Deep learning: applications in retinal and optic nerve diseases

References

• Hee MR, Izatt JA, Swanson EA et al. Optical coherence tomography of the human retina. Arch Ophthalmol. 1995;113:325–332.
   PubMed Web of Science ®Google Scholar
• Koozekanani D, Boyer K, Roberts C. Retinal thickness measurements from optical coherence tomography using a Markov boundary model. IEEE Trans Med Imaging. 2001;20:900–916.
   PubMed Web of Science ®Google Scholar
• Ishikawa H, Stein DM, Wollstein G et al. Macular segmentation with optical coherence tomography. Invest Ophthalmol Vis Sci. 2005;46:2012–2017.
   PubMed Web of Science ®Google Scholar
• Kajic V, Povazay B, Hermann B et al. Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Opt Express. 2010;18:14730–14744.
   PubMed Web of Science ®Google Scholar
• Staurenghi G, Sadda S, Chakravarthy U et al. Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN*OCT consensus. Ophthalmology. 2014;121:1572–1578.
   PubMed Web of Science ®Google Scholar
• Charng J, Cideciyan AV, Jacobson SG et al. Variegated yet non-random rod and cone photoreceptor disease patterns in RPGR-ORF15-associated retinal degeneration. Hum Mol Genet. 2016;25:5444–5459.
   PubMed Web of Science ®Google Scholar
• Lang A, Carass A, Hauser M et al. Retinal layer segmentation of macular OCT images using boundary classification. Biomed Opt Express. 2013;4:1133–1152.
   PubMed Web of Science ®Google Scholar
• Zeiler, MD, Fergus, R. Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B and Tuytelaars T, editors. Computer Vision-ECCV. Zurich: Springer; 2014;p. 818–833.
   Google Scholar
• Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–248.
   PubMed Web of Science ®Google Scholar
• Charng J, Xiao D, Mehdizadeh M et al. Deep learning segmentation of hyperautofluorescent fleck lesions in Stargardt disease. Sci Rep. 2020;10:16491.
   PubMed Web of Science ®Google Scholar
• Blindness GBD, Vision Impairment C, Vision Loss Expert Group of the Global Burden of Disease S. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the right to sight: an analysis for the global burden of disease study. Lancet Glob Health. 2021;9:e144–e160.
   PubMed Web of Science ®Google Scholar
• Keel S, Xie J, Foreman J et al. The prevalence of diabetic retinopathy in Australian adults with self-reported diabetes: the national eye health survey. Ophthalmology. 2017;124:977–984.
   PubMed Web of Science ®Google Scholar
• Heath Jeffery RC, Mukhtar SA, McAllister IL et al. Inherited retinal diseases are the most common cause of blindness in the working-age population in Australia. Ophthalmic Genet. 2021;42:431–439.
   PubMed Web of Science ®Google Scholar
• Watson MJG, McCluskey PJ, Grigg JR et al. Barriers and facilitators to diabetic retinopathy screening within Australian primary care. BMC Fam Pract. 2021;22:239.
   PubMed Web of Science ®Google Scholar
• Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. Jama. 2016;316:2366–2367.
   PubMed Web of Science ®Google Scholar
• Mathenge WC. Artificial intelligence for diabetic retinopathy screening in Africa. Lancet Digit Health. 2019;1:e6–e7.
   PubMedGoogle Scholar
• NHS diabetic eye screening programme. [cited 2022 July 18]. Available from: http://diabeticeye.screening.nhs.uk
   Google Scholar
• Ahmed J, Ward TP, Bursell SE et al. The sensitivity and specificity of nonmydriatic digital stereoscopic retinal imaging in detecting diabetic retinopathy. Diabetes Care. 2006;29:2205–2209.
   PubMed Web of Science ®Google Scholar
• Gibbins RL, Owens DR, Allen JC et al. Practical application of the European field guide in screening for diabetic retinopathy by using ophthalmoscopy and 35 mm retinal slides. Diabetologia. 1998;41:59–64.
   PubMed Web of Science ®Google Scholar
• Harper CA, Livingston PM, Wood C, et al. Screening for diabetic retinopathy using a non-mydriatic retinal camera in rural Victoria. Aust N Z J Ophthalmol. 1998;26: 117–121.
   PubMedGoogle Scholar
• Murray RB, Metcalf SM, Lewis PM et al. Sustaining remote-area programs: retinal camera use by aboriginal health workers and nurses in a Kimberley partnership. Med J Aust. 2005;182:520–523.
