Application of Artificial Intelligence in Targeting Retinal Diseases

Author(s): Francesco Saverio Sorrentino, Giuseppe Jurman, Katia De Nadai, Claudio Campa, Cesare Furlanello, Francesco Parmeggiani*

Journal Name: Current Drug Targets

Volume 21 , Issue 12 , 2020

  Journal Home
Translate in Chinese
Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Retinal diseases affect an increasing number of patients worldwide because of the aging population. Request for diagnostic imaging in ophthalmology is ramping up, while the number of specialists keeps shrinking. Cutting-edge technology embedding artificial intelligence (AI) algorithms are thus advocated to help ophthalmologists perform their clinical tasks as well as to provide a source for the advancement of novel biomarkers. In particular, optical coherence tomography (OCT) evaluation of the retina can be augmented by algorithms based on machine learning and deep learning to early detect, qualitatively localize and quantitatively measure epi/intra/subretinal abnormalities or pathological features of macular or neural diseases. In this paper, we discuss the use of AI to facilitate efficacy and accuracy of retinal imaging in those diseases increasingly treated by intravitreal vascular endothelial growth factor (VEGF) inhibitors (i.e. anti-VEGF drugs), also including integration and interpretation features in the process. We review recent advances by AI in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity that envision a potentially key role of highly automated systems in screening, early diagnosis, grading and individualized therapy. We discuss benefits and critical aspects of automating the evaluation of disease activity, recurrences, the timing of retreatment and therapeutically potential novel targets in ophthalmology. The impact of massive employment of AI to optimize clinical assistance and encourage tailored therapies for distinct patterns of retinal diseases is also discussed.

Keywords: Retinal diseases, macular complications, anti-VEGF drugs, retinal imaging, optical coherence tomography, artificial intelligence, machine learning, deep learning.

