Role of artificial intelligence and machine learning in ophthalmology



John Davis Akkara1, Anju Kuriakose2

1 Department of Glaucoma, Westend Eye Hospital, Kochi; Department of Ophthalmology, Little Flower Hospital and Research Centre, Angamaly, Kerala, India

2 Department of Ophthalmology, Jubilee Mission Medical College, Thrissur, Kerala, India



Correspondence Address:

Dr. John Davis Akkara

Department of Glaucoma, Westend Eye Hospital, Kacheripady, Kochi - 682 018, Kerala

India

Source of Support: None, Conflict of Interest: None Check

DOI: 10.4103/kjo.kjo_54_19





Artificial intelligence (AI) and machine learning (ML) have entered several avenues of modern life, and health care is just one of them. Ophthalmology is a field with a lot of imaging and measurable data, thus ideal for application of AI and ML. Many of these are still in research stage, but show promising results. The ophthalmic diseases where AI is being used are diabetic retinopathy, glaucoma, age-related macular degeneration, retinopathy of prematurity, retinal vascular occlusions, keratoconus, cataract, refractive errors, retinal detachment, squint, and ocular cancers. It is also useful for intraocular lens power calculation, planning squint surgeries, and planning intravitreal antivascular endothelial growth factor injections. In addition, AI can detect cognitive impairment, dementia, Alzheimer's disease, stroke risk, and so on from fundus photographs and optical coherence tomography. We will surely see many more innovations in this rapidly growing field.



Keywords: Artificial intelligence, convolutional neural networks, deep learning, glaucoma artificial intelligence, machine learning

1.

[Full text] Akkara J, Kuriakose A. The magic of three-dimensional printing in ophthalmology. Kerala J Ophthalmol 2018;30:209-15.

2.

Mirsky Y, Mahler T, Shelef I, Elovici Y. CT-GAN: Malicious tampering of 3D medical imagery using deep learning. arXiv preprint arXiv:1901.03597. 2019. Available from: http://arxiv.org/abs/1901.03597 . [Last accessed on 2019 Jul 10].

3. et al. Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 2019;126:552-64.

Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N,Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 2019;126:552-64.

4.

[PUBMED] [Full text] Akkara JD, Kuriakose A. Commentary: Rise of machine learning and artificial intelligence in ophthalmology. Indian J Ophthalmol 2019;67:1009-10.

5.

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500-10.

6. et al. Artificial intelligence in dermatology-where we are and the way to the future: A Review. American Journal of Clinical Dermatology 2019;5:1-7.

Hogarty DT, Su JC, Phan K, Attia M, Hossny M, Nahavandi S,. Artificial intelligence in dermatology-where we are and the way to the future: A Review. American Journal of Clinical Dermatology 2019;5:1-7.

7. et al. CM-Path AI in Histopathology Working Group, Bachtiar V, Booth R. Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice. The Journal of pathology 2019.

Colling R, Pitman H, Oien K, Rajpoot N, Macklin P, Snead D,. CM-Path AI in Histopathology Working Group, Bachtiar V, Booth R. Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice. The Journal of pathology 2019.

8. et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 2019;25:433-8.

Liang H, Tsui BY, Ni H, Valentim CC, Baxter SL, Liu G,Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 2019;25:433-8.

9.

Desai GS. Artificial intelligence: The future of obstetrics and gynecology. J Obstet Gynaecol India 2018;68:326-7.

10. et al. Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology. BJR Open 2019;1:20180031.

Rattan R, Kataria T, Banerjee S, Goyal S, Gupta D, Pandita A,. Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology. BJR Open 2019;1:20180031.

11.

Gubbi S, Hamet P, Tremblay J, Koch CA, Hannah-Shmouni F. Artificial intelligence and machine learning in endocrinology and metabolism: The dawn of a new era. Front Endocrinol (Lausanne) 2019;10:185.

12. et al. Artificial intelligence in cardiology. J Am Coll Cardiol 2018;71:2668-79.

Johnson KW, Soto JT, Glicksberg BS, Shameer K, Miotto R, Ali M,. Artificial intelligence in cardiology. J Am Coll Cardiol 2018;71:2668-79.

13.