   PubMed Web of Science ®Google Scholar
• Nguyen HV, Tan GS, Tapp RJ et al. Cost-Effectiveness of a national telemedicine diabetic retinopathy screening program in Singapore. Ophthalmology. 2016;123:2571–2580.
   PubMed Web of Science ®Google Scholar
• Abramoff MD, Folk JC, Han DP et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013;131:351–357.
   PubMed Web of Science ®Google Scholar
• Fleming AD, Goatman KA, Philip S et al. Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts. Br J Ophthalmol. 2010;94:1606–1610.
   PubMed Web of Science ®Google Scholar
• Quellec G, Lamard M, Cazuguel G, et al. Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs. Invest Ophthalmol Vis Sci. 2011;52:8342–8348.
   PubMed Web of Science ®Google Scholar
• Tufail A, Rudisill C, Egan C et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124:343–351.
   PubMed Web of Science ®Google Scholar
• Abramoff MD, Lou Y, Erginay A et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57:5200–5206.
   PubMed Web of Science ®Google Scholar
• Gulshan V, Peng L, Coram M et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama. 2016;316:2402–2410.
   PubMed Web of Science ®Google Scholar
• Quellec G, Russell SR, Abramoff MD. Optimal filter framework for automated, instantaneous detection of lesions in retinal images. IEEE Trans Med Imaging. 2011;30:523–533.
   PubMed Web of Science ®Google Scholar
• Li Z, Keel S, Liu C et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41:2509–2516.
   PubMed Web of Science ®Google Scholar
• Ting DSW, Cheung CY, Lim G et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama. 2017;318:2211–2223.
   PubMed Web of Science ®Google Scholar
• Bhuiyan A, Govindaiah A, Deobhakta A et al. Development and validation of an automated diabetic retinopathy screening tool for primary care setting. Diabetes Care. 2020;43:e147–e148.
   PubMed Web of Science ®Google Scholar
• Sánchez-Gutiérrez V, Hernández-Martínez P, Muñoz-Negrete FJ et al. Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings. arXiv. 2022;arXiv:2205.05554.
   Google Scholar
• Kanagasingam Y, Xiao D, Vignarajan J, et al. Evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care. JAMA Network Open. 2018;1:e182665.
   PubMed Web of Science ®Google Scholar
• Oliveira CM, Cristovao LM, Ribeiro ML et al. Improved automated screening of diabetic retinopathy. Ophthalmologica. 2011;226:191–197.
   PubMed Web of Science ®Google Scholar
• Ramos JD, Almeida N, Neves C et al. Retmarker screening alternative deep learning algorithm also increases burden reduction in DR screening programs. 29th meeting of the European association for the study of diabetes eye complications study group (EASDec). Amsterdam, The Netherlands: European Journal of Opthalmology; 2019.
   Google Scholar
• Abramoff MD, Lavin PT, Birch M et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.
   PubMed Web of Science ®Google Scholar
• Dai L, Wu L, Li H et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun. 2021;12:3242.
   PubMed Web of Science ®Google Scholar
• ElTanboly A, Ismail M, Shalaby A et al. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Med Phys. 2017;44:914–923.
   PubMed Web of Science ®Google Scholar
• Sandhu HS, Eltanboly A, Shalaby A et al. Automated diagnosis and grading of diabetic retinopathy using optical coherence tomography. Invest Ophthalmol Vis Sci. 2018;59:3155–3160.
   PubMedGoogle Scholar
• Alam M, Zhang Y, Lim JI et al. Quantitative optical coherence tomography angiography features for objective classification and staging of diabetic retinopathy. Retina. 2020;40:322–332.
   PubMed Web of Science ®Google Scholar
• Heisler M, Karst S, Lo J et al. Ensemble deep learning for diabetic retinopathy detection using optical coherence tomography angiography. Transl Vis Sci Technol. 2020;9:20.
   PubMed Web of Science ®Google Scholar
• Bora A, Balasubramanian S, Babenko B et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit Health. 2021;3:e10–e19.
   PubMedGoogle Scholar
• Arcadu F, Benmansour F, Maunz A et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit Med. 2019;2:92.
   PubMed Web of Science ®Google Scholar
• Li JQ, Welchowski T, Schmid M et al. Prevalence and incidence of age-related macular degeneration in Europe: a systematic review and meta-analysis. Br J Ophthalmol. 2020;104:1077–1084.
