Artificial intelligence in retina

Progress in Retinal and Eye Research

Volumn 67, Issue , 2018, Pages 1-29

  Schmidt Erfurth, Ursula ^a   Sadeghipour, Amir ^a   Gerendas, Bianca S ^a   Waldstein, Sebastian M ^a   Bogunović, Hrvoje ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

Artificial intelligence (AI); Automated screening; Deep learning (DL); Machine learning (ML); Personalized healthcare (PHC); Prognosis and prediction

Indexed keywords

ANATOMICAL CONCEPTS; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CLINICAL CLASSIFICATION; CLINICAL OUTCOME; CLINICAL PRACTICE; DEEP LEARNING; DIABETIC RETINOPATHY; DISEASE ACTIVITY; GLAUCOMA; HEALTH CARE; HUMAN; IMAGE SEGMENTATION; LEARNING; MACHINE LEARNING; MATHEMATICAL MODEL; OPHTHALMOLOGIST; OPHTHALMOLOGY; PATTERN RECOGNITION; PERSONALIZED HEALTHCARE; PERSONALIZED MEDICINE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; RECEIVER OPERATING CHARACTERISTIC; RETINA; RETINA IMAGE; RETROLENTAL FIBROPLASIA; REVIEW; SENSITIVITY AND SPECIFICITY; SYSTEMIC DISEASE; THERAPEUTIC ERROR; THERAPY EFFECT; VISUAL ACUITY; DIAGNOSTIC IMAGING; NERVE CELL NETWORK; REPRODUCIBILITY; RETINA DISEASE; VISUAL SYSTEM EXAMINATION;

ARTIFICIAL INTELLIGENCE; DEEP LEARNING; DIAGNOSTIC TECHNIQUES, OPHTHALMOLOGICAL; HUMANS; NERVE NET; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; RETINAL DISEASES;

EID: 85051146019     PISSN: 13509462     EISSN: 18731635     Source Type: Journal    
DOI: 10.1016/j.preteyeres.2018.07.004     Document Type: Review

References (193)

1. Abdelfattah, N.S., Zhang, H., Boyer, D.S., Rosenfeld, P.J., Feuer, W.J., Gregori, G., Sadda, S.R., Drusen volume as a predictor of disease progression in patients with late age-related macular degeneration in the fellow eye. Invest. Ophthalmol. Vis. Sci. 57 (2016), 1839–1846.
2. Abramoff, M.D., Folk, J.C., Han, D.P., Walker, J.D., Williams, D.F., Russell, S.R., Massin, P., Cochener, B., Gain, P., Tang, L., Lamard, M., Moga, D.C., Quellec, G., Niemeijer, M., Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131 (2013), 351–357.
3. Abramoff, M.D., Garvin, M.K., Sonka, M., Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3 (2010), 169–208.
4. Abramoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C., Niemeijer, M., Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57 (2016), 5200–5206.
5. Adhi, M., Duker, J.S., Optical coherence tomography–current and future applications. Curr. Opin. Ophthalmol. 24 (2013), 213–221.
6. Al-Bander, B., Al-Nuaimy, W., Williams, B.M., Zheng, Y., Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc. Biomed. Signal Process Contr. 40 (2018), 91–101.
7. Allam, A.M.N., Youssif, A.A.-H., Ghalwash, A.Z., Segmentation of exudates via color-based K-means clustering and statistical-based thresholding. J. Comput. Sci. 13 (2017), 524–536.
8. Alsaih, K., Lemaitre, G., Rastgoo, M., Massich, J., Sidibé, D., Meriaudeau, F., Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed. Eng. Online, 16, 2017, 68.
9. Arnold, J.J., Markey, C.M., Kurstjens, N.P., Guymer, R.H., The role of sub-retinal fluid in determining treatment outcomes in patients with neovascular age-related macular degeneration–a phase IV randomised clinical trial with ranibizumab: the FLUID study. BMC Ophthalmol., 16, 2016, 31.
10. Ataer-Cansizoglu, E., Bolon-Canedo, V., Campbell, J.P., Bozkurt, A., Erdogmus, D., Kalpathy-Cramer, J., Patel, S., Jonas, K., Chan, R.V.P., Ostmo, S., Chiang, M.F., on behalf of the i, R.O.P.R.C., Computer-based image analysis for plus disease diagnosis in retinopathy of prematurity: performance of the “i-ROP” system and image features associated with expert diagnosis. Translat. Vis. Sci. Technol., 4, 2015, 5.
11. Avati, A., Jung, K., Harman, S., Downing, L., Ng, A., Shah, N.H., Improving palliative care with deep learning. IEEE, (eds.) IEEE International Conference on Bioinformatics and Biomedicine 2017, 2017.
12. Balthazar, P., Harri, P., Prater, A., Safdar, N.M., Protecting your patients' interests in the era of big data, artificial intelligence, and predictive analytics. J. Am. Coll. Radiol. 15 (2018), 580–586.
13. Baxt, W.G., Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 115 (1991), 843–848.
14. Beam, A.L., Kohane, I.S., Big data and machine learning in health care. J. Am. Med. Assoc. 319 (2018), 1317–1318.
15. Becker, A.S., Marcon, M., Ghafoor, S., Wurnig, M.C., Frauenfelder, T., Boss, A., Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest. Radiol. 52 (2017), 434–440.
16. Bogunovic, H., Kwon, Y.H., Rashid, A., Lee, K., Critser, D.B., Garvin, M.K., Sonka, M., Abramoff, M.D., Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma. Invest. Ophthalmol. Vis. Sci. 56 (2014), 259–271.
17. Bogunovic, H., Montuoro, A., Baratsits, M., Karantonis, M.G., Waldstein, S.M., Schlanitz, F., Schmidt-Erfurth, U., Machine learning of the progression of intermediate age-related macular degeneration based on OCT imaging. Invest. Ophthalmol. Vis. Sci. 58 (2017), BIO141–BIO150.
18. Bogunovic, H., Waldstein, S.M., Sadeghipour, A., Gerendas, B.S., Schmidt-Erfurth, U., Artificial intelligence to predict optimal retreatment intervals in treat-and-extend anti-VEGF therapy. Invest. Ophthalmol. Vis. Sci., 59, 2018, 1620 (ARVO Annual Meeting Abstract).
