Journal of Ophthalmic and Vision Research

ISSN: 2008-322X

The latest research in clinical ophthalmology and the science of vision.

Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review

Published date: Sep 16 2024

Journal Title: Journal of Ophthalmic and Vision Research

Issue title: July–Sep 2024, Volume 19, Issue 3

Pages: 354–367

DOI: 10.18502/jovr.v19i3.15893

Authors:

Hesam Hashemian - shhlucky@yahoo.com

Tunde Peto - t.peto@qub.ac.uk - https://orcid.org/0000-0001-6265-0381

Renato Ambrósio Jr - dr.renatoambrosio@gmail.com - https://orcid.org/0000-0001-6919-4606

Imre Lengyel - i.lengyel@qub.ac.uk

Rahele Kafieh - Raheleh.kafieh@durham.ac.uk - https://orcid.org/0000-0003-0087-9476

Ahmed Muhammed Noori - ahmedalganemy@gmail.com - https://orcid.org/0009-0004-3894-6309

Masoud Khorrami-Nezhad - op_khorrami@yahoo.com - https://orcid.org/0000-0002-8270-9704

Abstract:

Artificial intelligence (AI) holds immense promise for transforming ophthalmic care through automated screening, precision diagnostics, and optimized treatment planning. This paper reviews recent advances and challenges in applying AI techniques such as machine learning and deep learning to major eye diseases. In diabetic retinopathy, AI algorithms analyze retinal images to accurately identify lesions, which helps clinicians in ophthalmology practice. Systems like IDx- DR (IDx Technologies Inc, USA) are FDA-approved for autonomous detection of referable diabetic retinopathy. For glaucoma, deep learning models assess optic nerve head morphology in fundus photographs to detect damage. In age-related macular degeneration, AI can quantify drusen and diagnose disease severity from both color fundus and optical coherence tomography images. AI has also been used in screening for retinopathy of prematurity, keratoconus, and dry eye disease. Beyond screening, AI can aid treatment decisions by forecasting disease progression and anti- VEGF response. However, potential limitations such as the quality and diversity of training data, lack of rigorous clinical validation, and challenges in regulatory approval and clinician trust must be addressed for the widespread adoption of AI. Two other significant hurdles include the integration of AI into existing clinical workflows and ensuring transparency in AI decisionmaking processes. With continued research to address these limitations, AI promises to enable earlier diagnosis, optimized resource allocation, personalized treatment, and improved patient outcomes. Besides, synergistic human-AI systems could set a new standard for evidence-based, precise ophthalmic care.

References:

1. Sun L, Gupta RK, Sharma A. Review and potential for artificial intelligence in healthcare. Int J Syst Assur Eng Manag 2022;13:54–62.

2. Anton N, Doroftei B, Curteanu S, Catãlin L, Ilie OD, Târcoveanu F, et al. Comprehensive review on the use of artificial intelligence in ophthalmology and future research directions. Diagnostics (Basel) 2022;13:100.

3. Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. Adv Ophthalmol Pract Res 2022;2:100078.

4. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342–1350.

5. Wang Z, Keane PA, Chiang M, Cheung CY, Wong TY, Ting DS. Artificial intelligence and deep learning in ophthalmology. Artif Intell Med 2022;13:1519–1552.

6. Benet D, Pellicer-Valero OJ. Artificial intelligence: The unstoppable revolution in ophthalmology. Surv Ophthalmol 2022;67:252–270.

7. Burton MJ, Ramke J, Marques AP, Bourne RRA, Congdon N, Jones I, et al. The Lancet Global Health Commission on global eye health: Vision beyond 2020. Lancet Glob Health 2021;9:E489–E551.

8. Holden BA, Wilson DA, Jong M, Sankaridurg P, Fricke TR, Smith EL 3rd, et al. Myopia: A growing global problem with sight-threatening complications. Community Eye Health 2015;28:35.

9. Lin H, Long E, Ding X, Diao H, Chen Z, Liu R, et al. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study. PLoS Med 2018;15:e1002674.

10. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018;1:39.

11. Ting DS, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103:167–175.

12. Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 2018;125:1199–1206.

13. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RV, et al.; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 2018;136:803–810.

14. Lin SR, Ladas JG, Bahadur GG, Al-Hashimi S, Pineda R, editors. A review of machine learning techniques for keratoconus detection and refractive surgery screening. Semin Ophthalmol 2019;34:317–326.