   PubMed Web of Science ®Google Scholar
• Heath Jeffery RC, Mukhtar SA, Lopez D et al. Incidence of newly registered blindness from age-related macular degeneration in Australia over a 21-year period: 1996-2016. Asia Pac J Ophthalmol (Phila). 2021;10:442–449.
   PubMed Web of Science ®Google Scholar
• Heier JS, Khanani AM, Quezada Ruiz C et al. Efficacy, durability, and safety of intravitreal faricimab up to every 16 weeks for neovascular age-related macular degeneration (TENAYA and LUCERNE): two randomised, double-masked, phase 3, non-inferiority trials. Lancet. 2022;399:729–740.
   PubMed Web of Science ®Google Scholar
• Liao DS, Grossi FV, El Mehdi D et al. Complement C3 inhibitor pegcetacoplan for geographic atrophy secondary to age-related macular degeneration: a randomized phase 2 trial. Ophthalmology. 2020;127:186–195.
   PubMed Web of Science ®Google Scholar
• Burlina PM, Joshi N, Pekala M et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135:1170–1176.
   PubMed Web of Science ®Google Scholar
• Gonzalez-Gonzalo C, Sanchez-Gutierrez V, Hernandez-Martinez P et al. Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration. Acta Ophthalmol. 2020;98:368–377.
   PubMed Web of Science ®Google Scholar
• Keenan TD, Dharssi S, Peng Y et al. A deep learning approach for automated detection of geographic atrophy from color fundus photographs. Ophthalmology. 2019;126:1533–1540.
   PubMed Web of Science ®Google Scholar
• Treder M, Lauermann JL, Eter N. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier. Graefes Arch Clin Exp Ophthalmol. 2018;256:2053–2060.
   PubMed Web of Science ®Google Scholar
• 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;1:322–327.
   PubMedGoogle Scholar
• Li F, Chen H, Liu Z et al. Fully automated detection of retinal disorders by image-based deep learning. Graefes Arch Clin Exp Ophthalmol. 2019;257:495–505.
   PubMed Web of Science ®Google Scholar
• Treder M, Lauermann JL, Eter N. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefes Arch Clin Exp Ophthalmol. 2018;256:259–265.
   PubMed Web of Science ®Google Scholar
• Yoo TK, Choi JY, Seo JG et al. The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Med Biol Eng Comput. 2019;57:677–687.
   PubMed Web of Science ®Google Scholar
• An G, Akiba M, Yokota H et al. Deep learning classification models built with two-step transfer learning for age related macular degeneration diagnosis. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:2049–2052.
   PubMedGoogle Scholar
• Motozawa N, An G, Takagi S et al. Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes. Ophthalmol Ther. 2019;8:527–539.
   PubMed Web of Science ®Google Scholar
• Grassmann F, Mengelkamp J, Brandl C et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology. 2018;125:1410–1420.
   PubMed Web of Science ®Google Scholar
• Yellapragada B, Hornauer S, Snyder K et al. Self-Supervised feature learning and phenotyping for assessing age-related macular degeneration using retinal fundus images. Ophthalmol Retina. 2021;6(2):116–129.
   PubMedGoogle Scholar
• Peng Y, Dharssi S, Chen Q et al. DeepSeenet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology. 2019;126:565–575.
   PubMed Web of Science ®Google Scholar
• Chiu CJ, Mitchell P, Klein R et al. A risk score for the prediction of advanced age-related macular degeneration: development and validation in 2 prospective cohorts. Ophthalmology. 2014;121:1421–1427.
   PubMed Web of Science ®Google Scholar
• Joachim ND, Mitchell P, Kifley A et al. Incidence, progression, and associated risk factors of medium drusen in age-related macular degeneration: findings from the 15-year follow-up of an Australian cohort. JAMA Ophthalmol. 2015;133:698–705.
   PubMed Web of Science ®Google Scholar
• Bhuiyan A, Wong TY, Ting DSW et al. Artificial intelligence to stratify severity of age-related macular degeneration (AMD) and predict risk of progression to late AMD. Transl Vis Sci Technol. 2020;9:25.
   PubMed Web of Science ®Google Scholar
• Burlina PM, Joshi N, Pacheco KD et al. Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration. JAMA Ophthalmol. 2018;136:1359–1366.
   PubMed Web of Science ®Google Scholar
• Peng Y, Keenan TD, Chen Q et al. Predicting risk of late age-related macular degeneration using deep learning. NPJ Digit Med. 2020;3:111.