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

14. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342-50.

De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S,Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342-50.

15.

AI Holds Promise for Glaucoma, a Leading Global Cause of Blindness. IBM Research Blog; 2019. Available from: https://www.ibm.com/blogs/research/2019/05/ai-glaucoma/ . [Last accessed on 2019 Jul 12].

17. et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103:167-75.

Ting DS, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R,Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103:167-75.

18.

Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol 2019;64:233-40.

19.

Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: A review. Clin Exp Ophthalmol 2019;47:128-39.

20.

Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y. Applications of artificial intelligence in ophthalmology: General overview. J Ophthalmol 2018;2018:5278196.

21.

Rahimy E. Deep learning applications in ophthalmology. Curr Opin Ophthalmol 2018;29:254-60.

22. et al. Deep learning in ophthalmology: The technical and clinical considerations. Prog Retin Eye Res 2019. pii: S1350-9462(18)30090-9.

Ting DS, Peng L, Varadarajan AV, Keane PA, Burlina PM, Chiang MF,Deep learning in ophthalmology: The technical and clinical considerations. Prog Retin Eye Res 2019. pii: S1350-9462(18)30090-9.

23.

Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol (Phila) 2019;8:264-72.

24.

Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol 2018;11:1555-61.

25.

Leben Care Technologies – AI Imaging Diagnostics and Screening for Ophthalmology, Diabetic Retinopathy, Glaucoma, Age Related Macular Degeneration. Available from: https://www.leben.ai/ . [Last accessed on 2019 Jul 14].

27.

Medios AI- Remidio. Available from: https://www.remidio.com/medios.php . [Last accessed on 2019 Jul 14].

29.

[PUBMED] [Full text] Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic retinopathy: A natural step to the future. Indian J Ophthalmol 2019;67:1004-9.

30.

Sosale AR. Screening for diabetic retinopathy – Is the use of artificial intelligence and cost-effective fundus imaging the answer? Int J Diabetes Dev Ctries 2019;39:1-3.

31.

Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye (Lond) 2018;32:1138-44.

32. 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.

Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ,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.

33.

Takahashi H, Tampo H, Arai Y, Inoue Y, Kawashima H. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLoS One 2017;12:e0179790.

34.

Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 2016;316:2366-7.

35.

Ting DS, Carin L, Abramoff MD. Observations andlessons learned from the artificial intelligence studies for Diabetic retinopathy screening. JAMA Ophthalmology. 2019. Available from: https://jamanetwork.com/journals/jamaophthalmology/fullarticle/2734989 . [Last accessed on 2019 Jul 05].

36.

Kanagasingam Y, Xiao D, Vignarajan J, Preetham A, Tay-Kearney ML, Mehrotra A. Evaluation of Artificial intelligence–based grading of diabetic retinopathy in primary care. JAMA Netw Open 2018;1:e182665.

37.

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.

38.

[PUBMED] [Full text] Raju B, Raju NS, Akkara JD, Pathengay A. Do it yourself smartphone fundus camera – DIYretCAM. Indian J Ophthalmol 2016;64:663-7.

39.

[PUBMED] [Full text] Chandrakanth P, Ravichandran R, Nischal NG, Subhashini M. Trash to treasure retcam. Indian J Ophthalmol 2019;67:541-4.

40.

Sharma A, Subramaniam SD, Ramachandran KI, Lakshmikanthan C, Krishna S, Sundaramoorthy SK. Smartphone-based fundus camera device (MII ret cam) and technique with ability to image peripheral retina. Eur J Ophthalmol 2016;26:142-4.

41. et al. 51-OR: Medios – A smartphone-based artificial intelligence algorithm in screening for diabetic retinopathy. Diabetes 2019;68 Suppl 1:51.

Sosale B, Sosale AR, Murthy H, Narayana S, Sharma U, Gowda SG,. 51-OR: Medios – A smartphone-based artificial intelligence algorithm in screening for diabetic retinopathy. Diabetes 2019;68 Suppl 1:51.

42.

Kapoor R, Whigham BT, Al-Aswad LA. The role of artificial intelligence in the diagnosis and management of glaucoma. Curr Ophthalmol Rep 2019;7:136-42.