15. Moraru AD, Costin D, Moraru RL, Branisteanu DC. Artificial intelligence and deep learning in ophthalmology - Present and future (Review). Exp Ther Med 2020;20:3469–3473.

16. Kumar P, Kumar R, Gupta M. Deep learning based analysis of ophthalmology: A systematic review. EAI Endorsed Trans Pervasive Health Technol 2021;7.

17. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and metaanalysis. Lancet Digit Health 2019;1:e271–97.

18. Ouyang J, Wang G, Gong E, Chen K, Pauly J, Zaharchuk G. Task-GAN: Improving generative adversarial network for image reconstruction. In Machine learning for medical image reconstruction: Second international workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, 2019, pp. 193–204. Springer International Publishing, 2019.

19. Qiu B, You Y, Huang Z, Meng X, Jiang Z, Zhou C, et al. N2NSR-OCT: Simultaneous denoising and superresolution in optical coherence tomography images using semi supervised deep learning. J Biophotonics 2021;14:e202000282.

20. Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 2018;125:549–558.

21. Rispoli M, Cennamo G, Antonio LD, Lupidi M, Parravano M, Pellegrini M, et al. Practical guidance for imaging biomarkers in exudative age-related macular degeneration. Surv Ophthalmol 2023;68:615–627.

22. Cheng AM, Chalam KV, Brar VS, Yang DT, Bhatt J, Banoub RG, et al. Recent advances in imaging macular atrophy for late-stage age-related macular degeneration. Diagnostics (Basel) 2023;13:3635.

23. Holomcik D, Seeböck P, Gerendas BS, Mylonas G, Najeeb BH, Schmidt-Erfurth U, et al. Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learning. Eye (Lond) 2023;37:1439–1444.

24. Wang WC, Ahn E, Feng D, Kim J. A review of predictive and contrastive self-supervised learning for medical images. Mach Intell Res 2023;20:483–513.

25. Burlina PM, Joshi N, Pekala M, Pacheco KD, Freund DE, Bressler NM. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol 2017;135:1170–1176.

26. Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology 2018;125:1410–1420.

27. Christopher M, Belghith A, Weinreb RN, Bowd C, Goldbaum MH, Saunders LJ, et al. Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression. Invest Ophthalmol Vis Sci 2018;59:2748– 2756.

28. Asaoka R, Murata H, Iwase A, Araie M. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 2016;123:1974–1980.

29. Sample PA, Goldbaum MH, Chan K, Boden C, Lee TW, Vasile C, et al. Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields. Invest Ophthalmol Vis Sci 2002;43:2660–2665.

30. Wen JC, Lee CS, Keane PA, Xiao S, Rokem AS, Chen PP, et al. Forecasting future Humphrey Visual Fields using deep learning. PLoS One 2019;14:e0214875.

31. Kim D, Seo SB, Park SJ, Cho HK. Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients. Sci Rep 2023;13:18304.

32. Redd TK, Campbell JP, Brown JM, Kim SJ, Ostmo S, Chan RV, et al.; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol 2018;103:580–584.

33. Almadhi NH, Dow ER, Paul Chan RV, Alsulaiman SM. Multimodal imaging, tele-education, and telemedicine in retinopathy of prematurity. Middle East Afr J Ophthalmol 2022;29:38–50.

34. Santodomingo-Rubido J, Carracedo G, Suzaki A, Villa- Collar C, Vincent SJ, Wolffsohn JS. Keratoconus: An updated review. Cont Lens Anterior Eye 2022;45:101559.

35. Ambrósio R Jr, Randleman JB. Screening for ectasia risk: What are we screening for and how should we screen for it? J Refract Surg 2013;29:230–232.

36. Ambrósio R Jr, Nogueira LP, Caldas DL, Fontes BM, Luz A, Cazal JO, et al. Evaluation of corneal shape and biomechanics before LASIK. Int Ophthalmol Clin 2011;51:11–38.

37. Ambrósio R Jr, Lopes BT, Faria-Correia F, Salomão MQ, Bühren J, Roberts CJ, et al. Integration of Scheimpflugbased corneal tomography and biomechanical assessments for enhancing ectasia detection. J Refract Surg 2017;33:434–443.

38. Mustapha A, Mohamed L, Hamid H, Ali K. Machine learning techniques in keratoconus classification: A systematic review. Mach Learn 2023;14.