[1]
Schmidt-Erfurth U, Klimscha S, Waldstein SM, Bogunović H. A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration. Eye (Lond) 2017; 31(1): 26-44.
[http://dx.doi.org/10.1038/eye.2016.227] [PMID: 27886184]
[2]
Gualino V, Tadayoni R, Cohen SY, et al. Optical coherence tomography, fluorescein angiography, and diagnosis of choroidal neovascularization in age-related macular degeneration. Retina 2019; 39(9): 1664-71.
[http://dx.doi.org/10.1097/IAE.0000000000002220] [PMID: 30045134]
[3]
Pauleikhoff LJB, Blobner K, Wehrmann K, Feucht N, Lohmann CP, Maier M. Fluorescein, indocyanine green and optical coherence tomography angiography in patients with native exudative age-related macular degeneration. Ophthalmologe 2018; 115(7): 579-84.
[http://dx.doi.org/10.1007/s00347-017-0537-4] [PMID: 28707091]
[4]
Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health 2014; 2(2): e106-16.
[http://dx.doi.org/10.1016/S2214-109X(13)70145-1] [PMID: 25104651]
[5]
Yau JW, Rogers SL, Kawasaki R, et al. Meta-Analysis for Eye Disease (META-EYE) Study Group. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 2012; 35(3): 556-64.
[http://dx.doi.org/10.2337/dc11-1909] [PMID: 22301125]
[6]
Varma R, Bressler NM, Doan QV, et al. Visual impairment and blindness avoided with ranibizumab in hispanic and non-hispanic whites with diabetic macular edema in the United States. Ophthalmology 2015; 122(5): 982-9.
[http://dx.doi.org/10.1016/j.ophtha.2014.12.007] [PMID: 25670501]
[7]
Mehta H, Tufail A, Daien V, et al. Real-world outcomes in patients with neovascular age-related macular degeneration treated with intravitreal vascular endothelial growth factor inhibitors. Prog Retin Eye Res 2018; 65: 127-46.
[http://dx.doi.org/10.1016/j.preteyeres.2017.12.002] [PMID: 29305324]
[8]
Boyer DS, Schmidt-Erfurth U, van Lookeren Campagne M, Henry EC, Brittain C. The pathophysiology of geographic atrophy secondary to age-related macular degeneration and the complement pathway as a therapeutic target. Retina 2017; 37(5): 819-35.
[http://dx.doi.org/10.1097/IAE.0000000000001392] [PMID: 27902638]
[9]
Schmidt-Erfurth U, Waldstein SM. A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog Retin Eye Res 2016; 50: 1-24.
[http://dx.doi.org/10.1016/j.preteyeres.2015.07.007] [PMID: 26307399]
[10]
Fu H, Garvin MK, MacGillivray T, Xu Y, Zheng Y, Eds. Proceedings of 6th International Workshop, OMIA 2019 held in conjunction with MICCAI 2019 Shenzhen. China. 2019.October 17, 2019; Fu H, Garvin MK, MacGillivray T. Xu Y, Zheng Y, Eds.
[11]
Toth CA, Decroos FC, Ying GS, et al. 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 2015; 35(7): 1303-14.
[http://dx.doi.org/10.1097/IAE.0000000000000483] [PMID: 26102433]
[12]
Gerendas BS, Prager S, Deak G, et al. Predictive imaging biomarkers relevant for functional and anatomical outcomes during ranibizumab therapy of diabetic macular oedema. Br J Ophthalmol 2018; 102(2): 195-203.
[http://dx.doi.org/10.1136/bjophthalmol-2017-310483] [PMID: 28724636]
[13]
Spaide RF. Improving the age-related macular degeneration construct: a new classification system. Retina 2018; 38(5): 891-9.
[http://dx.doi.org/10.1097/IAE.0000000000001732] [PMID: 28557901]
[14]
Khanifar AA, Koreishi AF, Izatt JA, Toth CA. Drusen ultrastructure imaging with spectral domain optical coherence tomography in age-related macular degeneration. Ophthalmology 2008; 115(11): 1883-90.
[http://dx.doi.org/10.1016/j.ophtha.2008.04.041] [PMID: 18722666]
[15]
Leuschen JN, Schuman SG, Winter KP, et al. Spectral-domain optical coherence tomography characteristics of intermediate age-related macular degeneration. Ophthalmology 2013; 120(1): 140-50.
[http://dx.doi.org/10.1016/j.ophtha.2012.07.004] [PMID: 22968145]
[16]
Curcio CA, Zanzottera EC, Ach T, Balaratnasingam C, Freund KB. Activated retinal pigment epithelium, an optical coherence tomography biomarker for progression in age-related macular degeneration. Invest Ophthalmol Vis Sci 2017; 58(6): BIO211-26.
[PMID: 28785769]
[17]
Fragiotta S, Rossi T, Cutini A, Grenga PL, Vingolo EM. Predictive factors for development of neovascular age-related macular degeneration: a spectral-domain optical coherence tomography study. Retina 2018; 38(2): 245-52.
[http://dx.doi.org/10.1097/IAE.0000000000001540] [PMID: 28166160]
[18]
Obermeyer Z, Lee TH. Lost in thought - the limits of the human mind and the future of medicine. N Engl J Med 2017; 377(13): 1209-11.
[http://dx.doi.org/10.1056/NEJMp1705348] [PMID: 28953443]
[19]
Waldstein SM, Philip AM, Leitner R, et al. Correlation of 3-dimensionally quantified intraretinal and subretinal fluid with visual acuity in neovascular age-related macular degeneration. JAMA Ophthalmol 2016; 134(2): 182-90.
[http://dx.doi.org/10.1001/jamaophthalmol.2015.4948] [PMID: 26661463]