43.

Zheng C, Johnson TV, Garg A, Boland MV. Artificial intelligence in glaucoma. Curr Opin Ophthalmol 2019;30:97-103.

44. et al. Use of machine learning on contact lens sensor-derived parameters for the diagnosis of primary open-angle glaucoma. Am J Ophthalmol 2018;194:46-53.

Martin KR, Mansouri K, Weinreb RN, Wasilewicz R, Gisler C, Hennebert J,Use of machine learning on contact lens sensor-derived parameters for the diagnosis of primary open-angle glaucoma. Am J Ophthalmol 2018;194:46-53.

45. et al. Automated anterior segment OCT image analysis for angle closure glaucoma mechanisms classification. Comput Methods Programs Biomed 2016;130:65-75.

Niwas SI, Lin W, Bai X, Kwoh CK, Jay Kuo CC, Sng CC,Automated anterior segment OCT image analysis for angle closure glaucoma mechanisms classification. Comput Methods Programs Biomed 2016;130:65-75.

46.

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;125:1199-206.

47. et al. Evaluation of a deep learning system for identifying glaucomatous optic neuropathy based on color fundus photographs. Journal of glaucoma 2019.

Al-Aswad LA, Kapoor R, Chu CK, Walters S, Gong D, Garg A,. Evaluation of a deep learning system for identifying glaucomatous optic neuropathy based on color fundus photographs. Journal of glaucoma 2019.

48.

Cerentini A, Welfer D, Cordeiro d'Ornellas M, Pereira Haygert CJ, Dotto GN. Automatic identification of glaucoma using deep learning methods. Stud Health Technol Inform 2017;245:318-21.

49. et al. A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. J Med Syst 2017;42:20.

Haleem MS, Han L, Hemert JV, Li B, Fleming A, Pasquale LR,A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. J Med Syst 2017;42:20.

50.

Thompson AC, Jammal AA, Medeiros FA. A deep learning algorithm to quantify neuroretinal rim loss from optic disc photographs. Am J Ophthalmol 2019;201:9-18.

51. et al. Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma 2017;26:1086-94.

Muhammad H, Fuchs TJ, De Cuir N, De Moraes CG, Blumberg DM, Liebmann JM,Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma 2017;26:1086-94.

52. et al. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol 2019;198:136-45.

Asaoka R, Murata H, Hirasawa K, Fujino Y, Matsuura M, Miki A,Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol 2019;198:136-45.

53. 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;59:2748-56.

Christopher M, Belghith A, Weinreb RN, Bowd C, Goldbaum MH, Saunders LJ,Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression. Invest Ophthalmol Vis Sci 2018;59:2748-56.

54.

Barella KA, Costa VP, Gonçalves Vidotti V, Silva FR, Dias M, Gomi ES. Glaucoma diagnostic accuracy of machine learning classifiers using retinal nerve fiber layer and optic nerve data from SD-OCT. J Ophthalmol 2013;2013:789129.

55.

Bizios D, Heijl A, Hougaard JL, Bengtsson B. Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by stratus OCT. Acta Ophthalmol 2010;88:44-52.

56.

Larrosa JM, Polo V, Ferreras A, García-Martín E, Calvo P, Pablo LE. Neural network analysis of different segmentation strategies of nerve fiber layer assessment for glaucoma diagnosis. J Glaucoma 2015;24:672-8.

57.

Asaoka R, Murata H, Iwase A, Araie M. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 2016;123:1974-80.

58. et al. Automatic differentiation of glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network. BMC Med Imaging 2018;18:35.

Li F, Wang Z, Qu G, Song D, Yuan Y, Xu Y,Automatic differentiation of glaucoma visual field from non-glaucoma visual filed using deep convolutional neural network. BMC Med Imaging 2018;18:35.

59. et al. Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 2005;46:3676-83.

Goldbaum MH, Sample PA, Zhang Z, Chan K, Hao J, Lee TW,Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects. Invest Ophthalmol Vis Sci 2005;46:3676-83.

60.

Andersson S, Heijl A, Bizios D, Bengtsson B. Comparison of clinicians and an artificial neural network regarding accuracy and certainty in performance of visual field assessment for the diagnosis of glaucoma. Acta Ophthalmol 2013;91:413-7.