39. Zorto AD, Sharif MS, Wall J, Brahma A, Alzahrani AI, Alalwan N. An innovative approach based on machine learning to evaluate the risk factors importance in diagnosing keratoconus. Inform Med Unlocked 2023;38:101208.

40. Arbelaez MC, Versaci F, Vestri G, Barboni P, Savini G. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology 2012;119:2231–2238.

41. Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach. Invest Ophthalmol Vis Sci 1997;38:2290–2299.

42. Lavric A, Valentin P. KeratoDetect: Keratoconus detection algorithm using convolutional neural networks. Comput Intel Neurosc 2019;2019. https://doi.org/10.1155/2019/8162567

43. Lopes BT, Ramos IC, Salomão MQ, Guerra FP, Schallhorn SC, Schallhorn JM, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am J Ophthalmol 2018;195:223–232.

44. Herber R, Pillunat LE, Raiskup F. Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity. Eye Vis (Lond) 2021;8:21.

45. Ferreira-Mendes J, Lopes BT, Faria-Correia F, Salomão MQ, Rodrigues-Barros S, Ambrósio R Jr. Enhanced ectasia detection using corneal tomography and biomechanics. Am J Ophthalmol 2019;197:7–16.

46. Ambrósio R Jr, Machado AP, Leão E, Lyra JM, Salomão MQ, Esporcatte LG, et al. Optimized artificial intelligence for enhanced ectasia detection using Scheimpflug-based corneal tomography and biomechanical data. Am J Ophthalmol 2023;251:126–142.

47. Craig JP, Nichols KK, Akpek EK, Caffery B, Dua HS, Joo CK, et al. TFOS DEWS II definition and classification report. Ocul Surf 2017;15:276–283.

48. Bron AJ, de Paiva CS, Chauhan SK, Bonini S, Gabison EE, Jain S, et al. TFOS DEWS II pathophysiology report. Ocul Surf 2017;15:438–510.

49. Farrand KF, Fridman M, Stillman IÖ, Schaumberg DA. Prevalence of diagnosed dry eye disease in the United States among adults aged 18 years and older. Am J Ophthalmol 2017;182:90–98.

50. Miljanović B, Dana R, Sullivan DA, Schaumberg DA. Impact of dry eye syndrome on vision-related quality of life. Am J Ophathalmol 2007;143:409–415.e2.

51. Tsai CY, Jiesisibieke ZL, Tung TH. Association between dry eye disease and depression: An umbrella review. Front Public Health 2022;10:910608.

52. Basilious A, Xu CY, Malvankar-Mehta MS. Dry eye disease and psychiatric disorders: A systematic review and metaanalysis. Eur J Ophthalmol 2022;32:1872–1889.

53. Milner MS, Beckman KA, Luchs JI, Allen QB, Awdeh RM, Berdahl J, et al. Dysfunctional tear syndrome: Dry eye disease and associated tear film disorders - New strategies for diagnosis and treatment. Curr Opin Ophthalmol 2017;27:3–47.

54. Downie LE, Keller PR. A pragmatic approach to dry eye diagnosis: Evidence into practice. Optom Vis Sci 2015;92:1189–1197.

55. Wolffsohn JS, Arita R, Chalmers R, Djalilian A, Dogru M, Dumbleton K, et al. TFOS DEWS II diagnostic methodology report. Ocul Surf 2017;15:539–574.

56. Miller KL, Walt JG, Mink DR, Satram-Hoang S, Wilson SE, Perry HD, et al. Minimal clinically important difference for the ocular surface disease index. Arch Ophthalmol 2010;128:94–101.

57. Foulks GN, Forstot SL, Donshik PC, Forstot JZ, Goldstein MH, Lemp MA, et al. Clinical guidelines for management of dry eye associated with Sjögren disease. Ocul Surf 2015;13:118–132.

58. Versura P, Profazio V, Campos EC. Performance of tear osmolarity compared to previous diagnostic tests for dry eye diseases. Curr Eye Res 2010;35:553–564.

59. Sullivan BD, Whitmer D, Nichols KK, Tomlinson A, Foulks GN, Geerling G, et al. An objective approach to dry eye disease severity. Invest Ophthalmol Vis Sci 2010;51:6125– 6130.

60. Li S, Wang Y, Yu C, Li Q, Chang P, Wang D, et al. Unsupervised learning based on meibography enables subtyping of dry eye disease and reveals ocular surface features. Invest Ophthalmol Vis Sci 2023;64:43.