[20]
Ahuja AS, Halperin LS. Understanding the advent of artificial intelligence in ophthalmology. J Curr Ophthalmol 2019; 31(2): 115-7.
[http://dx.doi.org/10.1016/j.joco.2019.05.001] [PMID: 31317087]
[21]
Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res 2018; 67: 1-29.
[http://dx.doi.org/10.1016/j.preteyeres.2018.07.004] [PMID: 30076935]
[22]
Russell SJ, Norvig P. Artificial intelligence: a modern approach. New Jersey: Inc. A Simon & Schuster Company Englewood Cliffs, Prentice Hall 1995.
[23]
Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 1996; 80(11): 940-4.
[http://dx.doi.org/10.1136/bjo.80.11.940] [PMID: 8976718]
[24]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[25]
Lee R, Wong TY, Sabanayagam C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis (Lond) 2015; 2: 17.
[http://dx.doi.org/10.1186/s40662-015-0026-2] [PMID: 26605370]
[26]
Liu Y, Yang J, Tao L, et al. Risk factors of diabetic retinopathy and sight-threatening diabetic retinopathy: a cross-sectional study of 13 473 patients with type 2 diabetes mellitus in mainland China. BMJ Open 2017; 7(9)e016280
[http://dx.doi.org/10.1136/bmjopen-2017-016280] [PMID: 28864696]
[27]
Sivaprasad S, Pearce E. The unmet need for better risk stratification of non-proliferative diabetic retinopathy. Diabet Med 2019; 36(4): 424-33.
[PMID: 30474144]
[28]
Scanlon PH, Malhotra R, Greenwood RH, et al. Comparison of two reference standards in validating two field mydriatic digital photography as a method of screening for diabetic retinopathy. Br J Ophthalmol 2003; 87(10): 1258-63.
[http://dx.doi.org/10.1136/bjo.87.10.1258] [PMID: 14507762]
[29]
Murray RB, Metcalf SM, Lewis PM, Mein JK, McAllister IL. Sustaining remote-area programs: retinal camera use by Aboriginal health workers and nurses in a Kimberley partnership. Med J Aust 2005; 182(10): 520-3.
[http://dx.doi.org/10.5694/j.1326-5377.2005.tb00018.x] [PMID: 15896180]
[30]
Ting DS, Tay-Kearney ML, Constable I, Lim L, Preen DB, Kanagasingam Y. Retinal video recording a new way to image and diagnose diabetic retinopathy. Ophthalmology 2011; 118(8): 1588-93.
[http://dx.doi.org/10.1016/j.ophtha.2011.04.009] [PMID: 21684608]
[31]
Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol 2016; 44(4): 260-77.
[http://dx.doi.org/10.1111/ceo.12696] [PMID: 26716602]
[32]
Abràmoff 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(13): 5200-6.
[http://dx.doi.org/10.1167/iovs.16-19964] [PMID: 27701631]
[33]
Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 2017; 124(7): 962-9.
[http://dx.doi.org/10.1016/j.ophtha.2017.02.008] [PMID: 28359545]
[34]
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(22): 2402-10.
[http://dx.doi.org/10.1001/jama.2016.17216] [PMID: 27898976]
[35]
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(22): 2211-23.
[http://dx.doi.org/10.1001/jama.2017.18152] [PMID: 29234807]
[36]
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(3): 343-51.
[http://dx.doi.org/10.1016/j.ophtha.2016.11.014] [PMID: 28024825]
[37]
Colijn JM, Buitendijk GHS, Prokofyeva E, et al. EYE-RISK consortium. European Eye Epidemiology (E3) consortium. Prevalence of Age-Related Macular Degeneration in Europe: The Past and the Future. Ophthalmology 2017; 124(12): 1753-63.
[http://dx.doi.org/10.1016/j.ophtha.2017.05.035] [PMID: 28712657]
[38]
Pennington KL, DeAngelis MM. Epidemiology of age-related macular degeneration (AMD): associations with cardiovascular disease phenotypes and lipid factors. Eye Vis (Lond) 2016; 3: 34.
[http://dx.doi.org/10.1186/s40662-016-0063-5] [PMID: 28032115]
[39]
Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: The technical and clinical considerations. Prog Retin Eye Res 2019.72100759
[http://dx.doi.org/10.1016/j.preteyeres.2019.04.003] [PMID: 31048019]
[40]
Lee CS, Baughman DM, Lee AY. Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmol Retina 2017; 1: 322-7.
[http://dx.doi.org/10.1016/j.oret.2016.12.009] [PMID: 30693348]
[41]
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(2): 259-65.
[http://dx.doi.org/10.1007/s00417-017-3850-3] [PMID: 29159541]
[42]
Roy AG, Conjeti S, Karri SPK, et al. ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 2017; 8(8): 3627-42.
[http://dx.doi.org/10.1364/BOE.8.003627] [PMID: 28856040]
[43]
Venhuizen FG, van Ginneken B, Liefers B, et al. Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. Biomed Opt Express 2018; 9(4): 1545-69.
[http://dx.doi.org/10.1364/BOE.9.001545] [PMID: 29675301]
[44]
Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt Express 2017; 8(5): 2732-44.
[http://dx.doi.org/10.1364/BOE.8.002732] [PMID: 28663902]
[45]
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018; 24(9): 1342-50.
[http://dx.doi.org/10.1038/s41591-018-0107-6] [PMID: 30104768]