61. et al. Glaucomatous patterns in frequency doubling technology (FDT) perimetry data identified by unsupervised machine learning classifiers. PLoS One 2014;9:e85941.

Bowd C, Weinreb RN, Balasubramanian M, Lee I, Jang G, Yousefi S,Glaucomatous patterns in frequency doubling technology (FDT) perimetry data identified by unsupervised machine learning classifiers. PLoS One 2014;9:e85941.

62. et al. Progression of Patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields. Invest Ophthalmol Vis Sci 2012;53:6557-67.

Goldbaum MH, Lee I, Jang G, Balasubramanian M, Sample PA, Weinreb RN,Progression of Patterns (POP): A machine classifier algorithm to identify glaucoma progression in visual fields. Invest Ophthalmol Vis Sci 2012;53:6557-67.

63. et al. Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol 2018;193:71-9.

Yousefi S, Kiwaki T, Zheng Y, Sugiura H, Asaoka R, Murata H,Detection of longitudinal visual field progression in glaucoma using machine learning. Am J Ophthalmol 2018;193:71-9.

64.

[Full text] Akkara JD, Kuriakose A. Review of recent innovations in ophthalmology. Kerala J Ophthalmol 2018;30:54.

65. et al. Forecasting future Humphrey visual fields using deep learning. PLoS One 2019;14:e0214875.

Wen JC, Lee CS, Keane PA, Xiao S, Rokem AS, Chen PP,Forecasting future Humphrey visual fields using deep learning. PLoS One 2019;14:e0214875.

66.

Kazemian P, Lavieri MS, Van Oyen MP, Andrews C, Stein JD. Personalized prediction of glaucoma progression under different target intraocular pressure levels using filtered forecasting methods. Ophthalmology 2018;125:569-77.

67. et al. Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics; 2018. p. 105790Q. Available from:

Brown JM, Campbell JP, Beers A, Chang K, Donohue K, Ostmo S,. Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning. In: Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics; 2018. p. 105790Q. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10579/105790Q/Fully-automated-disease-severity-assessment-and-treatment-monitoring-in-retinopathy/10.1117/12.2295942.short . [Last accessed on 2019 Jul 13].

68.

Worrall DE, Wilson CM, Brostow GJ. Automated retinopathy of prematurity case detection with convolutional neural networks. Deep Learning and Data Labeling for Medical Applications. Springer, Cham; 2016. p. 68-76.

69. et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 2018;136:803-10.

Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP,Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 2018;136:803-10.

70. et al. Evaluation of screening for retinopathy of prematurity by ROPtool or a lay reader. Ophthalmology 2016;123:385-90.

Abbey AM, Besirli CG, Musch DC, Andrews CA, Capone A Jr., Drenser KA,Evaluation of screening for retinopathy of prematurity by ROPtool or a lay reader. Ophthalmology 2016;123:385-90.

71.

Gelman R, Martinez-Perez ME, Vanderveen DK, Moskowitz A, Fulton AB. Diagnosis of plus disease in retinopathy of prematurity using retinal image multiScale analysis. Invest Ophthalmol Vis Sci 2005;46:4734-8.

72. et al. Computerized analysis of retinal vessel width and tortuosity in premature infants. Invest Ophthalmol Vis Sci 2008;49:3577-85.

Wilson CM, Cocker KD, Moseley MJ, Paterson C, Clay ST, Schulenburg WE,Computerized analysis of retinal vessel width and tortuosity in premature infants. Invest Ophthalmol Vis Sci 2008;49:3577-85.

73. et al. Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis. JAMA Ophthalmol 2016;134:651-7.

Campbell JP, Ataer-Cansizoglu E, Bolon-Canedo V, Bozkurt A, Erdogmus D, Kalpathy-Cramer J,Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis. JAMA Ophthalmol 2016;134:651-7.

74. 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;318:2211-23.

Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A,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;318:2211-23.

75.

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;135:1170-6.

76. 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;125:1410-20.

Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B,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;125:1410-20.

77.

Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM. Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 2017;82:80-6.

78. et al. DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 2019;126:565-75.

