Szemészet, 2021 (158. évfolyam, 1-4. szám)

2021-03-01 / 1. szám

Artificial intelligence in ophthalmology diction of Geographic Atrophy Growth Using Quantitative Spectral-Doma­in Optical Coherence Tomography Biomarkers. Ophthalmology 2016 Aug; 123(8): 1737-1750. 62. Niwas SI, Lin W, Bai X, Kwoh CK, Jay Kuo CC, Sng CC, Aquino MC, Chew PT. Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification. Comput Methods Programs Biomed 2016 Jul; 130: 65-75. 63. Pennington KL, DeAngelis MM. Epidemiology of age-related macular degeneration (AMD): associations with cardiovascular disease phenotypes and lipid factors. Eye Vis (Lond) 2016 Dec 22; 3: 34. 64. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2018 Mar; 2(3): 158-164. 65. Quigley HA, Brown AE, Morrison JD, Drance SM. The size and shape of the optic disc in normal human eyes. Arch Ophthalmol 1990; 108: 51-57. 66. Quigley HA, West SK, Rodriguez J. The prevalence of glaucoma in a po­pulation-based study of Hispanic subjects: Proyecto VER. Arch Ophthalmol 2001; 119: 1819-1826. 67. Rawat W, Wang Z. Deep Convolutional Neural Networks for Image Clas­sification: A Comprehensive Review. Neural Computation 2017; 29: 2410. 68. Russell SJ, Norvig P. Artificial Intelligence: A Modern Approach (3"1 ed.) Upper New Jersey Prentice Hall: Saddle River; 2009. 69. Saini JS, Jain AK, Kumar S, Vikal S, Pankaj S, Singh S. Neural network approach to classify infective keratitis. Curr Eye Res 2003 Aug; 27(2): 111-116. 70. Sample PA, Goldbaum MH, Chan K, Boden C, Lee TW, Vasile C, Boehm AG, Sejnowski T, Johnson CA, Weinreb RN. Using machine learning classifi­ers to identify glaucomatous change earlier in standard visual fields. Invest Ophthalmol Vis Sei 2002; 43: 2660-2665. 71. Schell GJ, Lavieri MS, Helm JE, Liu X, Musch DC, Van Oyen MP, Stein JD. Using filtered forecasting techniques to determine personalized mo­nitoring schedules for patients with open-angle glaucoma. Ophthalmology 2014; 121: 1539-1546. 72. Schiegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, Podkowinski 0, Gerendas BS, Langs G, Schmidt-Erfurth 0. Fuly­­ly Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning. Ophthalmology 2018 Apr; 125(4): 549-558. 73. Schiegl T, Bogunovic H, Klimscha S, Seeböck P, Sadeghipour A, Geren­das B, Waldenstein SM, Langs G, Schmidt-Erfurth U. Fully automated seg­mentation of hyperreflective foci in optical coherence tomography images. ArXiv: 1805.03278. 74. Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bo­gunovic H. Artificial intelligence in retina. Prog Retin Eye Res 2018 Nov; 67: 1-29. 75. Schmidt-Erfurth U, Bogunovic H, Sadeghipour A, Schiegl T, Langs G, Gerendas BS, Osborne A, Waldstein SM. Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Re­lated Macular Degeneration. Ophthalmol Retina 2018 Jan; 2(1): 24-30. 76. Schmidt-Erfurth U, Waldstein SM, Klimscha S, Sadeghipour A, Hu X, Gerendas BS, Osborne A, Bogunovic H. Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence. Invest Ophthalmol Vis Sei 2018 Jul 2; 59(8): 3199-3208. 77. Schmidt-Erfurth 0, Waldstein SM. A paradigm shift in imaging biomar­kers in neovascular age-related macular degeneration. Prog Retin Eye Res 2016 Jan; 50:1-24. 78. Sinthanayothin C, Boyce JF, Cook HL, Williamson TH. Automated locali­sation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 1999 Aug; 83(8): 902-10. 79. Smolek MK, Klyce SD. Current keratoconus detection methods compa­red with a neural network approach. Invest Ophthalmol Vis Sei 1997 Oct; 38(11): 2290-2299. 80. Sramka M, Slovak M, Tuckova J, Stodulka P. Improving clinical refrac­tive results of cataract surgery by machine learning. PeerJ 2019 Jul 2; 7: e7202. 