Advances on the application of deep learning in glaucoma screening and diagnosis

doi:10.3877/cma.j.issn.2095-2007.2023.03.012

Abstract

Abstract:

In recent years, deep learning has become an important cutting-edge field of computer science research, which has been widely used in image recognition, speech recognition and natural language processing. But its lateral integration on the medical field has not been yet fully developped. The application of deep learning in ophthalmic examinations including fundus photography, optical coherence tomography and visual field examination can effectively screen and evaluate a variety of common blinding eye diseases including glaucoma, cataract, age-related macular degeneration and diabetic retinopathy. Currently, it has been fully proved its practicability in the screening and diagnosis of common ophthalmic diseases. In this paper, the application of deep learning technology in the screening, diagnosis of glaucoma and the future research direction was reviewed.

Key words: Deep learning, Glaucoma, Fundus photography, Optical coherence tomography, Visual field

Ziyan Cai, Xuanchu Duan, Xiang Yang. Advances on the application of deep learning in glaucoma screening and diagnosis[J]. Chinese Journal of Ophthalmologic Medicine(Electronic Edition), 2023, 13(03): 188-192.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhykyxzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.2095-2007.2023.03.012

https://zhykyxzz.cma-cmc.com.cn/EN/Y2023/V13/I03/188

References 53

[1]	Cheung CY，冉安然. 青光眼影像人工智能深度学习研究现状与展望[J]. 山东大学学报(医学版)，2020，58(11)：24－32; 24－32，38.
[2]	Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504－507.
[3]	Junior BJR, Nicoletti MD, Zhao L. Attribute－based decision graphs: a framework for multiclass data classification[J]. Neural Netw, 2017, 85: 69－84.
[4]	张秀兰，李飞. 人工智能和青光眼：机遇与挑战[J]. 中华实验眼科杂志，2018，36(4)：245－247.
[5]	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[J]. Ophthalmology, 2014, 121(11): 2081－2090.
[6]	张军阳，王慧丽，郭阳，等. 深度学习相关研究综述[J]. 计算机应用研究，2018，35(7)：1921－1928; 1921－1928，1936.
[7]	章琳，袁非牛，张文睿，等. 全卷积神经网络研究综述[J]. 计算机工程与应用，2020，56(1)：25－37.
[8]	庞睿奇，刘含若，王宁利. 分析高影响力期刊发表的眼科文献特点认识深度学习算法在眼科研究中应用的意义[J/CD]. 中华眼科医学杂志(电子版)，2019，9(2)：65－70.
[9]	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[J]. JAMA, 2016, 316(22): 2402－2410.
[10]	Shin Y, Cho H, Jeong HC, et al. Deep learning－based diagnosis of glaucoma using wide－field optical coherence tomography images[J]. J Glaucoma, 2021, 30(9): 803－812.
[11]	Foster PJ, Buhrmann R, Quigley HA, et al. The definition and classification of glaucoma in prevalence surveys[J]. Br J Ophthalmol, 2002, 86(2): 238－242.
[12]	Crowston JG, Hopley CR, Healey PR, et al. The effect of optic disc diameter on vertical cup to disc ratio percentiles in a population based cohort: the Blue Mountains Eye Study[J]. Br J Ophthalmol, 2004, 88(6): 766－770.
[13]	Chauhan BC, Burgoyne CF. From clinical examination of the optic disc to clinical assessment of the optic nerve head: a paradigm change[J]. Am J Ophthalmol, 2013, 156(2): 218－227.
[14]	Savini G, Carbonelli M, Barboni P. Spectral－domain optical coherence tomography for the diagnosis and follow－up of glaucoma[J]. Curr Opin Ophthalmol, 2011, 22(2): 115－123.
[15]	Chauhan BC, Danthurebandara VM, Sharpe GP, et al. Bruch′s membrane opening minimum rim width and retinal nerve fiber layer thickness in a normal white population: a multicenter study[J]. Ophthalmology, 2015, 122(9): 1786－1794.
[16]	Vinicius DSFM, Oseas DCFA, De－Sousa AD, et al. Convolutional neural network and texture descriptor－based automatic detection and diagnosis of glaucoma[J]. Expert Syst Appl, 2018, 110(11): 250－263.
[17]	杨昊，胡曼，徐永利. 基于深度学习的多模态眼科图像回归预测[J]. 北京化工大学学报(自然科学版)，2021，48(3)：81－87.
[18]	徐志京，汪毅. 青光眼眼底图像的迁移学习分类方法[J]. 计算机工程与应用，2021，57(3)：144－149.
[19]	Ting D, 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[J]. JAMA, 2017, 318(22): 2211－2223.
[20]	Li F, Wang Z, Qu G, et al. Automatic differentiation of glaucoma visual field from non－glaucoma visual filed using deep convolutional neural network[J]. BMC Med Imaging, 2018, 18(1): 35.
[21]	Shibata N, Tanito M, Mitsuhashi K, et al. Development of a deep residual learning algorithm to screen for glaucoma from fundus photography[J]. Sci Rep, 2018, 8(1): 14665.
[22]	Ting D, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations[J]. Prog Retin Eye Res, 2019, 72: 100759.
[23]	Ahn JM, Kim S, Ahn KS, et al. A deep learning model for the detection of both advanced and early glaucoma using fundus photography[J]. PLoS One, 2018, 13(11): e0207982.
[24]	Luo X, Li J, Chen M, et al. Ophthalmic disease detection via deep learning with a novel mixture loss function[J]. IEEE J Biomed Health Inform, 2021, 25(9): 3332－3339.
[25]	Cho H, Hwang YH, Chung JK, et al. Deep learning ensemble method for classifying glaucoma stages using fundus photographs and convolutional neural networks[J]. Curr Eye Res, 2021, 46(10): 1516－1524.