Peng Y, Dharssi S, Chen Q, Keenan TD, Agrón E, Wong WT,DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 2019;126:565-75.

79.

Lee CS, Baughman DM, Lee AY. Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. Ophthalmol Retina 2017;1:322-7.

80.

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;256:259-65.

81. et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 2018;125:549-58.

Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM,Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 2018;125:549-58.

82. et al. Automated identification of lesion activity in neovascular age-related macular degeneration. Ophthalmology 2016;123:1731-6.

Chakravarthy U, Goldenberg D, Young G, Havilio M, Rafaeli O, Benyamini G,Automated identification of lesion activity in neovascular age-related macular degeneration. Ophthalmology 2016;123:1731-6.

83. et al. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina 2018;2:24-30.

Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Gerendas BS,Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol Retina 2018;2:24-30.

84. et al. Use of a neural net to model the impact of optical coherence tomography abnormalities on vision in age-related macular degeneration. Am J Ophthalmol 2018;185:94-100.

Aslam TM, Zaki HR, Mahmood S, Ali ZC, Ahmad NA, Thorell MR,Use of a neural net to model the impact of optical coherence tomography abnormalities on vision in age-related macular degeneration. Am J Ophthalmol 2018;185:94-100.

85. et al. Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 2017;58:3240-8.

Bogunovic H, Waldstein SM, Schlegl T, Langs G, Sadeghipour A, Liu X,Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest Ophthalmol Vis Sci 2017;58:3240-8.

86. et al. Deep neural network-based method for detecting central retinal vein occlusion using ultrawide-field fundus ophthalmoscopy. Journal of ophthalmology 2018. Available from:

Nagasato D, Tabuchi H, Ohsugi H, Masumoto H, Enno H, Ishitobi N,. Deep neural network-based method for detecting central retinal vein occlusion using ultrawide-field fundus ophthalmoscopy. Journal of ophthalmology 2018. Available from: https://www.hindawi.com/journals/joph/2018/1875431/ . [Last accessed on 2019 Jul 13].

87.

Zhao R, Chen Z, Chi Z. Convolutional Neural Networks for Branch Retinal Vein Occlusion recognition? In: 2015 IEEE International Conference on Information and Automation; 2015. p. 1633-6.

88.

Zhang H, Chen Z, Chi Z, Fu H. Hierarchical local binary pattern for branch retinal vein occlusion recognition with fluorescein angiography images. Electron Lett 2014;50:1902-4.

89. et al. Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning. Sci Rep 2017;7:2928.

Waldstein SM, Montuoro A, Podkowinski D, Philip AM, Gerendas BS, Bogunovic H,Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning. Sci Rep 2017;7:2928.

90. et al. Automated detection of macular diseases by optical coherence tomography and artificial intelligence machine learning of optical coherence tomography images. J Ophthalmol 2019;2019:6319581.

Kuwayama S, Ayatsuka Y, Yanagisono D, Uta T, Usui H, Kato A,Automated detection of macular diseases by optical coherence tomography and artificial intelligence machine learning of optical coherence tomography images. J Ophthalmol 2019;2019:6319581.

91. et al. Treatment potential for macular cone vision in leber congenital amaurosis due to CEP290 or NPHP5 mutations: Predictions from artificial intelligence. Invest Ophthalmol Vis Sci 2019;60:2551-62.

Sumaroka A, Garafalo AV, Semenov EP, Sheplock R, Krishnan AK, Roman AJ,Treatment potential for macular cone vision in leber congenital amaurosis due to CEP290 or NPHP5 mutations: Predictions from artificial intelligence. Invest Ophthalmol Vis Sci 2019;60:2551-62.

92.

Odaibo SG, MomPremier M, Hwang RY, Yousuf S, Williams S, Grant J. Mobile artificial intelligence technology for detecting macula edema and subretinal fluid on OCT scans: Initial results from the DATUM alpha Study. arXiv preprint arXiv:1902.02905. 2019. Available from: http://arxiv.org/abs/1902.02905 . [Last accessed on 2019 Jul 05].

93.

Ohsugi H, Tabuchi H, Enno H, Ishitobi N. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment. Sci Rep 2017;7:9425.