19. Bogunovic, H., Waldstein, S.M., Schlegl, T., Langs, G., Sadeghipour, A., Liu, X., Gerendas, B.S., Osborne, A., Schmidt-Erfurth, U., Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach. Invest. Ophthalmol. Vis. Sci. 58 (2017), 3240–3248.
20. Bowd, C., Hao, J., Tavares, I.M., Medeiros, F.A., Zangwill, L.M., Lee, T.-W., Sample, P.A., Weinreb, R.N., Goldbaum, M.H., Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes. Invest. Ophthalmol. Vis. Sci. 49 (2008), 945–953.
21. Boyer, D.S., Schmidt-Erfurth, U., van Lookeren Campagne, M., Henry, E.C., Brittain, C., The pathophysiology of geographic atrophy secondary to age-related macular degeneration and the complement pathway as a therapeutic target. Retina 37 (2017), 819–835.
22. Breger, A., Ehler, M., Bogunovic, H., Waldstein, S.M., Philip, A.M., Schmidt-Erfurth, U., Gerendas, B.S., Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images. Eye 31 (2017), 1212–1220.
23. Browning, D.J., Glassman, A.R., Aiello, L.P., Beck, R.W., Brown, D.M., Fong, D.S., Bressler, N.M., Danis, R.P., Kinyoun, J.L., Nguyen, Q.D., Bhavsar, A.R., Gottlieb, J., Pieramici, D.J., Rauser, M.E., Apte, R.S., Lim, J.I., Miskala, P.H., Relationship between optical coherence tomography-measured central retinal thickness and visual acuity in diabetic macular edema. Ophthalmology 114 (2007), 525–536.
24. Buchanan, B.G., Shortliffe, E.H., Rule-based Expert Systems: the Mycin Experiments of the stanford Heuristic Programming Project. 1984, USC/Information Sciences Institute, Marina del Rey, CA 90292, U.S.A.
25. Burlina, P.M., Joshi, N., Pekala, M., Pacheco, K.D., Freund, D.E., Bressler, N.M., Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 135 (2017), 1170–1176.
26. Busbee, B.G., Ho, A.C., Brown, D.M., Heier, J.S., Suner, I.J., Li, Z., Rubio, R.G., Lai, P., Twelve-month efficacy and safety of 0.5 mg or 2.0 mg ranibizumab in patients with subfoveal neovascular age-related macular degeneration. Ophthalmology 120 (2013), 1046–1056.
27. Bzdok, D., Meyer-Lindenberg, A., Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatr. Cogn. Neurosci. Neuroimaging 3 (2018), 223–230.
28. Campbell, J.P., Ataer-Cansizoglu, E., Bolon-Canedo, V., Bozkurt, A., Erdogmus, D., Kalpathy-Cramer, J., Patel, S.N., Reynolds, J.D., Horowitz, J., Hutcheson, K., Shapiro, M., Repka, M.X., Ferrone, P., Drenser, K., Martinez-Castellanos, M.A., Ostmo, S., Jonas, K., Chan, R.V.P., Chiang, M.F., on behalf of the i, R.O.P.r.c., Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis. JAMA Ophthalmol. 134 (2016), 651–657.
29. Care, C.o.D.E.i.H., Services, B.o.H.C., Medicine, I.o., The National Academies of Sciences, E., and Medicine, Improving diagnosis in health care. Balogh, E.P., Miller, B.T., Ball, J.R., (eds.) Improving Diagnosis in Health Care, 2015, National Academies Press, Washington (DC).
30. Chakravarthy, U., Goldenberg, D., Young, G., Havilio, M., Rafaeli, O., Benyamini, G., Loewenstein, A., Automated identification of lesion activity in neovascular age-related macular degeneration. Ophthalmology 123 (2016), 1731–1736.
31. Chen, J.H., Alagappan, M., Goldstein, M.K., Asch, S.M., Altman, R.B., Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int. J. Med. Inf. 102 (2017), 71–79.
32. Chen, J.H., Asch, S.M., Machine learning and prediction in medicine - beyond the peak of inflated expectations. N. Engl. J. Med. 376 (2017), 2507–2509.
33. Chen, Q., Leng, T., Zheng, L., Kutzscher, L., Ma, J., de Sisternes, L., Rubin, D.L., Automated drusen segmentation and quantification in SD-OCT images. Med. Image Anal. 17 (2013), 1058–1072.
34. Chiu, C.J., Mitchell, P., Klein, R., Klein, B.E., Chang, M.L., Gensler, G., Taylor, A., A risk score for the prediction of advanced age-related macular degeneration: development and validation in 2 prospective cohorts. Ophthalmology 121 (2014), 1421–1427.
35. Choi, J.Y., Yoo, T.K., Seo, J.G., Kwak, J., Um, T.T., Rim, T.H., Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS One, 12, 2017, e0187336.
36. Curcio, C.A., Zanzottera, E.C., Ach, T., Balaratnasingam, C., Freund, K.B., Activated retinal pigment epithelium, an optical coherence tomography biomarker for progression in age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 58 (2017), BIO211–BIO226.
37. de Oliveira Dias, J.R., Zhang, Q., Garcia, J.M.B., Zheng, F., Motulsky, E.H., Roisman, L., Miller, A., Chen, C.L., Kubach, S., de Sisternes, L., Durbin, M.K., Feuer, W., Wang, R.K., Gregori, G., Rosenfeld, P.J., Natural history of subclinical neovascularization in nonexudative age-related macular degeneration using swept-source OCT angiography. Ophthalmology 125 (2018), 255–266.
38. de Sisternes, L., Jonna, G., Greven, M.A., Chen, Q., Leng, T., Rubin, D.L., Individual drusen segmentation and repeatability and reproducibility of their automated quantification in optical coherence tomography images. Translat. Vis. Sci. Technol., 6, 2017, 12.
39. de Sisternes, L., Simon, N., Tibshirani, R., Leng, T., Rubin, D.L., Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression. Invest. Ophthalmol. Vis. Sci. 55 (2014), 7093–7103.
40. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., ImageNet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009, IEEE, 248–255.
41. Densen, P., Challenges and opportunities facing medical education. Trans. Am. Clin. Climatol. Assoc. 122 (2011), 48–58.
