thomas, A. N. Warwick, A. Tufail, and C. I. Sánchez, “Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning,” American Journal of Ophthalmology 226, 1–12 (2021).
84. A. Szeskin, R. Yehuda, O. Shmueli, J. Levy, and L. Joskowicz, “A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans,” Medical Image Analysis 63, 101693 (2021).
85. Y. Yan, K. Jin, Z. Gao, X. Huang, F. Wang, Y. Wang, and J. Ye, “Attention Based Deep Learning System for Automated Diagnoses of Age-Related Macular Degeneration in Optical Coherence Tomography Images,” Medical Physics 48(9), 4926–4934 (2021).
86. Z. Xu, W. Wang, J. Yang, J. Zhao, D. Ding, D. Chen, Z. Yang, X. Li,W. Yu, and Y. Chen, “Automated diagnoses of age-related macular degeneration and polypoidal choroidal vasculopathy using bi-modal deep convolutional neural networks,” British Journal of Ophthalmology 105(4), 561–566 (2021).
87. D. D. J. Hwang, S. Choi, J. Ko, J. Yoon, J. I. Park, J. S. Hwang, J. M. Han, H. J. Lee, J. Sohn, K. H. Park, and J. Han, “Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography,” Scientific Reports 11(1), 9275 (2021).
88. F. G. Holz, R. Abreu-Gonzalez, F. Bandello, R. Duval, L. O’toole, D. Pauleikhoff, G. Staurenghi, A. Wolf, D. Lorand, A. Clemens, and B. Gmeiner, “Does real-time artificial intelligence-based visual pathology enhancement of three-dimensional optical coherence tomography scans optimise treatment decision in patients with nAMD? Rationale and design of the RAZORBILL study,” British Journal of Ophthalmology 107(1), 96–101 (2021).
89. Y. Derradji, A. Mosinska, S. Apostolopoulos, C. Ciller, S. De Zanet, and I. Mantel, “Fully-automated atrophy segmentation in dry age-related macular degeneration in optical coherence tomography,” Scientific Reports 11(1), 21893 (2021).
90. Sunija, S. Kar, Gayathri, V. P. Gopi, and P. Palanisamy, “OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images,” Computer Methods and Programs in Biomedicine 200, 105877 (2021).
91. S. Riedl, V. Wolf-Dieter, J. Mai, G. S. Reiter, D. Lachinov, C. Grechenig, A. McKeown, L. Scheibler, H. Bogunović, and U. Schmidt-Erfurth, “The Effect of Pegcetacoplan Treatment on Photoreceptor Maintenance in Geographic Atrophy Monitored by Artificial Intelligence–Based OCT Analysis,” Ophthalmology Retina 6(11), 1009–1018 (2022).
92. V. Pramil, L. de Sisternes, L. Omlor, W. Lewis, H. Sheikh, Z. Chu, N. Manivannan, M. Durbin, R. Wang, P. Rosenfeld, M. Shen, R. Guymer, M. Liang, G. Gregori, and N. K Waheed, “A Deep Learning Model for Automated Segmentation of Geographic Atrophy Imaged Using Swept-Source OCT,” Ophthalmology Retina 7(2), 127–141 (2022).
93. K. A. Thakoor, J. Yao, D. Bordbar, O. Moussa, W. Lin, P. Sajda, and R. W. S. Chen, “A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers,” Scientific Reports 12(1), 2585 (2022).
94. T. G. Kamenskikh, O. N. Dolinina, I. O. Kolbenev, and E. V. Veselova, “An intelligent decision-making system for early diagnosis of macular pathology,” Rossiiskii Oftal’mologicheskii Zhurnal 15(2), 69–74 (2022). [in Russian].
95. J. Han, S. Choi, J. I. Park, J. S. Hwang, J. M. Han, H. J. Lee, J. Ko, J. Yoon, and D. Duck-Jin Hwang, “Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images,” Scientific Reports 12(1), 1–10 (2022).
96. P. Zang, T. T. Hormel, T. S. Hwang, S. T. Bailey, D. Huang, and Y. Jia, “Deep-learning-aided Diagnosis of DR, AMD, and Glaucoma based on Structural and Angiographic Optical Coherence Tomography,” Ophthalmology Science 3(1), 100245 (2022).
97. E. Parra-Mora, L. A. da Silva Cruz, “LOCTseg: A lightweight fully convolutional network for end-to-end optical coherence tomography segmentation,” Computers in Biology and Medicine 150, 106174 (2022).
98. T. C. Yeh, A.C. Luo, Y. S. Deng, Y. H. Lee, S. J. Chen, P. H. Chang, C. J. Lin, M. C. Tai, and Y. B. Chou, “Prediction of treatment outcome in neovascular age-related macular degeneration using a novel convolutional neural network,” Scientific Reports 12(1), 5871 (2022).
99. A. Thomas, P. M. Harikrishnan, R. Ramachandran, S. Ramachandran, R. Manoj, P. Palanisamy, and V. P. Gopi, “A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images,” Computer Methods and Programs in Biomedicine 209, 106294 (2021).
100. D. S. Kermany, M. Goldbaum, W. Cai, C. C. S. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, and K. Zhang, “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell 172(5), 1122–1113 (2018).
101. M. Adhi, J. S. Duker, “Optical coherence tomography-current and future applications,” Current Opinion in Ophthalmology 24(3), 213 (2013).
102. S. Laotaweerungsawat, C. Psaras, Z. Haq, X. Liu, and J. M. Stewart, “Racial and ethnic differences in foveal avascular zone in diabetic and nondiabetic eyes revealed by optical coherence tomography angiography,” PLoS One 16(10), e0258848 (2021).
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