94. et al. Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy. Biomed Opt Express 2017;8:4061-76.

Xu Y, Yan K, Kim J, Wang X, Li C, Su L,Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy. Biomed Opt Express 2017;8:4061-76.

95.

Saad A, Gatinel D. Topographic and tomographic properties of forme fruste keratoconus corneas. Invest Ophthalmol Vis Sci 2010;51:5546-55.

96. et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg 2016;42:275-83.

Kovács I, Miháltz K, Kránitz K, Juhász É, Takács Á, Dienes L,Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg 2016;42:275-83.

97.

Klyce SD. The future of keratoconus screening with artificial intelligence. Ophthalmology 2018;125:1872-3.

98. et al. Evaluation of a machine-learning classifier for keratoconus detection based on Scheimpflug tomography. Cornea 2016;35:827-32.

Ruiz Hidalgo I, Rodriguez P, Rozema JJ, Ní Dhubhghaill S, Zakaria N, Tassignon MJ,Evaluation of a machine-learning classifier for keratoconus detection based on Scheimpflug tomography. Cornea 2016;35:827-32.

99.

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;119:2231-8.

100.

Souza MB, Medeiros FW, Souza DB, Garcia R, Alves MR. Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations. Clinics (Sao Paulo) 2010;65:1223-8.

101. et al. Detection of subclinical keratoconus using an automated decision tree classification. Am J Ophthalmol 2013;156:237-460.

Smadja D, Touboul D, Cohen A, Doveh E, Santhiago MR, Mello GR,Detection of subclinical keratoconus using an automated decision tree classification. Am J Ophthalmol 2013;156:237-460.

102.

Maeda N, Klyce SD, Smolek MK, Thompson HW. Automated keratoconus screening with corneal topography analysis. Invest Ophthalmol Vis Sci 1994;35:2749-57.

103. et al. Integration of Scheimpflug-based corneal tomography and biomechanical assessments for enhancing ectasia detection. J Refract Surg 2017;33:434-43.

Ambrósio R Jr., Lopes BT, Faria-Correia F, Salomão MQ, Bühren J, Roberts CJ,Integration of Scheimpflug-based corneal tomography and biomechanical assessments for enhancing ectasia detection. J Refract Surg 2017;33:434-43.

104.

Sharif MS, Qahwaji R, Ipson S, Brahma A. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Appl Soft Comput 2015;36:269-82.

105.

Mahesh K, Gunasundari R. Computer-aided diagnosis of anterior segment eye abnormalities using visible wavelength image analysis based machine learning. Journal of medical systems 2018;42:128.

106.

Gao X, Lin S, Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 2015;62:2693-701.

107. In vivo automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Trans Biomed Eng 2016;63:2326-35.

Caixinha M, Amaro J, Santos M, Perdigao F, Gomes M, Santos J.automatic nuclear cataract detection and classification in an animal model by ultrasounds. IEEE Trans Biomed Eng 2016;63:2326-35.

108. et al. Exploiting ensemble learning for automatic cataract detection and grading. Comput Methods Programs Biomed 2016;124:45-57.

Yang JJ, Li J, Shen R, Zeng Y, He J, Bi J,Exploiting ensemble learning for automatic cataract detection and grading. Comput Methods Programs Biomed 2016;124:45-57.

109. et al. Automatic cataract detection and grading using Deep Convolutional Neural Network. In: 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC); 2017. p. 60-5.

Zhang L, Li J, Zhang I, Han H, Liu B, Yang J,. Automatic cataract detection and grading using Deep Convolutional Neural Network. In: 2017 IEEE 14International Conference on Networking, Sensing and Control (ICNSC); 2017. p. 60-5.

110. et al. Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification. J Cataract Refract Surg 2012;38:403-8.

Mohammadi SF, Sabbaghi M, Z-Mehrjardi H, Hashemi H, Alizadeh S, Majdi M,Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification. J Cataract Refract Surg 2012;38:403-8.

111.

Gillner M, Eppig T, Langenbucher A. Automatic intraocular lens segmentation and detection in optical coherence tomography images. Z Med Phys 2014;24:104-11.

112. et al. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One 2017;12:e0168606.