[46]
Hamwood J, Alonso-Caneiro D, Read SA, Vincent SJ, Collins MJ. Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. Biomed Opt Express 2018; 9(7): 3049-66.
[http://dx.doi.org/10.1364/BOE.9.003049] [PMID: 29984082]
[47]
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(11): 5832-51.
[http://dx.doi.org/10.1364/BOE.10.005832] [PMID: 31799050]
[48]
Schlegl T, Waldstein SM, Bogunovic H, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 2018; 125(4): 549-58.
[http://dx.doi.org/10.1016/j.ophtha.2017.10.031] [PMID: 29224926]
[49]
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(4): 565-75.
[http://dx.doi.org/10.1016/j.ophtha.2018.11.015] [PMID: 30471319]
[50]
Blencowe H, Moxon S, Gilbert C. Update on blindness due to retinopathy of prematurity globally and in India. Indian Pediatr 2016; 53(Suppl. 2): S89-92.
[PMID: 27915313]
[51]
Cryotherapy for Retinopathy of Prematurity Cooperative Group. Multicenter Trial of Cryotherapy for Retinopathy of Prematurity: ophthalmological outcomes at 10 years. Arch Ophthalmol 2001; 119(8): 1110-8.
[http://dx.doi.org/10.1001/archopht.119.8.1110] [PMID: 11483076]
[52]
Gilbert C, Rahi J, Eckstein M, O’Sullivan J, Foster A. Retinopathy of prematurity in middle-income countries. Lancet 1997; 350(9070): 12-4.
[http://dx.doi.org/10.1016/S0140-6736(97)01107-0] [PMID: 9217713]
[53]
Fleck BW, Williams C, Juszczak E, et al. BOOST II Retinal Image Digital Analysis (RIDA) Group. An international comparison of retinopathy of prematurity grading performance within the Benefits of Oxygen Saturation Targeting II trials. Eye (Lond) 2018; 32(1): 74-80.
[http://dx.doi.org/10.1038/eye.2017.150] [PMID: 28752837]
[54]
Daniel E, Quinn GE, Hildebrand PL, et al. e-ROP Cooperative Group. Validated system for centralized grading of retinopathy of prematurity: telemedicine approaches to evaluating acute-phase retinopathy of prematurity (e-ROP) study. JAMA Ophthalmol 2015; 133(6): 675-82.
[http://dx.doi.org/10.1001/jamaophthalmol.2015.0460] [PMID: 25811772]
[55]
Worrall DE, Wilson CM, Brostow GJ. Automated retinopathy of prematurity case detection with convolutional neural networks. Deep Learning and Data Labeling for Medical Applications DLMIA, LABELS, Springer Lecture Notes in Computer ScienceCham 2016. 10008.
[http://dx.doi.org/10.1007/978-3-319-46976-8_8]
[56]
Brown JM, Campbell JP, Beers A, et al. Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 2018; 136(7): 803-10.
[http://dx.doi.org/10.1001/jamaophthalmol.2018.1934] [PMID: 29801159]
[57]
Brown JM, Campbell JP, Beers A. Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning Proceedings volume 10579, medical imaging 2018: imaging informatics for healthcare, research, and applications 2018.
[http://dx.doi.org/10.1117/12.2295942]
[58]
Ataer-Cansizoglu E, Bolon-Canedo V, Campbell JP, et al. i-ROP Research Consortium. 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. Transl Vis Sci Technol 2015; 4(6): 5.
[http://dx.doi.org/10.1167/tvst.4.6.5] [PMID: 26644965]
[59]
Campbell JP, Ataer-Cansizoglu E, Bolon-Canedo V, et al. Imaging and Informatics in ROP (i-ROP) Research Consortium. Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis. JAMA Ophthalmol 2016; 134(6): 651-7.
[http://dx.doi.org/10.1001/jamaophthalmol.2016.0611] [PMID: 27077667]
[60]
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019; 17(1): 195.
[http://dx.doi.org/10.1186/s12916-019-1426-2] [PMID: 31665002]
[61]
Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 2018; 15(141): 141.
[http://dx.doi.org/10.1098/rsif.2017.0387] [PMID: 29618526]
[62]
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med 2019; 25(1): 24-9.
[http://dx.doi.org/10.1038/s41591-018-0316-z] [PMID: 30617335]
[63]
Arcadu F, Benmansour F, Maunz A, et al. Deep Learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci 2019; 60(4): 852-7.
[http://dx.doi.org/10.1167/iovs.18-25634] [PMID: 30821810]
[64]
Li Z, Guo C, Nie D, et al. Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images. Commun Biol 2020; 3(1): 15.
[http://dx.doi.org/10.1038/s42003-019-0730-x] [PMID: 31925315]
[65]
Gulshan V, Rajan RP, Widner K, et al. Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India. JAMA Ophthalmol 2019; 10.
[http://dx.doi.org/10.1001/jamaophthalmol.2019.2004] [PMID: 31194246]
[66]
Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V. Ophthalmic diagnosis using deep learning with fundus images - A critical review. Artif Intell Med 2020.102101758
[http://dx.doi.org/10.1016/j.artmed.2019.101758] [PMID: 31980096]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 21
ISSUE: 12
Year: 2020
Published on: 18 September, 2020
Page: [1208 - 1215]
Pages: 8
DOI: 10.2174/1389450121666200708120646
Price: $65

Article Metrics

PDF: 33
HTML: 1