42. Devalla, S.K., Chin, K.S., Mari, J.-M., Tun, T.A., Strouthidis, N.G., Aung, T., Thiéry, A.H., Girard, M.J.A., A deep learning approach to digitally stain optical coherence tomography images of the optic nerve head. Invest. Ophthalmol. Vis. Sci. 59 (2018), 63–74.
43. Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J., the, C.C., Hermsen, M., Manson, Q.F., Balkenhol, M., Geessink, O., Stathonikos, N., van Dijk, M.C., Bult, P., Beca, F., Beck, A.H., Wang, D., Khosla, A., Gargeya, R., Irshad, H., Zhong, A., Dou, Q., Li, Q., Chen, H., Lin, H.J., Heng, P.A., Hass, C., Bruni, E., Wong, Q., Halici, U., Oner, M.U., Cetin-Atalay, R., Berseth, M., Khvatkov, V., Vylegzhanin, A., Kraus, O., Shaban, M., Rajpoot, N., Awan, R., Sirinukunwattana, K., Qaiser, T., Tsang, Y.W., Tellez, D., Annuscheit, J., Hufnagl, P., Valkonen, M., Kartasalo, K., Latonen, L., Ruusuvuori, P., Liimatainen, K., Albarqouni, S., Mungal, B., George, A., Demirci, S., Navab, N., Watanabe, S., Seno, S., Takenaka, Y., Matsuda, H., Ahmady Phoulady, H., Kovalev, V., Kalinovsky, A., Liauchuk, V., Bueno, G., Fernandez-Carrobles, M.M., Serrano, I., Deniz, O., Racoceanu, D., Venancio, R., Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. J. Am. Med. Assoc. 318 (2017), 2199–2210.
44. ElTanboly, A., Ismail, M., Shalaby, A., Switala, A., El-Baz, A., Schaal, S., Gimel'farb, G., El-Azab, M., A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Med. Phys. 44 (2017), 914–923.
45. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S., Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (2017), 115–118.
46. Fan, Z., Rong, Y., Cai, X., Lu, J., Li, W., Lin, H., Chen, X., Optic disk detection in fundus image based on structured learning. IEEE J. Biomed. Health Informat. 22 (2018), 224–234.
47. Farsiu, S., Chiu, S.J., O'Connell, R.V., Folgar, F.A., Yuan, E., Izatt, J.A., Toth, C.A., Age-Related Eye Disease Study 2 Ancillary Spectral Domain Optical Coherence Tomography Study, G., Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology 121 (2014), 162–172.
48. Fatima, K.N., Hassan, T., Akram, M.U., Akhtar, M., Butt, W.H., Fully automated diagnosis of papilledema through robust extraction of vascular patterns and ocular pathology from fundus photographs. Biomed. Optic Express 8 (2017), 1005–1024.
49. Feeny, A.K., Tadarati, M., Freund, D.E., Bressler, N.M., Burlina, P., Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images. Comput. Biol. Med. 65 (2015), 124–136.
50. Fei, X., Zhao, J., Zhao, H., Yun, D., Zhang, Y., Deblurring adaptive optics retinal images using deep convolutional neural networks. Biomed. Optic Express 8 (2017), 5675–5687.
51. Figueiredo, I.N., Kumar, S., Oliveira, C.M., Ramos, J.D., Engquist, B., Automated lesion detectors in retinal fundus images. Comput. Biol. Med. 66 (2015), 47–65.
52. Fragiotta, S., Rossi, T., Cutini, A., Grenga, P.L., Vingolo, E.M., PREDICTIVE factors for development of neovascular age-related macular degeneration: a spectral-domain optical coherence tomography study. Retina 38 (2018), 245–252.
53. Freund, K.B., Korobelnik, J.-F., Devenyi, R., Framme, C., Galic, J., Herbert, E., Hoerauf, H., Lanzetta, P., Michels, S., Mitchell, P., Monés, J., Regillo, C., Tadayoni, R., Talks, J., Wolf, S., Treat-and-extend regimens with anti-VEGF agents in retinal diseases: a literature review and consensus recommendations. Retina 35 (2015), 1489–1506.
54. García, M., Sánchez, C.I., López, M.I., Abásolo, D., Hornero, R., Neural network based detection of hard exudates in retinal images. Comput. Meth. Progr. Biomed. 93 (2009), 9–19.
55. García, M., Sánchez, C.I., Poza, J., López, M.I., Hornero, R., Detection of hard exudates in retinal images using a radial basis function classifier. Ann. Biomed. Eng. 37 (2009), 1448–1463.
56. Gardner, G.G., Keating, D., Williamson, T.H., Elliott, A.T., Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. BJO (Br. J. Ophthalmol.) 80 (1996), 940–944.
57. Gargeya, R., Leng, T., Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124 (2017), 962–969.
58. GeethaRamani, R., Balasubramanian, L., Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening. Comput. Meth. Progr. Biomed. 160 (2018), 153–163.
59. Gerendas, B.S., Bogunovic, H., Sadeghipour, A., Schlegl, T., Langs, G., Waldstein, S.M., Schmidt-Erfurth, U., Computational image analysis for prognosis determination in DME. Vis. Res. 139 (2017), 204–210.
60. Gerendas, B.S., Prager, S., Deak, G., Simader, C., Lammer, J., Waldstein, S.M., Guerin, T., Kundi, M., Schmidt-Erfurth, U.M., Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema. BJO (Br. J. Ophthalmol.) 102 (2018), 195–203.
61. Gordon, E.S., Babu, D., Laney, D.A., The future is now: technology's impact on the practice of genetic counseling. Am. J. Med. Genet. C Semin. Med. Genet. 178 (2018), 15–23.
62. Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P.C., Mega, J.L., Webster, D.R., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316 (2016), 2402–2410.
63. Guo, Z., Kwon, Y.H., Lee, K., Wang, K., Wahle, A., Alward, W.L.M., Fingert, J.H., Bettis, D.I., Johnson, C.A., Garvin, M.K., Sonka, M., Abramoff, M.D., Optical coherence tomography analysis based prediction of humphrey 24-2 visual field thresholds in patients with glaucoma. Invest. Ophthalmol. Vis. Sci. 58 (2017), 3975–3985.