Liu X, Jiang J, Zhang K, Long E, Cui J, Zhu M,Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PLoS One 2017;12:e0168606.

113. et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng 2017;1:24.

Long E, Lin H, Liu Z, Wu X, Wang L, Jiang J,. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng 2017;1:24.

114. et al. Prediction of postoperative complications of pediatric cataract patients using data mining. J Transl Med 2019;17:2.

Zhang K, Liu X, Jiang J, Li W, Wang S, Liu L,Prediction of postoperative complications of pediatric cataract patients using data mining. J Transl Med 2019;17:2.

115.

Almeida JD, Silva AC, Paiva AC, Teixeira JA. Computational methodology for automatic detection of strabismus in digital images through Hirschberg test. Comput Biol Med 2012;42:135-46.

116.

Reid JE, Eaton E. Artificial Intelligence for Pediatric Ophthalmology. ArXiv preprint arXiv:1904.08796. 2019. Available from: http://arxiv.org/abs/1904.08796 . [Last accessed on 2019 Jul 05].

117.

Asensio-Sánchez VM, Díaz-Cabanas L, Martín-Prieto A. Photoleukocoria with smartphone photographs. Int Med Case Rep J 2018;11:117-9.

118.

Rivas-Perea P, Baker E, Hamerly G, Shaw BF. Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings. BMC Ophthalmol 2014;14:110.

119.

Almeida JD, Silva AC, Teixeira JA, Paiva AC, Gattass M. Surgical planning for horizontal strabismus using support vector regression. Comput Biol Med 2015;63:178-86.

120.

Habibalahi A, Bala C, Allende A, Anwer AG, Goldys EM. Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence imaging. Ocul Surf 2019. pii: S1542-0124 (18) 30284-2.

121.

Tan E, Lin F, Sheck L, Salmon P, Ng S. A practical decision-tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision. J Eur Acad Dermatol Venereol 2017;31:717-23.

122.

Das AV, Verkicharla P, Kekunnaya R, Gullapalli R. Prediction of myopia and refractive error progression in children using machine learning – A study. Artif Intell Med 2017. Available from: https://ai-med.io/ dt_team/prediction-of-myopia-and-refractive-error-progression-in-chil dren-using-machine-learning-a-study/ . [Last accessed on 2019 Jul 14].

123. et al. Validating the accuracy of a model to predict the onset of myopia in children. Invest Ophthalmol Vis Sci 2011;52:5836-41.

Zhang M, Gazzard G, Fu Z, Li L, Chen B, Saw SM,Validating the accuracy of a model to predict the onset of myopia in children. Invest Ophthalmol Vis Sci 2011;52:5836-41.

124. 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.

Lin H, Long E, Ding X, Diao H, Chen Z, Liu R,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.

125. et al. Deep learning for predicting refractive error from retinal fundus images. Invest Ophthalmol Vis Sci 2018;59:2861-8.

Varadarajan AV, Poplin R, Blumer K, Angermueller C, Ledsam J, Chopra R,Deep learning for predicting refractive error from retinal fundus images. Invest Ophthalmol Vis Sci 2018;59:2861-8.

126. et al. Detection of pathological myopia by PAMELA with texture-based features through an SVM approach. Journal of Healthcare Engineering. 2010;1:1. Available from:

Liu J, Wong DW, Lim JH, Tan NM, Zhang Z, Li H,. Detection of pathological myopia by PAMELA with texture-based features through an SVM approach. Journal of Healthcare Engineering. 2010;1:1. Available from: https://www.hindawi.com/journals/jhe/2010/657574/abs/ . [Last accessed on 2019 Jul 14].

127. et al. Automatic diagnosis of pathological myopia from heterogeneous biomedical data. PLoS One 2013;8:e65736.

Zhang Z, Xu Y, Liu J, Wong DW, Kwoh CK, Saw SM,Automatic diagnosis of pathological myopia from heterogeneous biomedical data. PLoS One 2013;8:e65736.

128.

Koprowski R, Lanza M, Irregolare C. Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks. Biomed Eng Online 2016;15:121.

129.

Hill W. Hill-RBF Calculator for IOL Power Calculations. Available from: https://rbfcalculator.com/online/ . [Last accessed on 2019 Jul 14].