61. Wang J, Li S, Yeh TN, Chakraborty R, Graham AD, Yu SX, et al. Quantifying meibomian gland morphology using artificial intelligence. Optom Vis Sci 2021;98:1094–1103.

62. Chase C, Elsawy A, Eleiwa T, Ozcan E, Tolba M, Abou Shousha M. Comparison of autonomous AS-OCT deep learning algorithm and clinical dry eye tests in diagnosis of dry eye disease. Clin Ophthalmol 2021;15:4281–4289.

63. Jabbour S, Bower KS. Refractive surgery in the US in 2021. JAMA 2021;326:77–78.

64. Rampat R, Deshmukh R, Chen X, Ting DS, Said DG, Dua HS, et al. Artificial intelligence in cornea, refractive surgery, and cataract: Basic principles, clinical applications, and future directions. Asia Pac J Ophthalmol (Phila) 2021;10:268–281.

65. Ang M, Gatinel D, Reinstein DZ, Mertens E, Alió Del Barrio JL, Alió JL. Refractive surgery beyond 2020. Eye (Lond) 2021;35:362–382.

66. Xie Y, Zhao L, Yang X, Wu X, Yang Y, Huang X, et al. Screening candidates for refractive surgery with corneal tomographic-based deep learning. JAMA Ophthalmol 2020;138:519–526.

67. Stopyra W, Langenbucher A, Grzybowski A. Intraocular lens power calculation formulas-A systematic review. Ophthalmol Ther 2023;12:2881–2902.

68. Voytsekhivskyy OV, Hoffer KJ, Tutchenko L, Cooke DL, Savini G. Accuracy of 24 IOL power calculation methods. J Refract Surg 2023;39:249–256.

69. Wendelstein J, Hoffmann P, Hirnschall N, Fischinger IR, Mariacher S, Wingert T, et al. Project hyperopic power prediction: Accuracy of 13 different concepts for intraocular lens calculation in short eyes. Br J Ophthalmol 2022;106:795–801.

70. Gutierrez L, Lim JS, Foo LL, Ng WY, Yip M, Lim GY, et al. Application of artificial intelligence in cataract management: Current and future directions. Eye Vis (Lond) 2022;9:3.

71. Cooke DL, Cooke TL. Comparison of 9 intraocular lens power calculation formulas. J Cataract Refract Surg 2016;42:1157–1164.

72. Langenbucher A, Szentmáry N, Wendelstein J, Hoffmann P. Artificial intelligence, machine learning and calculation of intraocular lens power. Klin Monbl Augenheilkd 2020;237:1430–1437.

73. Gatinel D, Debellemanière G, Saad A, Dubois M, Rampat R. Determining the theoretical effective lens position of thick intraocular lenses for machine learning-based IOL power calculation and simulation. Transl Vis Sci Technol 2021;10:27.

74. Hashemi H, Rezvan F, Pakzad R, Ansaripour A, Heydarian S, Yekta A, et al., editors. Global and regional prevalence of diabetic retinopathy; A comprehensive systematic review and meta-analysis. Semin Ophthalmol 2022. https://dx.doi.org/10.2139/ssrn.3429928

75. Safi H, Safi S, Hafezi-Moghadam A, Ahmadieh H. Early detection of diabetic retinopathy. Surv Ophthalmol 2018;63:601–608.

76. Scanlon PH. The English national screening programme for diabetic retinopathy 2003–2016. Acta Diabetol 2017;54:515–525.

77. Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 2017;124:962–969.

78. Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal 2017;39:178–193.

79. Ting DS, Cheung CY, Lim G, Tan GS, Quang ND, Gan A, 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;318:2211–2223.

80. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402–2410.

81. Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 2016;57:5200–5206.

82. Vanessa CC, Au SC. Artificial intelligence systems for diabetic retinopathy screening: Appraisal on the 3rd US FDA approved algorithms-AEYE-DS. J Ophthalmol Adv Res 2023;4:1–3.

83. Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, et al. An automated grading system for detection of visionthreatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 2018;41:2509– 2516.

84. Ramachandran N, Hong SC, Sime MJ, Wilson GA. Diabetic retinopathy screening using deep neural network. Clin Exp Ophthalmol 2018;46:412–416.

85. Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphonebased fundus photography using artificial intelligence. Eye (Lond) 2018;32:1138–1144.

86. Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: A pilot study. Sci Rep 2018;8:4330.