64. Hahnel, M., 2015 - the Year of Open Data Mandates. 2015.
65. Haleem, M.S., Han, L., Hemert, J.v., Fleming, A., Pasquale, L.R., Silva, P.S., Song, B.J., Aiello, L.P., Regional image features model for automatic classification between normal and glaucoma in fundus and scanning laser ophthalmoscopy (SLO) images. J. Med. Syst., 40, 2016, 132.
66. Haleem, M.S., Han, L., Hemert, J.v., Li, B., Fleming, A., Pasquale, L.R., Song, B.J., A novel adaptive deformable model for automated optic disc and cup segmentation to aid glaucoma diagnosis. J. Med. Syst., 42, 2017, 20.
67. Halpern, Y., Horng, S., Choi, Y., Sontag, D., Electronic medical record phenotyping using the anchor and learn framework. J. Am. Med. Inf. Assoc. 23 (2016), 731–740.
68. Han, S.S., Park, G.H., Lim, W., Kim, M.S., Na, J.I., Park, I., Chang, S.E., Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PLoS One, 13, 2018, e0191493.
69. Harangi, B., Hajdu, A., Automatic exudate detection by fusing multiple active contours and regionwise classification. Comput. Biol. Med. 54 (2014), 156–171.
70. He, K., Zhang, X., Ren, S., Sun, J., Delving Deep into Rectifiers: Surpassing Human-level Performance on ImageNet Classification. 2015.
71. He, Y., Carass, A., Jedynak, B.M., Solomon, S.D., Saidha, S., Calabresi, P.A., Prince, J.L., Topology Guaranteed Segmentation of the Human Retina from OCT Using Convolutional Neural Networks. 2018 arXiv:1803.05120 [cs].
72. Heimes, B., Schick, T., Brinkmann, C.K., Wiedon, A., Haegele, B., Kirchhof, B., Holz, F.G., Pauleikhoff, D., Ziemssen, F., Liakopoulos, S., Spital, G., Schmitz-Valckenberg, S., Design des ORCA-Moduls der OCEAN-Studie. Ophthalmologe 113 (2016), 570–580.
73. Issac, A., Partha Sarathi, M., Dutta, M.K., An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput. Meth. Progr. Biomed. 122 (2015), 229–244.
74. Ji, Z., Chen, Q., Niu, S., Leng, T., Rubin, D.L., Beyond retinal layers: a deep voting model for automated geographic atrophy segmentation in SD-OCT images. Transl Vis Sci Technol, 7, 2018, 1.
75. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., Wang, Y., Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2 (2017), 230–243.
76. Kao, E.-F., Lin, P.-C., Chou, M.-C., Jaw, T.-S., Liu, G.-C., Automated detection of fovea in fundus images based on vessel-free zone and adaptive Gaussian template. Comput. Meth. Progr. Biomed. 117 (2014), 92–103.
77. Karri, S.P., Chakraborty, D., Chatterjee, J., Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed. Optic Express 8 (2017), 579–592.
78. Kaur, J., Mittal, D., A generalized method for the segmentation of exudates from pathological retinal fundus images. Biocybernetics Biomed. Eng. 38 (2018), 27–53.
79. Keane, P.A., Grossi, C.M., Foster, P.J., Yang, Q., Reisman, C.A., Chan, K., Peto, T., Thomas, D., Patel, P.J., Consortium, U.K.B.E.V., Optical coherence tomography in the UK biobank study - rapid automated analysis of retinal thickness for large population-based studies. PLoS One, 11, 2016, e0164095.
80. Kermany, D.S., Goldbaum, M., Cai, W., Valentim, C.C.S., Liang, H., Baxter, S.L., McKeown, A., Yang, G., Wu, X., Yan, F., Dong, J., Prasadha, M.K., Pei, J., Ting, M.Y.L., Zhu, J., Li, C., Hewett, S., Dong, J., Ziyar, I., Shi, A., Zhang, R., Zheng, L., Hou, R., Shi, W., Fu, X., Duan, Y., Huu, V.A.N., Wen, C., Zhang, E.D., Zhang, C.L., Li, O., Wang, X., Singer, M.A., Sun, X., Xu, J., Tafreshi, A., Lewis, M.A., Xia, H., Zhang, K., Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172, 2018, e1129 1122-1131.
81. Khalid, S., Akram, M.U., Hassan, T., Jameel, A., Khalil, T., Automated segmentation and quantification of drusen in fundus and optical coherence tomography images for detection of ARMD. J. Digit. Imag., Dec. 4, 2017, 1–13, 10.1007/s10278-017-0038-7.
82. Kharghanian, R., Ahmadyfard, A., Retinal blood vessel segmentation using gabor wavelet and line operator. Int. J. Mach. Learn. Comput., 2012, 593–597.
83. Kim, S.J., Cho, K.J., Oh, S., Development of machine learning models for diagnosis of glaucoma. PLoS One, 12, 2017, e0177726.
84. Kluwer, W., Wolters Kluwer Health 2011 point-of-care-survey: Physicians Face Disconnects at point-of-care. 2011.
85. Ko, F., Foster, P.J., Strouthidis, N.G., Shweikh, Y., Yang, Q., Reisman, C.A., Muthy, Z.A., Chakravarthy, U., Lotery, A.J., Keane, P.A., Tufail, A., Grossi, C.M., Patel, P.J., Eye, U.K.B., Vision, C., Associations with retinal pigment epithelium thickness measures in a large cohort: results from the UK biobank. Ophthalmology 124 (2017), 105–117.
86. Koopman, C., How Democracy Can Survive Big Data, the New York Times. 2018.
87. Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G.S., Peng, L., Webster, D.R., Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy. Ophthalmology 125 (2018), 1264–1272.
88. Lakhani, P., Sundaram, B., Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284 (2017), 574–582.
89. Lang, A., Carass, A., Hauser, M., Sotirchos, E.S., Calabresi, P.A., Ying, H.S., Prince, J.L., Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Optic Express 4 (2013), 1133–1152.
90. Larson, D.B., Chen, M.C., Lungren, M.P., Halabi, S.S., Stence, N.V., Langlotz, C.P., Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287 (2018), 313–322.