   PubMed Web of Science ®Google Scholar
• Yan Q, Weeks DE, Xin H et al. Deep-learning-based prediction of late age-related macular degeneration progression. Nat Mach Intell. 2020;2:141–150.
   PubMedGoogle Scholar
• Mishra Z, Ganegoda A, Selicha J et al. Automated retinal layer segmentation using graph-based algorithm incorporating deep-learning-derived information. Sci Rep. 2020;10:9541.
   PubMed Web of Science ®Google Scholar
• Moraes G, Fu DJ, Wilson M et al. Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128:693–705.
   PubMed Web of Science ®Google Scholar
• Schmidt-Erfurth U, Vogl WD, Jampol LM et al. Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration. Ophthalmology. 2020;127:1211–1219.
   PubMed Web of Science ®Google Scholar
• Saha S, Nassisi M, Wang M et al. Automated detection and classification of early AMD biomarkers using deep learning. Sci Rep. 2019;9:10990.
   PubMed Web of Science ®Google Scholar
• Schmidt-Erfurth U, Bogunovic H, Grechenig C et al. Role of deep learning-quantified hyperreflective foci for the prediction of geographic atrophy progression. Am J Ophthalmol. 2020;216:257–270.
   PubMed Web of Science ®Google Scholar
• Hosoda Y, Miyake M, Yamashiro K et al. Deep phenotype unsupervised machine learning revealed the significance of pachychoroid features in etiology and visual prognosis of age-related macular degeneration. Sci Rep. 2020;10:18423.
   PubMed Web of Science ®Google Scholar
• Gigon A, Mosinska A, Montesel A et al. Personalized atrophy risk mapping in age-related macular degeneration. Transl Vis Sci Technol. 2021;10:18.
   PubMed Web of Science ®Google Scholar
• Pfau M, Sahu S, Rupnow RA et al. Probabilistic forecasting of anti-VEGF treatment frequency in neovascular age-related macular degeneration. Transl Vis Sci Technol. 2021;10:30.
   PubMed Web of Science ®Google Scholar
• Romo-Bucheli D, Erfurth US, Bogunovic H. End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging. IEEE J Biomed Health Inform. 2020;24:3456–3465.
   PubMed Web of Science ®Google Scholar
• Holtan JP, Selmer KK, Heimdal KR et al. Inherited retinal disease in Norway - a characterization of current clinical and genetic knowledge. Acta Ophthalmol. 2020;98:286–295.
   PubMed Web of Science ®Google Scholar
• Arsalan M, Baek NR, Owais M et al. Deep learning-based detection of pigment signs for analysis and diagnosis of retinitis pigmentosa. Sensors (Basel). 2020;20(12):3454.
   PubMed Web of Science ®Google Scholar
• Shah M, Roomans Ledo A, Rittscher J. Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning. Acta Ophthalmol. 2020;98:e715–e721.
   PubMed Web of Science ®Google Scholar
• Miere A, Le Meur T, Bitton K et al. Deep learning-based classification of inherited retinal diseases using fundus autofluorescence. J Clin Med. 2020;9:3303.
   PubMed Web of Science ®Google Scholar
• Kugelman J, Alonso-Caneiro D, Chen Y et al. Retinal boundary segmentation in Stargardt disease optical coherence tomography images using automated deep learning. Transl Vis Sci Technol. 2020;9:12.
   PubMed Web of Science ®Google Scholar
• Mishra Z, Wang Z, Sadda SR et al. Automatic segmentation in multiple OCT layers for Stargardt disease characterization via deep learning. Transl Vis Sci Technol. 2021;10:24.
   PubMed Web of Science ®Google Scholar
• Wang YZ, Wu W, Birch DG. A hybrid model composed of two convolutional neural networks (CNNs) for automatic retinal layer segmentation of OCT images in retinitis pigmentosa (RP). Transl Vis Sci Technol. 2021;10:9.
   Web of Science ®Google Scholar
• Rim TH, Lee AY, Ting DS et al. Computer-Aided detection and abnormality score for the outer retinal layer in optical coherence tomography. Br J Ophthalmol. 2021.
   Web of Science ®Google Scholar
• Davidson B, Kalitzeos A, Carroll J et al. Automatic cone photoreceptor localisation in healthy and Stargardt afflicted retinas using deep learning. Sci Rep. 2018;8:7911.