87. Jayaram H, Kolko M, Friedman DS, Gazzard G. Glaucoma: Now and beyond. Lancet 2023;402:1788–1801.

88. Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology 2014;121:2081–2090.

89. Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: A review. JAMA 2014;311:1901– 1911.

90. Diaz-Pinto A, Morales S, Naranjo V, Köhler T, Mossi JM, Navea A. CNNs for automatic glaucoma assessment using fundus images: An extensive validation. Biomed Eng Online 2019;18:29.

91. Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, et al. Advanced machine learning in action: Identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 2018;1:9.

92. Tan JH, Bhandary SV, Sivaprasad S, Hagiwara Y, Bagchi A, Raghavendra U, et al. Age-related macular degeneration detection using deep convolutional neural network. Future Gener Comput Syst 2018;87:127–135.

93. Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, Sousa DC, Arnold L, Schlenker MB, et al. Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: Translation to clinical practice. Transl Vis Sci Technol 2020;9:55.

94. Orlando JI, Prokofyeva E, Del Fresno M, Blaschko MB. An ensemble deep learning based approach for red lesion detection in fundus images. Comput Methods Programs Biomed 2018;153:115–127.

95. Bellemo V, Lim G, Rim TH, Tan GS, Cheung CY, Sadda S, et al. Artificial intelligence screening for diabetic retinopathy: The real-world emerging application. Curr Diab Rep 2019;19:72.

96. Chen X, Xu Y, Wong DW, Wong TY, Liu J, editors. Glaucoma detection based on deep convolutional neural network. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015.

97. Oguz C, Aydin T, Yaganoglu M. A CNN-based hybrid model to detect glaucoma disease. Multimedia Tools Appl 2023;83:17921–17939.

98. Ting DS, Peng L, Varadarajan AV, Keane PA, Burlina PM, Chiang MF, et al. Deep learning in ophthalmology: The technical and clinical considerations. Prog Retin Eye Res 2019;72:100759.

99. Wong WL, Su X, Li X, Cheung CM, Klein R, Cheng CY, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: A systematic review and meta-analysis. Lancet Glob Health 2014;2:e106–16.

100. Al-Zamil WM, Yassin SA. Recent developments in agerelated macular degeneration: A review. Clin Interv Aging 2017;12:1313–1330.

101. Mitchell P, Liew G, Gopinath B, Wong TY. Age-related macular degeneration. Lancet 2018;392:1147–1159.

102. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res 2018;67:1–29.

103. Treder M, Lauermann JL, Eter N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol 2018;256:259–265.

104. Schmidt-Erfurth U, Waldstein SM, Klimscha S, Sadeghipour A, Hu X, Gerendas BS, et al. Prediction of individual disease conversion in early AMD using artificial intelligence. Invest Ophthalmol Vis Sci 2018;59:3199– 3208.

105. Yoo TK, Choi JY, Seo JG, Ramasubramanian B, Selvaperumal S, Kim DW. 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. Med Biol Eng Comput 2019;57:677–687.

106. Lee N, Laine AF, Smith RT, Barbazetto I, Busuoic M. Quantification of geographic atrophy in fundus autofluorescence images for diagnosis of age-related macular degeneration. Heffner Biomedical Imaging Lab. http://hbil bme columbia edu/wpcontent/themes/HBIL MJP/publications/156 pdf

107. Chakravarthy U, Goldenberg D, Young G, Havilio M, Rafaeli O, Benyamini G, et al. Automated identification of lesion activity in neovascular age-related macular degeneration. Ophthalmology 2016;123:1731–1736.

108. Kawasaki R, Yasuda M, Song SJ, Chen SJ, Jonas JB, Wang JJ, et al. The prevalence of age-related macular degeneration in Asians: A systematic review and metaanalysis. Ophthalmology 2010;117:921–927.

109. Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172:1122-31.e9.

110. Bogunović H, Montuoro A, Baratsits M, Karantonis MG, Waldstein SM, Schlanitz F, et al. Machine learning of the progression of intermediate age-related macular degeneration based on OCT imaging. Invest Ophthalmol Vis Sci 2017;58:BIO141–50.

111. Asani B, Holmberg O, Schiefelbein JB, Hafner M, Herold T, Spitzer H, et al. Evaluation of OCT biomarker changes in treatment-naive neovascular AMD using a deep semantic segmentation algorithm [Internet]. medRxiv [Preprint]. 2022. Available from: https://doi.org/10.1101/2022.06.16.22276342.