91. Lawrence, M.G., The accuracy of digital-video retinal imaging to screen for diabetic retinopathy: an analysis of two digital-video retinal imaging systems using standard stereoscopic seven-field photography and dilated clinical examination as reference standards. Trans. Am. Ophthalmol. Soc. 102 (2004), 321–340.
92. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
93. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86 (1998), 2278–2324.
94. Lee, C.S., Baughman, D.M., Lee, A.Y., Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmol. Retina 1 (2017), 322–327.
95. Lee, C.S., Tyring, A.J., Deruyter, N.P., Wu, Y., Rokem, A., Lee, A.Y., Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed. Optic Express 8 (2017), 3440–3448.
96. Lee, C.S., Tyring, A.J., Wu, Y., Xiao, S., Rokem, A.S., Deruyter, N.P., Zhang, Q., Tufail, A., Wang, R.K., Lee, A.Y., Generating Perfusion Maps from Structural Optical Coherence Tomography with Artificial Intelligence. 2018 bioRxiv: 271346.
97. Lee, H., Kang, K.E., Chung, H., Kim, H.C., Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration. Am. J. Ophthalmol. 191 (2018), 64–75.
98. Lee, R., Wong, T.Y., Sabanayagam, C., Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis (Lond), 2, 2015, 17.
99. Liefers, B., Venhuizen, F.G., Schreur, V., van Ginneken, B., Hoyng, C., Fauser, S., Theelen, T., Sánchez, C.I., Automatic detection of the foveal center in optical coherence tomography. Biomed. Optic Express 8 (2017), 5160–5178.
100. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J., van Ginneken, B., Sanchez, C.I., A survey on deep learning in medical image analysis. Med. Image Anal. 42 (2017), 60–88.
101. Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G.E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P.Q., Corrado, G.S., Hipp, J.D., Peng, L., Stumpe, M.C., Detecting Cancer Metastases on Gigapixel Pathology Images. 2017 arXiv.org, arXiv:1703.02442.
102. Liu, Y.-Y., Chen, M., Ishikawa, H., Wollstein, G., Schuman, J.S., Rehg, J.M., Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Med. Image Anal. 15 (2011), 748–759.
103. Liu, Y.-Y., Ishikawa, H., Chen, M., Wollstein, G., Duker, J.S., Fujimoto, J.G., Schuman, J.S., Rehg, J.M., Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multi-scale texture and shape features. Invest. Ophthalmol. Vis. Sci. 52 (2011), 8316–8322.
104. Maclin, P.S., Dempsey, J., Brooks, J., Rand, J., Using neural networks to diagnose cancer. J. Med. Syst. 15 (1991), 11–19.
105. Mehta, H., Tufail, A., Daien, V., Lee, A.Y., Nguyen, V., Ozturk, M., Barthelmes, D., Gillies, M.C., Real-world outcomes in patients with neovascular age-related macular degeneration treated with intravitreal vascular endothelial growth factor inhibitors. Prog. Retin. Eye Res. 65 (2018), 127–146.
106. Memari, N., Ramli, A.R., Bin Saripan, M.I., Mashohor, S., Moghbel, M., Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier. PLoS One, 12, 2017.
107. Miri, M.S., Abràmoff, M.D., Kwon, Y.H., Sonka, M., Garvin, M.K., A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes. Med. Image Anal. 39 (2017), 206–217.
108. Miri, M.S., Abràmoff, M.D., Lee, K., Niemeijer, M., Wang, J.K., Kwon, Y.H., Garvin, M.K., Multimodal segmentation of optic disc and cup from SD-OCT and color fundus photographs using a machine-learning graph-based approach. IEEE Trans. Med. Imag. 34 (2015), 1854–1866.
109. Moccia, S., De Momi, E., El Hadji, S., Mattos, L.S., Blood vessel segmentation algorithms — review of methods, datasets and evaluation metrics. Comput. Meth. Progr. Biomed. 158 (2018), 71–91.
110. Molina-Casado, J.M., Carmona, E.J., García-Feijoó, J., Fast detection of the main anatomical structures in digital retinal images based on intra- and inter-structure relational knowledge. Comput. Meth. Progr. Biomed. 149 (2017), 55–68.
111. Montuoro, A., Waldstein, S.M., Gerendas, B.S., Schmidt-Erfurth, U., Bogunovic, H., Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed. Optic Express 8 (2017), 1874–1888.
112. Murakami, Y., Jain, A., Silva, R.A., Lad, E.M., Gandhi, J., Moshfeghi, D.M., Stanford university network for diagnosis of retinopathy of prematurity (SUNDROP): 12-month experience with telemedicine screening. BJO (Br. J. Ophthalmol.) 92 (2008), 1456–1460.
113. Niu, S., de Sisternes, L., Chen, Q., Rubin, D.L., Leng, T., Fully automated prediction of geographic atrophy growth using quantitative spectral-domain optical coherence tomography biomarkers. Ophthalmology 123 (2016), 1737–1750.
114. Obermeyer, Z., Emanuel, E.J., Predicting the future - big data, machine learning, and clinical medicine. N. Engl. J. Med. 375 (2016), 1216–1219.
115. Obermeyer, Z., Lee, T.H., Lost in thought - the limits of the human mind and the future of medicine. N. Engl. J. Med. 377 (2017), 1209–1211.
116. 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., 7, 2017, 9425.
117. Patel, P.J., Foster, P.J., Grossi, C.M., Keane, P.A., Ko, F., Lotery, A., Peto, T., Reisman, C.A., Strouthidis, N.G., Yang, Q., Eyes, U.K.B., Vision, C., Spectral-domain optical coherence tomography imaging in 67 321 adults: associations with macular thickness in the UK biobank study. Ophthalmology 123 (2016), 829–840.
118. Penha, F.M., Gregori, G., Garcia Filho, C.A., Yehoshua, Z., Feuer, W.J., Rosenfeld, P.J., Quantitative changes in retinal pigment epithelial detachments as a predictor for retreatment with anti-VEGF therapy. Retina 33 (2013), 459–466.
119. Pennington, K.L., DeAngelis, M.M., Epidemiology of age-related macular degeneration (AMD): associations with cardiovascular disease phenotypes and lipid factors. Eye Vis (Lond), 3, 2016, 34.
120. Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., Peng, L., Webster, D.R., Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2 (2018), 158–164.