[26]	Kucur ᶊS, Holló G, Sznitman R. A deep learning approach to automatic detection of early glaucoma from visual fields[J]. PLoS One, 2018, 13(11): e0206081.
[27]	Elze T, Pasquale LR, Shen LQ, et al. Patterns of functional vision loss in glaucoma determined with archetypal analysis[J]. J R Soc Interface, 2015, 12(103): 20141118.
[28]	Cai S, Elze T, Bex PJ, et al. Clinical correlates of computationally derived visual field defect archetypes in patients from a glaucoma clinic[J]. Curr Eye Res, 2017, 42(4): 568－574.
[29]	Wang M, Pasquale LR, Shen LQ, et al. Reversal of glaucoma hemifield test results and visual field features in glaucoma[J]. Ophthalmology, 2018, 125(3): 352－360.
[30]	Yousefi S, Goldbaum MH, Balasubramanian M, et al. Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements[J]. IEEE Trans Biomed Eng, 2014, 61(7): 2112－2124.
[31]	Li F, Song D, Chen H, et al. Development and clinical deployment of a smartphone－based visual field deep learning system for glaucoma detection[J]. NPJ Digit Med, 2020, 3: 123.
[32]	Asano S, Asaoka R, Murata H, et al. Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images[J]. Sci Rep, 2021, 11(1): 2214.
[33]	García G, Del－Amor R, Colomer A, et al. Circumpapillary OCT－focused hybrid learning for glaucoma grading using tailored prototypical neural networks[J]. Artif Intell Med, 2021, PMID: 34412848.
[34]	Muhammad H, Fuchs TJ, De－Cuir N, et al. Hybrid deep learning on single wide－field optical coherence tomography scans accurately classifies glaucoma suspects[J]. J Glaucoma, 2017, 26(12): 1086－1094.
[35]	Asaoka R, Murata H, Hirasawa K, et al. Using deep learning and transfer learning to accurately diagnose early－onset glaucoma from macular optical coherence tomography images[J]. Am J Ophthalmol, 2019, 198: 136－145.
[36]	Medeiros FA, Jammal AA, Thompson AC. From machine to machine: an OCT－trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs[J]. Ophthalmology, 2019, 126(4): 513－521.
[37]	Thakoor KA, Koorathota SC, Hood DC, et al. Robust and interpretable convolutional neural networks to detect glaucoma in optical coherence tomography images[J]. IEEE Trans Biomed Eng, 2021, 68(8): 2456－2466.
[38]	Maetschke S, Antony B, Ishikawa H, et al. A feature agnostic approach for glaucoma detection in OCT volumes[J]. PLoS One, 2019, 14(7): e0219126.
[39]	Bowd C, Belghith A, Christopher M, et al. Individualized glaucoma change detection using deep learning auto encoder－based regions of interest[J]. Transl Vis Sci Technol, 2021, 10(8): 19.
[40]	George Y, Antony BJ, Ishikawa H, et al. Attention－guided 3D－CNN framework for glaucoma detection and structural－functional association using volumetric images[J]. IEEE J Biomed Health Inform, 2020, 24(12): 3421－3430.
[41]	Cumba RJ, Radhakrishnan S, Bell NP, et al. Reproducibility of scleral spur identification and angle measurements using fourier domain anterior segment optical coherence tomography[J]. J Ophthalmol, 2012, PMID: 23209880.
[42]	Pham TH, Devalla SK, Ang A, et al. Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images[J]. Br J Ophthalmol, 2021, 105(9): 1231－1237.
[43]	Wang W, Wang L, Wang T, et al. Automatic localization of the scleral spur using deep learning and ultrasound biomicroscopy[J]. Transl Vis Sci Technol, 2021, 10(9): 28.
[44]	Xiong J, Li F, Song D, et al. Multi－modal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy[J]. Ophthalmology, 2021, 129(2): 171－180.
[45]	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[J]. Ophthalmology, 2021, 128(11): 1534－1548.
[46]	Park K, Kim J, Kim S, et al. Prediction of visual field from swept－source optical coherence tomography using deep learning algorithms[J]. Graefes Arch Clin Exp Ophthalmol, 2020, 258(11): 2489－2499.
[47]	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[J]. Am J Ophthalmol, 2020, 211: 123－131.
[48]	Devalla SK, Liang Z, Pham TH, et al. Glaucoma management in the era of artificial intelligence[J]. Br J Ophthalmol, 2020, 104(3): 301－311.
[49]	Chen A, Liu L, Wang J, et al. Measuring glaucomatous focal perfusion loss in the peripapillary retina using OCT angiography[J]. Ophthalmology, 2020, 127(4): 484－491.
[50]	Hou H, Moghimi S, Proudfoot JA, et al. Ganglion cell complex thickness and macular vessel density loss in primary open－angle glaucoma[J]. Ophthalmology, 2020, 127(8): 1043－1052.
[51]	Harris A, Guidoboni G, Siesky B, et al. Ocular blood flow as a clinical observation: value, limitations and data analysis[J]. Prog Retin Eye Res, 2020, PMID: 31987983.
[52]	Lee T, Jammal AA, Mariottoni EB, et al. Predicting glaucoma development with longitudinal deep learning predictions from fundus photographs[J]. Am J Ophthalmol, 2021, 225: 86－94.
[53]	Medeiros FA, Jammal AA, Mariottoni EB. Detection of progressive glaucomatous optic nerve damage on fundus photo－graphs with deep learning[J]. Ophthalmology, 2021, 128(3): 383－392.

Please choose a citation manager

Content to export