   PubMed Web of Science ®Google Scholar
• Sumaroka A, Cideciyan AV, Sheplock R et al. Foveal therapy in blue cone monochromacy: predictions of visual potential from artificial intelligence. Front Neurosci. 2020;14:800.
   PubMed Web of Science ®Google Scholar
• Sumaroka A, Garafalo AV, Semenov EP, 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;60:2551–2562.
   PubMed Web of Science ®Google Scholar
• Tham YC, Li X, Wong TY et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology. 2014;121:2081–2090.
   PubMed Web of Science ®Google Scholar
• Flaxman SR, Bourne RRA, Resnikoff S et al. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5:e1221–e1234.
   PubMed Web of Science ®Google Scholar
• Jammal AA, Thompson AC, Mariottoni EB et al. Human versus machine: comparing a deep learning algorithm to human gradings for detecting glaucoma on fundus photographs. Am J Ophthalmol. 2020;211:123–131.
   PubMed Web of Science ®Google Scholar
• Phene S, Dunn RC, Hammel N et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs. Ophthalmology. 2019;126:1627–1639.
   PubMed Web of Science ®Google Scholar
• Shibata N, Tanito M, Mitsuhashi K et al. Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Sci Rep. 2018;8:14665.
   PubMed Web of Science ®Google Scholar
• Russakoff DB, Mannil SS, Oakley JD et al. A 3D deep learning system for detecting referable glaucoma using full OCT macular cube scans. Transl Vis Sci Technol. 2020;9:12.
   PubMed Web of Science ®Google Scholar
• Kim KE, Kim JM, Song JE et al. Development and validation of a deep learning system for diagnosing glaucoma using optical coherence tomography. J Clin Med. 2020;9:2167.
   PubMed Web of Science ®Google Scholar
• Lee J, Kim YK, Park KH et al. Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma. 2020;29:287–294.
   PubMed Web of Science ®Google Scholar
• Wang P, Shen J, Chang R et al. Machine learning models for diagnosing glaucoma from retinal nerve fiber layer thickness maps. Ophthalmol Glaucoma. 2019;2:422–428.
   PubMedGoogle Scholar
• Thompson AC, Jammal AA, Berchuck SI et al. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans. JAMA Ophthalmol. 2020;138:333–339.
   PubMed Web of Science ®Google Scholar
• Lee J, Kim Y, Kim JH et al. Screening glaucoma with red-free fundus photography using deep learning classifier and polar transformation. J Glaucoma. 2019;28:258–264.
   PubMed Web of Science ®Google Scholar
• Sulot D, Alonso-Caneiro D, Ksieniewicz P et al. Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method. PLoS One. 2021;16:e0252339.
   PubMed Web of Science ®Google Scholar
• Bowd C, Belghith A, Zangwill LM et al. Deep learning image analysis of optical coherence tomography angiography measured vessel density improves classification of healthy and glaucoma eyes. Am J Ophthalmol. 2021;236:298–308.
   PubMed Web of Science ®Google Scholar
• Asaoka R, Murata H, Iwase A et al. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology. 2016;123:1974–1980.
   PubMed Web of Science ®Google Scholar
• Kucur SS, Hollo G, Sznitman R. A deep learning approach to automatic detection of early glaucoma from visual fields. PLoS One. 2018;13:e0206081.
   PubMed Web of Science ®Google Scholar
• Xiong J, Li F, Song D et al. Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology. 2021;129(2):171–180.
   PubMed Web of Science ®Google Scholar
• Christopher M, Nakahara K, Bowd C et al. Effects of study population, labeling and training on glaucoma detection using deep learning algorithms. Transl Vis Sci Technol. 2020;9:27.
   PubMed Web of Science ®Google Scholar
• Chang J, Lee J, Ha A et al. Explaining the rationale of deep learning glaucoma decisions with adversarial examples. Ophthalmology. 2021;128:78–88.
   PubMed Web of Science ®Google Scholar
• Keel S, Wu J, Lee PY, et al. Visualizing deep learning models for the detection of referable diabetic retinopathy and glaucoma. JAMA Ophthalmol. 2019;137:288–292.
   PubMed Web of Science ®Google Scholar
• Thakoor KA, Koorathota SC, Hood DC et al. Robust and interpretable convolutional neural networks to detect glaucoma in optical coherence tomography images. IEEE Trans Biomed Eng. 2021;68:2456–2466.
