Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis
Author:
Corresponding Author:

Ming-Fang Cao and Guang-Hui Liu. Department of Ophthalmology, Affiliated People’s Hospital (Fujian Provincial People’s Hospital), Fujian University of Traditional Chinese Medicine, 602 817 Middle Road, Taijiang District, Fuzhou 350004, Fujian Province, China. farrahcao@126.com; latiny@gmail.com

  • Article
  • | |
  • Metrics
  • |
  • Reference [74]
  • |
  • Related
  • |
  • Cited by
  • | |
  • Comments
    Abstract:

    AIM: To quantify the performance of artificial intelligence (AI) in detecting glaucoma with spectral-domain optical coherence tomography (SD-OCT) images. METHODS: Electronic databases including PubMed, Embase, Scopus, ScienceDirect, ProQuest and Cochrane Library were searched before May 31, 2023 which adopted AI for glaucoma detection with SD-OCT images. All pieces of the literature were screened and extracted by two investigators. Meta-analysis, Meta-regression, subgroup, and publication of bias were conducted by Stata16.0. The risk of bias assessment was performed in Revman5.4 using the QUADAS-2 tool. RESULTS: Twenty studies and 51 models were selected for systematic review and Meta-analysis. The pooled sensitivity and specificity were 0.91 (95%CI: 0.86–0.94, I2=94.67%), 0.90 (95%CI: 0.87–0.92, I2=89.24%). The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 8.79 (95%CI: 6.93–11.15, I2=89.31%) and 0.11 (95%CI: 0.07–0.16, I2=95.25%). The pooled diagnostic odds ratio (DOR) and area under curve (AUC) were 83.58 (95%CI: 47.15–148.15, I2=100%) and 0.95 (95%CI: 0.93–0.97). There was no threshold effect (Spearman correlation coefficient=0.22, P>0.05). CONCLUSION: There is a high accuracy for the detection of glaucoma with AI with SD-OCT images. The application of AI-based algorithms allows together with “doctor+artificial intelligence” to improve the diagnosis of glaucoma.