121. Prahs, P., Radeck, V., Mayer, C., Cvetkov, Y., Cvetkova, N., Helbig, H., Märker, D., OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefe's Arch. Clin. Exp. Ophthalmol. 256 (2018), 91–98.
122. Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G.E., Irvine, J., Le, Q., Litsch, K., Mossin, A., Tansuwan, J., Wang, D., Wexler, J., Wilson, J., Ludwig, D., Volchenboum, S.L., Chou, K., Pearson, M., Madabushi, S., Shah, N.H., Butte, A.J., Howell, M.D., Cui, C., Corrado, G.S., Dean, J., Scalable and accurate deep learning with electronic health records. NPJ Digit. Med., 1, 2018, 18.
123. Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., Ng, A.Y., Cardiologist-level Arrhythmia Detection with Convolutional Neural Networks. 2017 arXiv.org, arXiv:1707.01836.
124. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K., Lungren, M.P., Ng, A.Y., CheXNet: Radiologist-level Pneumonia Detection on Chest X-rays with Deep Learning. 2017 arXiv.org, arXiv:1711.05225.
125. Relan, D., MacGillivray, T., Ballerini, L., Trucco, E., Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE. Conference proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2014, 2014, 142–145.
126. Ren, X., Zheng, Y., Zhao, Y., Luo, C., Wang, H., Lian, J., He, Y., Drusen segmentation from retinal images via supervised feature learning. IEEE Access 6 (2018), 2952–2961.
127. Rohm, M., Tresp, V., Muller, M., Kern, C., Manakov, I., Weiss, M., Sim, D.A., Priglinger, S., Keane, P.A., Kortuem, K., Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration. Ophthalmology 125 (2018), 1028–1035.
128. Ronneberger, O., Fischer, P., Brox, T., U-net: convolutional networks for biomedical image segmentation. Proceedings of Medical Image Computing and Computer-assisted Intervention (MICCAI), vol.9351, 2015, LNCS, 234–241.
129. Roy, A.G., Conjeti, S., Karri, S.P.K., Sheet, D., Katouzian, A., Wachinger, C., Navab, N., ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed. Optic Express 8 (2017), 3627–3642.
130. Rubin, D.L., Chen, Q., Leng, T., Zheng, L.L., Kutzscher, L., de Sisternes, L., Improving drusen visualization on projection images in optical coherence tomography. Ophthalmology, 120, 2013, e642 644-644.
131. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L., ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115 (2015), 211–252.
132. Russell, S.J., Norvig, P., Artificial Intelligence: a Modern Approach. 1995, Prentice Hall.
133. Sayegh, R.G., Sacu, S., Dunavolgyi, R., Kroh, M.E., Roberts, P., Mitsch, C., Montuoro, A., Ehrenmuller, M., Schmidt-Erfurth, U., Geographic atrophy and foveal-sparing changes related to visual acuity in patients with dry age-related macular degeneration over time. Am. J. Ophthalmol. 179 (2017), 118–128.
134. Schlanitz, F.G., Baumann, B., Kundi, M., Sacu, S., Baratsits, M., Scheschy, U., Shahlaee, A., Mittermuller, T.J., Montuoro, A., Roberts, P., Pircher, M., Hitzenberger, C.K., Schmidt-Erfurth, U., Drusen volume development over time and its relevance to the course of age-related macular degeneration. BJO (Br. J. Ophthalmol.) 101 (2017), 198–203.
135. Schlegl, T., Bogunovic, H., Klimscha, S., Seeboeck, P., Sadeghipour, A., Gerendas, B.S., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U., Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images. 2018 arXiv.org, arXiv:1805.03278.
136. Schlegl, T., Seeboeck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G., Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. International Conference on Information Processing in Medical Imaging, 2017, IPMI), 146–147.
137. Schlegl, T., Waldstein, S.M., Bogunovic, H., Endstraßer, F., Sadeghipour, A., Philip, A.-M., Podkowinski, D., Gerendas, B.S., Langs, G., Schmidt-Erfurth, U., Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125 (2018), 549–558.
138. Schmidt-Erfurth, U., Bogunovic, H., Sadeghipour, A., Schlegl, T., Langs, G., Gerendas, B.S., Osborne, A., Waldstein, S.M., Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol. Retina 2 (2018), 24–30.
139. Schmidt-Erfurth, U., Waldstein, S.M., A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog. Retin. Eye Res. 50 (2016), 1–24.
140. Schmidt-Erfurth, U., Waldstein, S.M., Deak, G.G., Kundi, M., Simader, C., Pigment epithelial detachment followed by retinal cystoid degeneration leads to vision loss in treatment of neovascular age-related macular degeneration. Ophthalmology 122 (2015), 822–832.
141. Schmidt-Erfurth, U., Waldstein, S.M., Klimscha, S., Sadeghipour, A., Hu, X., Gerendas, B.S., Osborne, A., Bogunovic, H., Prediction of individual disease conversion in early AMD using artificial intelligence. Invest. Ophthalmol. Vis. Sci. 59 (2018), 3199–3208.
142. Schmitz-Valckenberg, S., Göbel, A.P., Saur, S.C., Steinberg, J.S., Thiele, S., Wojek, C., Russmann, C., Holz, F.G., for the, M.-S.G., Automated retinal image analysis for evaluation of focal hyperpigmentary changes in intermediate age-related macular degeneration. Translat. Vis. Sci. Technol., 5, 2016, 3.
143. Scientific-American, World Changing Ideas 2015. 2015, Scientific American.
144. Scott, I.U., Bressler, N.M., Bressler, S.B., Browning, D.J., Chan, C.K., Danis, R.P., Davis, M.D., Kollman, C., Qin, H., Diabetic Retinopathy Clinical Research Network Study, G., Agreement between clinician and reading center gradings of diabetic retinopathy severity level at baseline in a phase 2 study of intravitreal bevacizumab for diabetic macular edema. Retina 28 (2008), 36–40.
145. Seeboeck, P., Waldstein, S.M., Klimscha, S., Gerendas, B.S., Donner, R., Schlegl, T., Schmidt-Erfurth, U., Langs, G., Identifying and Categorizing Anomalies in Retinal Imaging Data. 2016 arXiv.org, arXiv:1612.00686.