130.

Siddiqui AA, Juthani V, Kang J, Chuck RS. The future of intraocular lens calculations: Ladas Super Formula. Annals of Eye Science 2019;4. Available from: http://aes.amegroups.com/article/view/4812 . [Last accessed on 2019 Jul 14].

131.

Ladas JG, Siddiqui AA, Devgan U, Jun AS. A 3-D “Super surface” combining modern intraocular lens formulas to generate a “Super formula” and maximize accuracy. JAMA Ophthalmol 2015;133:1431-6.

132.

Clarke GP, Burmeister J. Comparison of intraocular lens computations using a neural network versus the Holladay formula. J Cataract Refract Surg 1997;23:1585-9.

133.

Yarmahmoodi M, Arabalibeik H, Mokhtaran M, Shojaei A. Intraocular lens power formula selection using support vector machines. Front Biomed Technol 2015;2:36-44.

134.

Sramka M, Slovak M, Tuckova J, Stodulka P. Improving clinical refractive results of cataract surgery by machine learning. PeerJ 2019;7:e7202.

135.

Findl O, Struhal W, Dorffner G, Drexler W. Analysis of nonlinear systems to estimate intraocular lens position after cataract surgery. J Cataract Refract Surg 2004;30:863-6.

136.

Kane JX, Van Heerden A, Atik A, Petsoglou C. Accuracy of 3 new methods for intraocular lens power selection. J Cataract Refract Surg 2017;43:333-9.

137.

Zafar S, McCormick J, Giancardo L, Saidha S, Abraham A, Channa R. Retinal imaging for neurological diseases: “A window into the brain”. Int Ophthalmol Clin 2019;59:137-54.

138.

Dumitrascu OM, Qureshi TA. Retinal vascular imaging in vascular cognitive impairment: Current and future perspectives. J Exp Neurosci 2018;12:1179069518801291.

139.

Chan VT, Wong PP, Cheung CY. Retinal vascular changes in diabetes and dementia. Diabet Retin Cardiovasc Dis 2019;27:86-99.

140.

Cheung CY, Chan VT, Mok VC, Chen C, Wong TY. Potential retinal biomarkers for dementia: What is new? Curr Opin Neurol 2019;32:82-91.

141.

Cheung CY, Chen C, Wong TY. Ocular fundus photography as a tool to study stroke and dementia. Semin Neurol 2015;35:481-90.

142. et al. Microvascular network alterations in the retina of patients with Alzheimer's disease. Alzheimers Dement 2014;10:135-42.

Cheung CY, Ong YT, Ikram MK, Ong SY, Li X, Hilal S,Microvascular network alterations in the retina of patients with Alzheimer's disease. Alzheimers Dement 2014;10:135-42.

143.

Sandeep C, Kumar AS. WN segmentation of retina images for the early diagnosis of Alzheimer's disease (AD). Anal Pharm Res 2018;7:2. Available from: http://medcraveonline.com/JAPLR/JAPLR-07-00225 . pdf. [Last accessed on 2019 Jul 14].

144.

Cabrera DeBuc D, Arthur E. Recent Developments of Retinal Image Analysis in Alzheimer's Disease and Potential AI Applications. In: Carneiro G, You S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science. Springer, Cham: Springer International Publishing; 2019;11367:261-75.

145.

Zhou Q, Sinai MJ, Moore JC, Wong W. Method and system for detecting the effects of Alzheimer's disease in the human retina. US6988995B2; 2006. Available from: https://patents.google.com/patent/US6988995B2/en . [Last accessed on 2019 Jul 14].

146. et al. Retinal image analytics detects white matter hyperintensities in healthy adults. Ann Clin Transl Neurol 2019;6:98-105.

Lau AY, Mok V, Lee J, Fan Y, Zeng J, Lam B,Retinal image analytics detects white matter hyperintensities in healthy adults. Ann Clin Transl Neurol 2019;6:98-105.

147.

Korot E, Wood E, Weiner A, Sim DA, Trese M. A renaissance of teleophthalmology through artificial intelligence. Eye (Lond) 2019;33:861-3.