    Reference
    1 Schuster AK, Erb C, Hoffmann EM, Dietlein T, Pfeiffer N. The diagnosis and treatment of glaucoma. Dtsch Arztebl Int 2020;117(13):225-234.
    2 Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 2006;90(3):262-267.
    3 Weinreb RN, Aung T, Medeiros FA. The pathophysiology and treatment of glaucoma: a review. JAMA 2014;311(18):1901-1911.
    4 Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 2014;121(11):2081-2090.
    5 Weinreb RN, Khaw PT. Primary open-angle glaucoma. Lancet 2004;363(9422):1711-1720.
    6 Inoue R, Hangai M, Kotera Y, Nakanishi H, Mori S, Morishita S, Yoshimura N. Three-dimensional high-speed optical coherence tomography imaging of lamina cribrosa in glaucoma. Ophthalmology 2009;116(2):214-222.
    7 Bussel II, Wollstein G, Schuman JS. OCT for glaucoma diagnosis, screening and detection of glaucoma progression. Br J Ophthalmol 2014;98(Suppl 2):ii15-ii19.
    8 Sung KR, Kim DY, Park SB, Kook MS. Comparison of retinal nerve fiber layer thickness measured by cirrus HD and stratus optical coherence tomography. Ophthalmology 2009;116(7):1264-1270.e1.
    9 Leung CK, Cheung CY, Weinreb RN, et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology 2009;116(7):1257-1263, 1263.e1-1263.e2.
    10 Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H. Artificial intelligence in retina. Prog Retin Eye Res 2018;67:1-29.
    11 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500-510.
    12 Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 2019;69(2):127-157.
    13 Du-Harpur X, Watt FM, Luscombe NM, Lynch MD. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol 2020;183(3):423-430.
    14 Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial intelligence in cardiology. J Am Coll Cardiol 2018;71(23):2668-2679.
    15 Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020;158(1):76-94.e2.
    16 Moraru AD, Costin D, Moraru RL, Branisteanu DC. Artificial intelligence and deep learning in ophthalmology - present and future (Review). Exp Ther Med 2020;20(4):3469-3473.
    17 Li JO, Liu HR, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res 2021;82:100900.
    18 Prum BE Jr, Herndon LW Jr, Moroi SE, et al. Primary angle closure preferred practice pattern® guidelines. Ophthalmology 2016;123(1): P1-P40.
    19 Prum BE, Rosenberg LF, Gedde SJ, et al. Primary open-angle glaucoma preferred practice pattern® guidelines. Ophthalmology 2016;123(1):P41-P111.
    20 Zhang LY, Tang L, Xia M, Cao GF. The application of artificial intelligence in glaucoma diagnosis and prediction. Front Cell Dev Biol 2023;11:1173094.
    21 Spaide T, Wu Y, Yanagihara RT, et al. Using deep learning to automate goldmann applanation tonometry readings. Ophthalmology 2020;127(11):1498-1506.
    22 Dixit A, Yohannan J, Boland MV. Assessing glaucoma progression using machine learning trained on longitudinal visual field and clinical data. Ophthalmology 2021;128(7):1016-1026.
    23 Li F, Su YD, Lin FB, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest 2022;132(11):e157968.
    24 Ting DSW, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res 2019;72:100759.
    25 Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol 2019;8(3):264-272.
    26 McInnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA 2018;319(4):388-396.
    27 Singh LK, Pooja, Garg H, Khanna M. An artificial intelligence-based smart system for early glaucoma recognition using OCT images. Int J E Health Med Commun 2021;12(4):32-59.
    28 Li C, Chua J, Schwarzhans F, et al. Assessing the external validity of machine learning-based detection of glaucoma. Sci Rep 2023;13(1):558.
    29 Thompson AC, Jammal AA, Berchuck SI, Mariottoni EB, Medeiros FA. Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans. JAMA Ophthalmol 2020;138(4):333-339.
    30 Song DP, Li F, Li C, Xiong J, He JJ, Zhang XL, Qiao Y. Asynchronous feature regularization and cross-modal distillation for OCT based glaucoma diagnosis. Comput Biol Med 2022;151(Pt B):106283.
    31 Noury E, Mannil SS, Chang RT, et al. Deep learning for glaucoma detection and identification of novel diagnostic areas in diverse real-world datasets. Transl Vis Sci Technol 2022;11(5):11.
    32 Zheng C, Xie XL, Huang LT, et al. Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model. Graefes Arch Clin Exp Ophthalmol 2020;258(3):577-585.
    33 Kim KE, Kim JM, Song JE, Kee C, Han JC, Hyun SH. Development and validation of a deep learning system for diagnosing glaucoma using optical coherence tomography. J Clin Med 2020;9(7):2167.
    34 Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma. PLoS One 2017;12(5):e0177726.
    35 Lee J, Kim YK, Park KH, Jeoung JW. Diagnosing glaucoma with spectral-domain optical coherence tomography using deep learning classifier. J Glaucoma 2020;29(4):287-294.
    36 Yoshida T, Iwase A, Hirasawa H, Murata H, Mayama C, Araie M, Asaoka R. Discriminating between glaucoma and normal eyes using optical coherence tomography and the ‘Random Forests’ classifier. PLoS One 2014;9(8):e106117.
    37 Sun S, Ha A, Kim YK, Yoo BW, Kim HC, Park KH. Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography. Br J Ophthalmol 2021;105(11):1555-1560.
    38 Oh S, Park Y, Cho KJ, Kim SJ. Explainable machine learning model for glaucoma diagnosis and its interpretation. Diagnostics 2021;11(3):510.
    39 Raja H, Akram MU, Shaukat A, Khan SA, Alghamdi N, Khawaja SG, Nazir N. Extraction of retinal layers through convolution neural network (CNN) in an OCT image for glaucoma diagnosis. J Digit Imaging 2020;33(6):1428-1442.
    40 García G, Colomer A, Naranjo V. Glaucoma detection from raw SD-OCT volumes: a novel approach focused on spatial dependencies. Comput Methods Programs Biomed 2021;200:105855.
    41 Wu CW, Chen HY, Chen JY, Lee CH. Glaucoma detection using support vector machine method based on spectralis OCT. Diagnostics 2022;12(2):391.