112. Maunz A, Barras L, Kawczynski MG, Dai J, Lee AY, Spaide RF, et al. Machine learning to predict response to ranibizumab in neovascular age-related macular degeneration. Ophthalmol Sci 2023;3:100319.

113. Emami-Naeini P, Garmo V, Boucher N, Fernando R, Menezes A. Maintenance of vision needed to drive after intravitreal anti-VEGF therapy in patients with neovascular AMD and diabetic macular edema. Ophthalmol Retina 2023;8:388–398.

114. Reibaldi M, Fallico M, Avitabile T, Marolo P, Parisi G, Cennamo G, et al. Frequency of intravitreal anti-VEGF injections and risk of death: A systematic review with metaanalysis. Ophthalmol Retina 2022;6:369–376.

115. Okonkwo ON, Akanbi T, Agweye CT. Current management of diabetic macular edema. Diabetic eye disease-from therapeutic pipeline to the real world: IntechOpen; 2022.

116. Yeh TC, Luo AC, Deng YS, Lee YH, Chen SJ, Chang PH, et al. Prediction of treatment outcome in neovascular agerelated macular degeneration using a novel convolutional neural network. Sci Rep 2022;12:5871.

117. Kikuchi Y, Kawczynski MG, Anegondi N, Neubert A, Dai J, Ferrara D, et al. Machine learning to predict faricimab treatment outcome in neovascular age-related macular degeneration. Ophthalmol Sci 2023;4:100385.

118. Zhou Y, Chia MA, Wagner SK, Ayhan MS, Williamson DJ, Struyven RR, et al.; UK Biobank Eye & Vision Consortium. A foundation model for generalizable disease detection from retinal images. Nature 2023;622:156–163.

119. Daich Varela M, Sen S, De Guimaraes TA, Kabiri N, Pontikos N, Balaskas K, et al. Artificial intelligence in retinal disease: Clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023;261:3283–3297.

120. Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med Educ 2023;23:689.

121. Hasani N, Farhadi F, Morris MA, Nikpanah M, Rhamim A, Xu Y, et al. Artificial intelligence in medical imaging and its impact on the rare disease community: Threats, challenges and opportunities. PET Clin 2022;17:13–29.

122. Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJ, Kann BH. Randomized clinical trials of machine learning interventions in health care: A systematic review. JAMA Network Open 2022;5:e2233946-e.

123. Müller H, Holzinger A, Plass M, Brcic L, Stumptner C, Zatloukal K. Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European in vitro diagnostic regulation. N Biotechnol 2022;70:67–72.124. Ahmed I, Jeon G, Piccialli F. From artificial intelligence to explainable artificial intelligence in industry

124. Ahmed I, Jeon G, Piccialli F. From artificial intelligence to explainable artificial intelligence in industry4.0: a survey on what, how, and where. IEEE Trans Industr Inform 2022;18:5031–5042.

125. Mulrenan C, Rhode K, Fischer BM. A literature review on the use of artificial intelligence for the diagnosis of COVID- 19 on CT and chest X-ray. Diagnostics (Basel) 2022;12:869.

126. Saw SN, Ng KH. Current challenges of implementing artificial intelligence in medical imaging. Phys Med 2022;100:12–17.

127. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: Scoping review. J Med Internet Res 2022;24:e32215.

128. Boeken T, Feydy J, Lecler A, Soyer P, Feydy A, Barat M, et al. Artificial intelligence in diagnostic and interventional radiology: Where are we now? Diagn Interv Imaging 2023;104:1–5.

129. Agrawal P, Nikhade P, Nikhade PP. Artificial intelligence in dentistry: Past, present, and future. Cureus 2022;14:e27405.

130. Grzybowski A, Rao DP, Brona P, Negiloni K, Krzywicki T, Savoy FM. Diagnostic ACCURACY OF AUTOMATED DIABETIC RETINOPATHY IMAGE ASSESSMENT SOFTWARES: IDx-DR and Medios Artificial Intelligence. Ophthalmic Res 2023;66:1286–1292.

131. Gutfleisch M, Ester O, Aydin S, Quassowski M, Spital G, Lommatzsch A, et al. Clinically applicable deep learningbased decision aids for treatment of neovascular AMD. Graefes Arch Clin Exp Ophthalmol 2022;260:2217–2230.

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