81. Stirling SM. T2: Infiltrator. New York City, New York, USA: HarperCol­lins Publishers LLC, 2001. 82. Tan P-N, Steinbach M, Kumar V. Bevezetés az adatbányászatba. Ma­gyarország: Panem Kft.; 2011. 83. Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalen ­ce of glaucoma and projections of glaucoma burden through 2040: a syste­matic review and meta-analysis. Ophthalmology 2014; 121: 2081-2090. 84. Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY. 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 Dec 12; 318(22): 2211-2223. 85. Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetteren L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019 Feb; 103(2): 167-175. 86. Tong Y, Lu W, Yu Y, Shen Y. Application of machine learning in ophthalmic imaging modalities. Eye Vis (Lond) 2020 Apr 16; 7: 22 87. Turing A. M. Computing Machinery and Intelligence. Mind, 1950:433-460. 88. Ulam S. Tribute to John von Neumann. Bulletin of the American Mathe­matical Society; 1958: 1-49. 89. Varadarajan AV, Poplin R, Blumer K, Angermueller C, Ledsam J, Chopra R, Keane PA, Corrado GS, Peng L, Webster DR. Deep Learning for Predicti­ng Refractive Error From Retinal Fundus Images. Invest Ophthalmol Vis Sei 2018 Jun 1; 59(7): 2861-2868. 90. Varga L, Kovács A, Grósz T, Thury G, Hadarits F, Dégi R, Dombi J. Au­tomatic segmentation of hyperreflective foci in OCT images. Comput Met­hods Programs Biomed 2019 Sep; 178: 91-103. 91. Varma DK, Simpson SM, Rai AS, Ahmed IIK. Undetected angle closure in patients with a diagnosis of open-angle glaucoma. Can J Ophthalmol 2017 Aug; 52(4): 373-378. 92. Velez-Montoya R, Oliver SC, Olson JL, Fine SL, Mandava N, Quiroz-Mer­cado H. Current knowledge and trends in age-related macular degenera­tion: today's and future treatments. Retina 2013 Sep; 33(8): 1487-502. 93. Venhuizen FG, van Ginneken B, van Asten F, van Grinsven MJJP, Fauser S, Hoyng CB, Theelen T, Sánchez Cl. Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography. Invest Opht­halmol Vis Sei 2017 Apr 1; 58(4): 2318-2328. 94. Vianna JR, Chauhan BC. How to detect progression in glaucoma. Prog Brain Res 2015;221:135-158. 95. Wan Zaki WMD, Mat Daud M, Abdani SR, Hussain A, Mutalib HA. Au­tomated pterygium detection method of anterior segment photographed images. Comput Methods Programs Biomed 2018 Feb; 154; 71-78. 96. Wen JC, Lee CS, Keane PA, Xiao S, Rokem AS, Chen PP, Wu Y, Lee AY. Forecasting future Humphrey Visual Fields using deep learning. PLoS One 2019; 14: e0214875. 97. Xu BY, Chiang M, Chaudhary S, Kulkarni S, Pardeshi AA, Varma R. Deep Learning Classifiers for Automated Detection of Gonioscopic Angle Closure Based on Anterior Segment OCT Images. Am J Ophthalmol 2019 Dec; 208: 273-280. 98. Xu X, Zhang L, Li J, Guan Y, Zhang L. A Hybrid Global-Local Represen­tation CNN Model for Automatic Cataract Grading. IEEE J Biomed Health Inform 2020 Feb; 24(2): 556-567. 99. Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, Kim JS, Rim TH. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. NPJ Digit Med 2019 Jun 20; 2: 59. 100. Yousefi S, Yousefi E, Takahashi H, Hayashi T, Tempo H, Inoda S, Arai Y, Asbell P. Keratoconus severity identification using unsupervised machine learning. PLoS One 2018 Nov 6; 13(11): e0205998. 101. Zhang L, Devalla SK, Cheng C-Y. A Multi-device, Multi-ethnicity Deep Learning Algorithm to Detect Glaucoma from A Single Optical Coherence Tomography Scan of the Optic Nerve Head. Invest Ophthalmol Vis Sei 2019; 60: 2207. Levelezési c On Szabó Áron, e-mail: szabo.aron.dr@gmail.com ■ 14 ■

Next

/
Thumbnails
Contents