146. Shi, F., Chen, X., Zhao, H., Zhu, W., Xiang, D., Gao, E., Sonka, M., Chen, H., Automated 3-d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments. IEEE Trans. Med. Imag. 34 (2015), 441–452.
147. Sidibé, D., Sankar, S., Lemaître, G., Rastgoo, M., Massich, J., Cheung, C.Y., Tan, G.S.W., Milea, D., Lamoureux, E., Wong, T.Y., Mériaudeau, F., An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. Comput. Meth. Progr. Biomed. 139 (2017), 109–117.
148. Silva, R., Berta, A., Larsen, M., Macfadden, W., Feller, C., Mones, J., Treat-and-Extend versus monthly regimen in neovascular age-related macular degeneration: results with ranibizumab from the TREND study. Ophthalmology 125 (2018), 57–65.
149. Simonyan, K., Zisserman, A., Very Deep Convolutional Networks for Large-scale Image Recognition. 2014 arXiv:1409.1556.
150. Somashekhar, S.P., Sepulveda, M.J., Puglielli, S., Norden, A.D., Shortliffe, E.H., Rohit Kumar, C., Rauthan, A., Arun Kumar, N., Patil, P., Rhee, K., Ramya, Y., Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann. Oncol. 29 (2018), 418–423.
151. Song, Q., Bai, J., Garvin, M.K., Sonka, M., Buatti, J.M., Wu, X., Optimal multiple surface segmentation with shape and context priors. IEEE Trans. Med. Imag. 32 (2013), 376–386.
152. Spaide, R.F., IMPROVING the age-related macular degeneration construct: a new classification system. Retina 38 (2018), 891–899.
153. Spaide, R.F., Fujimoto, J.G., Waheed, N.K., Sadda, S.R., Staurenghi, G., Optical coherence tomography angiography. Prog. Retin. Eye Res. 64 (2017), 1–55.
154. Srinivasan, P.P., Kim, L.A., Mettu, P.S., Cousins, S.W., Comer, G.M., Izatt, J.A., Farsiu, S., Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Optic Express 5 (2014), 3568–3577.
155. Sun, Y., Li, S., Sun, Z., Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. J. Biomed. Optic., 22, 2017, 16012.
156. Sun, Z., Chen, H., Shi, F., Wang, L., Zhu, W., Xiang, D., Yan, C., Li, L., Chen, X., An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images. Sci. Rep., 6, 2016, 21739.
157. 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, 12, 2017, e0179790.
158. Tang, A., Tam, R., Cadrin-Chenevert, A., Guest, W., Chong, J., Barfett, J., Chepelev, L., Cairns, R., Mitchell, J.R., Cicero, M.D., Poudrette, M.G., Jaremko, J.L., Reinhold, C., Gallix, B., Gray, B., Geis, R., Canadian Association of Radiologists Artificial Intelligence Working, G., Canadian association of radiologists white paper on artificial intelligence in radiology. Can. Assoc. Radiol. J. 69 (2018), 120–135.
159. Ting, D.S.W., Cheung, C.Y., Lim, G., Tan, G.S.W., Quang, N.D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I.Y., Lee, S.Y., Wong, E.Y.M., Sabanayagam, C., Baskaran, M., Ibrahim, F., Tan, N.C., Finkelstein, E.A., Lamoureux, E.L., Wong, I.Y., Bressler, N.M., Sivaprasad, S., Varma, R., Jonas, J.B., He, M.G., Cheng, C.Y., Cheung, G.C.M., Aung, T., Hsu, W., Lee, M.L., Wong, T.Y., Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. J. Am. Med. Assoc. 318 (2017), 2211–2223.
160. The Guardian, Royal Free breached UK data law in 1.6m patient deal with Google's DeepMind. https://www.theguardian.com/technology/2017/jul/03/google-deepmind-16m-patient-royal-free-deal-data-protection-act, 2017. (Accessed 19 July 2018)
161. Topol, E., The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. 2011, Basic Books.
162. Torkamani, A., Andersen, K.G., Steinhubl, S.R., Topol, E.J., High-definition medicine. Cell 170 (2017), 828–843.
163. Toth, C.A., Decroos, F.C., Ying, G.S., Stinnett, S.S., Heydary, C.S., Burns, R., Maguire, M., Martin, D., Jaffe, G.J., Identification of fluid on optical coherence tomography by treating ophthalmologists versus a reading center in the comparison of age-related macular degeneration treatments trials. Retina 35 (2015), 1303–1314.
164. Treder, M., Lauermann, J.L., Eter, N., Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch. Clin. Exp. Ophthalmol. 256 (2018), 259–265.
165. Tufail, A., Rudisill, C., Eagan, C., Kapetanakis, V.V., Salas-Vega, S., Owen, C.G., Lee, A., Louw, V., Anderson, J., Liew, G., Bolter, L., Srinivas, S., Nittala, M., Sadda, S., Raylor, P., Rudnicka, A.R., Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology 124 (2017), 343–351.
166. van Grinsven, M.J., van Ginneken, B., Hoyng, C.B., Theelen, T., Sanchez, C.I., Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans. Med. Imag. 35 (2016), 1273–1284.
167. van Grinsven, M.J.J.P., Buitendijk, G.H.S., Brussee, C., van Ginneken, B., Hoyng, C.B., Theelen, T., Klaver, C.C.W., Sánchez, C.I., Automatic identification of reticular pseudodrusen using multimodal retinal image analysis. Invest. Ophthalmol. Vis. Sci. 56 (2015), 633–639.
168. van Grinsven, M.J.J.P., Lechanteur, Y.T.E., van de Ven, J.P.H., van Ginneken, B., Hoyng, C.B., Theelen, T., Sánchez, C.I., Automatic drusen quantification and risk assessment of age-related macular degeneration on color fundus images. Invest. Ophthalmol. Vis. Sci. 54 (2013), 3019–3027.
169. Varma, R., Bressler, N.M., Doan, Q.V., Danese, M., Dolan, C.M., Lee, A., Turpcu, A., Visual impairment and blindness avoided with ranibizumab in hispanic and non-hispanic whites with diabetic macular edema in the United States. Ophthalmology 122 (2015), 982–989.