    42 Akter N, Fletcher J, Perry S, Simunovic MP, Briggs N, Roy M. Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Sci Rep 2022;12(1):8064.
    43 Barella KA, Costa VP, Gonçalves Vidotti V, Silva FR, Dias M, Gomi ES. Glaucoma diagnostic accuracy of machine learning classifiers using retinal nerve fiber layer and optic nerve data from SD-OCT. J Ophthalmol 2013;2013:789129.
    44 Escamez CSF, Martinez SP, Fernandez NT. High interpretable machine learning classifier for early glaucoma diagnosis. Int J Ophthalmol 2021;14(3):393-398.
    45 Wang PY, Shen J, Chang R, et al. Machine learning models for diagnosing glaucoma from retinal nerve fiber layer thickness maps. Ophthalmol Glaucoma 2019;2(6):422-428.
    46 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. Am J Ophthalmol 2019;198:136-145.
    47 Michels TC, Ivan O. Glaucoma: diagnosis and management. Am Fam Physician 2023;107(3):253-262.
    48 Quigley HA. Glaucoma. Lancet 2011;377(9774):1367-1377.
    49 Lee JWY, Chan PP, Zhang XJ, Chen LJ, Jonas JB. Latest developments in normal-pressure glaucoma: diagnosis, epidemiology, genetics, etiology, causes and mechanisms to management. Asia Pac J Ophthalmol 2019;8(6):457-468.
    50 Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, et al. Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: translation to clinical practice. Transl Vis Sci Technol 2020;9(2):55.
    51 Tatham AJ, Medeiros FA. Detecting structural progression in glaucoma with optical coherence tomography. Ophthalmology 2017;124(12S):S57-S65.
    52 Kang JM, Tanna AP. Glaucoma. Med Clin N Am 2021;105(3):493-510.
    53 Shigueoka LS, Vasconcellos JPC, Schimiti RB, et al. Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma. PLoS One 2018;13(12):e0207784.
    54 Harwerth RS, Wheat JL, Fredette MJ, Anderson DR. Linking structure and function in glaucoma. Prog Retin Eye Res 2010;29(4):249-271.
    55 Yi SL, Zhang G, Qian CX, Lu YQ, Zhong H, He JF. A multimodal classification architecture for the severity diagnosis of glaucoma based on deep learning. Front Neurosci 2022;16:939472.
    56 Huang XQ, Sun J, Gupta K, et al. Detecting glaucoma from multi-modal data using probabilistic deep learning. Front Med 2022;9:923096.
    57 Xiong J, Li F, Song DP, et al. Multimodal machine learning using visual fields and peripapillary circular OCT scans in detection of glaucomatous optic neuropathy. Ophthalmology 2022;129(2):171-180.
    58 Wu CW, Shen HL, Lu CJ, Chen SH, Chen HY. Comparison of different machine learning classifiers for glaucoma diagnosis based on spectralis OCT. Diagnostics 2021;11(9):1718.
    59 Kim MS, Nam KY, Hwang YH, Lee MW, Lee WH, Lim HB, Kim JY. Effect of Weiss ring on peripapillary retinal nerve fiber layer thickness measurements using SD-OCT. Sci Rep 2022;12(1):17357.
    60 Renard JP, Fénolland JR, Giraud JM. Glaucoma progression analysis by Spectral-Domain Optical Coherence Tomography (SD-OCT). J Fr Ophtalmol 2019;42(5):499-516.
    61 Tabuchi H. Understanding required to consider AI applications to the field of ophthalmology. Taiwan J Ophthalmol 2022;12(2):123-129.
    62 Wiggs JL, Pasquale LR. Genetics of glaucoma. Hum Mol Genet 2017;26(R1):R21-R27.
    63 Wang ZX, Wiggs JL, Aung T, Khawaja AP, Khor CC. The genetic basis for adult onset glaucoma: recent advances and future directions. Prog Retin Eye Res 2022;90:101066.
    64 Thompson AC, Jammal AA, Medeiros FA. A review of deep learning for screening, diagnosis, and detection of glaucoma progression. Transl Vis Sci Technol 2020;9(2):42.
    65 Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103(2):167-175.
    66 Hood DC, La Bruna S, Tsamis E, et al. Detecting glaucoma with only OCT: implications for the clinic, research, screening, and AI development. Prog Retin Eye Res 2022;90:101052.
    67 Nakahara K, Asaoka R, Tanito M, et al. Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone. Br J Ophthalmol 2022;106(4): 587-592.
    68 Kashyap R, Nair R, Gangadharan SMP, Botto-Tobar M, Farooq S, Rizwan A. Glaucoma detection and classification using improved U-net deep learning model. Healthcare 2022;10(12):2497.
    69 Vazquez LE, Bye A, Aref AA. Recent developments in the use of optical coherence tomography for glaucoma. Curr Opin Ophthalmol 2021;32(2):98-104.
    70 Pierro L, Gagliardi M, Iuliano L, Ambrosi A, Bandello F. Retinal nerve fiber layer thickness reproducibility using seven different OCT instruments. Invest Ophthalmol Vis Sci 2012;53(9):5912-5920.
    71 Huang JJ, Liu X, Wu ZQ, Guo XX, Xu HZ, Dustin L, Sadda S. Macular and retinal nerve fiber layer thickness measurements in normal eyes with the Stratus OCT, the Cirrus HD-OCT, and the Topcon 3D OCT-1000. J Glaucoma 2011;20(2):118-125.
    72 Kapoor R, Whigham BT, Al-Aswad LA. Artificial intelligence and optical coherence tomography imaging. Asia Pac J Ophthalmol 2019;8(2):187-194.
    73 Yang WH, Shao Y, Xu YW, Expert Workgroup of Guidelines on Clinical Research Evaluation of Artificial Intelligence in Ophthalmology (2023), Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association Intelligent Medicine Committee of Chinese Medicine Education Association. Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023). Int J Ophthalmol 2023;16(9):1361-1372.
    74 Hasan MM, Phu J, Sowmya A, Meijering E, Kalloniatis M. Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases. Clin Exp Optom 2023:1-17.
    Related
    Cited by
Get Citation

Nan-Nan Shi, Jing Li, Guang-Hui Liu,/et al.Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis. Int J Ophthalmol, 2024,17(3):408-419

Copy
Share
Article Metrics
  • Abstract:205
  • PDF: 830
  • HTML: 0
  • Cited by: 0
Publication History
  • Received:August 16,2023
  • Revised:December 18,2023
  • Online: February 27,2024