   PubMed Web of Science ®Google Scholar
• Christopher M, Bowd C, Proudfoot JA et al. Deep learning estimation of 10-2 and 24-2 visual field metrics based on thickness maps from macula OCT. Ophthalmology. 2021;128:1534–1548.
   PubMed Web of Science ®Google Scholar
• Hashimoto Y, Asaoka R, Kiwaki T et al. Deep learning model to predict visual field in central 10 degrees from optical coherence tomography measurement in glaucoma. Br J Ophthalmol. 2021;105:507–513.
   PubMed Web of Science ®Google Scholar
• Park K, Kim J, Lee J. A deep learning approach to predict visual field using optical coherence tomography. PLoS One. 2020;15:e0234902.
   PubMedGoogle Scholar
• Lee J, Kim YK, Ha A et al. Macular ganglion cell-inner plexiform layer thickness prediction from red-free fundus photography using hybrid deep learning model. Sci Rep. 2020;10:3280.
   PubMed Web of Science ®Google Scholar
• Bowd C, Belghith A, Christopher M et al. Individualized glaucoma change detection using deep learning auto encoder-based regions of interest. Transl Vis Sci Technol. 2021;10:19.
   PubMed Web of Science ®Google Scholar
• Berchuck SI, Mukherjee S, Medeiros FA. Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder. Sci Rep. 2019;9:18113.
   PubMed Web of Science ®Google Scholar
• Lee T, Jammal AA, Mariottoni EB et al. Predicting glaucoma development with longitudinal deep learning predictions from fundus photographs. Am J Ophthalmol. 2021;225:86–94.
   PubMed Web of Science ®Google Scholar
• Wen JC, Lee CS, Keane PA, et al. Forecasting future Humphrey visual fields using deep learning. PLoS One. 2019;14:e0214875.
   PubMed Web of Science ®Google Scholar
• Shin J, Kang MS, Park K et al. Association between metabolic risk factors and optic disc cupping identified by deep learning method. PLoS One. 2020;15:e0239071.
   PubMed Web of Science ®Google Scholar
• Mao Z, Miki A, Mei S et al. Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans. Biomed Opt Express. 2019;10:5832–5851.
   PubMed Web of Science ®Google Scholar
• Panda SK, Cheong H, Tun TA et al. Describing the structural phenotype of the glaucomatous optic nerve head using artificial intelligence. Am J Ophthalmol. 2021;236:172–182.
   PubMed Web of Science ®Google Scholar
• Chia EM, Wang JJ, Rochtchina E et al. Impact of bilateral visual impairment on health-related quality of life: the blue mountains eye study. Invest Ophthalmol Vis Sci. 2004;45:71–76.
   PubMed Web of Science ®Google Scholar
• Ivers RQ, Cumming RG, Mitchell P et al. Visual impairment and falls in older adults: the blue mountains eye study. J Am Geriatr Soc. 1998;46:58–64.
   PubMed Web of Science ®Google Scholar
• Evans JR, Fletcher AE, Wormald RP. Depression and anxiety in visually impaired older people. Ophthalmology. 2007;114:283–288.
   PubMed Web of Science ®Google Scholar
• Microsoft. Seeing AI | talking camera app for those with a visual impairment; 2019 [cited 2022 Aug 15]. Available from: https://www.microsoft.com/en-us/garage/wall-of-fame/seeing-ai/
   Google Scholar
• El-Taher FE, Taha A, Courtney J et al. A systematic review of urban navigation systems for visually impaired people. Sensors (Basel). 2021;21:3103.
   PubMed Web of Science ®Google Scholar
• Alwi SRAW, Ahmad MN. Survey on outdoor navigation system needs for blind people. IEEE Student Conference on Research and Developement; Putrajaya, Malaysia; 2013.
   Google Scholar
• Li B, Muñoz JP, Rong X, et al. ISANA: wearable context-aware indoor assistive navigation with obstacle avoidance for the blind. Computer Vision – ECCV 2016 Workshops Lecture Notes in Computer Science; Amsterdam, The Netherlands; 2016.
   Google Scholar
• Yang G, Saniie J. Indoor navigation for visually impaired using AR markers. IEEE International Conference on Electro Information Technology; Lincoln, NE, USA; 2017. p 1–5.
   Google Scholar
• Taeihagh A, Lim HSM. Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport Rev. 2019;39:103–128.
   Web of Science ®Google Scholar
• Hopkins D, Schwanen T. Talking about automated vehicles: what do levels of automation do?. Technol Soc. 2021;64:64.
   Web of Science ®Google Scholar