170. Veiga, D., Martins, N., Ferreira, M., Monteiro, J., Automatic microaneurysm detection using laws texture masks and support vector machines. Comput. Meth. Biomech. Biomed. Eng.: Imag. Visual. 0 (2017), 1–12.
171. Venhuizen, F.G., Ginneken, B.v., Liefers, B., Asten, F.v., Schreur, V., Fauser, S., Hoyng, C., Theelen, T., Sánchez, C.I., Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. Biomed. Optic Express 9 (2018), 1545–1569.
172. Venhuizen, F.G., van Ginneken, B., van Asten, F., van Grinsven, M., Fauser, S., Hoyng, C.B., Theelen, T., Sanchez, C.I., Automated staging of age-related macular degeneration using optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 58 (2017), 2318–2328.
173. Verghese, A., Shah, N.H., Harrington, R.A., What this computer needs is a physician: humanism and artificial intelligence. J. Am. Med. Assoc. 319 (2018), 19–20.
174. Vogl, W.D., Waldstein, S.M., Gerendas, B.S., Schlegl, T., Langs, G., Schmidt-Erfurth, U., Analyzing and predicting visual acuity outcomes of anti-VEGF therapy by a longitudinal mixed effects model of imaging and clinical data. Invest. Ophthalmol. Vis. Sci. 58 (2017), 4173–4181.
175. Vogl, W.D., Waldstein, S.M., Gerendas, B.S., Schmidt-Erfurth, U., Langs, G., Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images. IEEE Trans. Med. Imag. 36 (2017), 1773–1783.
176. Waldstein, S.M., Gerendas, B.S., Montuoro, A., Simader, C., Schmidt-Erfurth, U., Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments. BJO (Br. J. Ophthalmol.) 99 (2015), 794–800.
177. Waldstein, S.M., Montuoro, A., Podkowinski, D., Philip, A.M., Gerendas, B.S., Bogunovic, H., Schmidt-Erfurth, U., Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning. Sci. Rep., 7, 2017, 2928.
178. Waldstein, S.M., Philip, A.M., Leitner, R., Simader, C., Langs, G., Gerendas, B.S., Schmidt-Erfurth, U., Correlation of 3-dimensionally quantified intraretinal and subretinal fluid with visual acuity in neovascular age-related macular degeneration. JAMA Ophthalmol. 134 (2016), 182–190.
179. Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., Klein, J.-C., Automatic detection of microaneurysms in color fundus images. Med. Image Anal. 11 (2007), 555–566.
180. Wang, S., Tang, H.L., turk, L.I.A., Hu, Y., Sanei, S., Saleh, G.M., Peto, T., Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng. 64 (2017), 990–1002.
181. Wang, S.K., Callaway, N.F., Wallenstein, M.B., Henderson, M.T., Leng, T., Moshfeghi, D.M., SUNDROP: six years of screening for retinopathy of prematurity with telemedicine. Canadian journal of ophthalmology. J. Can. Ophtalmol. 50 (2015), 101–106.
182. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-supervised Classification and Localization of Common Thorax Diseases. 2017 arXiv:1705.02315.
183. Wang, Z., Camino, A., Hagag, A.M., Wang, J., Weleber, R.G., Yang, P., Pennesi, M.E., Huang, D., Li, D., Jia, Y., Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning. J. Biophot., 11, 2018, e201700313, 10.1002/jbio.201700313.
184. Welikala, R.A., Foster, P.J., Whincup, P.H., Rudnicka, A.R., Owen, C.G., Strachan, D.P., Barman, S.A., Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort. Comput. Biol. Med. 90 (2017), 23–32.
185. Wells, J.A., Glassman, A.R., Jampol, L.M., Aiello, L., Antoszyk, A., Arnold-Bush, B., Baker, C., Bressler, N., Browning, D., Elman, M., Ferris, F., Friedman, C., Pieramici, D., Sun, J., Beck, R., Aflibercept, bevacizumab, or ranibizumab for diabetic macular edema. N. Engl. J. Med. 372 (2015), 1193–1203.
186. Yan, Q., Ding, Y., Liu, Y., Sun, T., Fritsche, L.G., Clemons, T., Ratnapriya, R., Klein, M.L., Cook, R.J., Liu, Y., Fan, R., Wei, L., Abecasis, G.R., Swaroop, A., Chew, E.Y., Group, A.R., Weeks, D.E., Chen, W., Genome-wide analysis of disease progression in age-related macular degeneration. Hum. Mol. Genet. 27 (2018), 929–940.
187. Yu, F., Sun, J., Li, A., Cheng, J., Wan, C., Liu, J., Image quality classification for DR screening using deep learning. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, 664–667.
188. Yu, V.L., Fagan, L.M., Wraith, S.M., Clancey, W.J., Scott, A.C., Hannigan, J., Blum, R.L., Buchanan, B.G., Cohen, S.N., Antimicrobial selection by a computer. A blinded evaluation by infectious diseases experts. J. Am. Med. Assoc. 242 (1979), 1279–1282.
189. Zadeh, S.G., Wintergerst, M., Wiens, V., Thiele, S., Holz, F., Finger, R., Schultz, T., CNNs enable accurate and fast segmentation of drusen in optical coherence tomography. Proceedings of Deep Learning in Medical Image Analysis (DLMIA), 2017.
190. Zeiler, M.D., Fergus, R., Visualizing and understanding convolutional networks. European Conference on Computer Vision, 2014, Springer, 818–833.
191. Zhang, L., Sonka, M., Folk, J.C., Russell, S.R., Abramoff, M.D., Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization. Invest. Ophthalmol. Vis. Sci. 55 (2014), 2329–2335.
192. Zheng, Y., Xiao, R., Wang, Y., Gee, J.C., A generative model for OCT retinal layer segmentation by integrating graph-based multi-surface searching and image registration. Medical image computing and computer-assisted intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 16, 2013, 428–435.
193. Ziemssen, F., Bertelmann, T., Hufenbach, U., Scheffler, M., Liakopoulos, S., Schmitz-Valckenberg, S., Delayed treatment initiation of more than 2 weeks. Relevance for possible gain of visual acuity after anti-VEGF therapy under real life conditions (interim analysis of the prospective OCEAN study). Ophthalmologe 113 (2016), 143–151.