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Jiravarnsirikul A, Belghith A, Rezapour J, Bowd C, Moghimi S, Jonas JB, Christopher M, Fazio MA, Yang H, Burgoyne CF, Weinreb RN, Zangwill LM. Evaluating glaucoma in myopic eyes: Challenges and opportunities. Surv Ophthalmol 2025; 70:563-582. [PMID: 39701308 DOI: 10.1016/j.survophthal.2024.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 12/09/2024] [Accepted: 12/11/2024] [Indexed: 12/21/2024]
Abstract
The increasing global prevalence of myopia presents a significant public health concern, and growing evidence has demonstrated that myopia is a major risk factor for the development of open-angle glaucoma. Therefore, timely detection and management of glaucoma in myopic patients are crucial; however, identifying the structural alterations of glaucoma in the optic nerve head (ONH) and retinal tissues of myopic eyes using standard diagnostic tools such as fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA) presents challenges. Additionally, myopia-related perimetric defects can be confounded with glaucoma-related defects. We comprehensively examine the challenges encountered in evaluating glaucoma in myopic eyes through various diagnostic tools, including fundus photography, OCT of the ONH, retinal nerve fiber layer, and macular ganglion cell layer, OCTA, and perimetry. We also explore potential opportunities to address these challenges, providing insights for clinicians to effectively manage myopic glaucoma patients in clinical practice.
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Affiliation(s)
- Anuwat Jiravarnsirikul
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States; Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Akram Belghith
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States
| | - Jasmin Rezapour
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States; Department of Ophthalmology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
| | - Christopher Bowd
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States
| | - Sasan Moghimi
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States
| | - Jost B Jonas
- Institut Français de Myopie, Hôpital Fondation Rothschild, 44 Avenue Mathurin Moreau, Paris 75019, France; Singapore Eye Research Institute, Singapore National Eye Center, Singapore; Privatpraxis Prof Jonas und Dr. Panda-Jonas, Heidelberg, Germany
| | - Mark Christopher
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States
| | - Massimo A Fazio
- Department of Ophthalmology and Vision Science, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, United States; Department of Biomedical Engineering, School of Engineering, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Hongli Yang
- Devers Eye Institute, Optic Nerve Head Research Laboratory, Legacy Research Institute, Portland, OR, United States
| | - Claude F Burgoyne
- Devers Eye Institute, Optic Nerve Head Research Laboratory, Legacy Research Institute, Portland, OR, United States
| | - Robert N Weinreb
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, CA, United States.
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Zuo H, Huang B, He J, Fang L, Huang M. Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis. J Med Internet Res 2025; 27:e57644. [PMID: 39753217 PMCID: PMC11748443 DOI: 10.2196/57644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 07/02/2024] [Accepted: 11/06/2024] [Indexed: 01/24/2025] Open
Abstract
BACKGROUND In recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. Some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility. OBJECTIVE This study aims to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice, thereby providing evidence-based support for the future development and refinement of intelligent diagnostic or predictive tools. METHODS PubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The prediction model risk of bias assessment tool was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented using a bivariate mixed-effects model. In the validation set, subgroup analyses were conducted based on the ML target events (diagnosis and prediction of high myopia and diagnosis of pathological myopia and high myopia-associated glaucoma) and modeling methods. RESULTS This study ultimately included 45 studies, of which 32 were used for quantitative meta-analysis. The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI 0.95-0.98), 0.91 (95% CI 0.89-0.92), and 0.95 (95% CI 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI 0.95-0.98), sensitivity of 0.92 (95% CI 0.90-0.93), and specificity of 0.96 (95% CI 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI 0.75-0.92), sensitivity of 0.77 (95% CI 0.69-0.84), and specificity of 0.85 (95% CI 0.75-0.92). For the diagnosis and prediction of high myopia, the SROC, sensitivity, and specificity of ML were 0.98 (95% CI 0.96-0.99), 0.94 (95% CI 0.90-0.96), and 0.94 (95% CI 0.88-0.97), respectively. For the diagnosis of high myopia-associated glaucoma, the SROC, sensitivity, and specificity of ML were 0.96 (95% CI 0.94-0.97), 0.92 (95% CI 0.85-0.96), and 0.88 (95% CI 0.67-0.96), respectively. CONCLUSIONS ML demonstrated highly promising accuracy in diagnosing high myopia and pathologic myopia. Moreover, based on the limited evidence available, we also found that ML appeared to have favorable accuracy in predicting the risk of developing high myopia in the future. DL can be used as a potential method for intelligent image processing and intelligent recognition, and intelligent examination tools can be developed in subsequent research to provide help for areas where medical resources are scarce. TRIAL REGISTRATION PROSPERO CRD42023470820; https://tinyurl.com/2xexp738.
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Affiliation(s)
- Huiyi Zuo
- Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China
| | - Baoyu Huang
- Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China
| | - Jian He
- Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China
| | - Liying Fang
- Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China
| | - Minli Huang
- Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, China
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
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Cakir I, Altan C, Yalcinkaya G, Tellioglu A, Yilmaz E, Alagoz N, Taskapili M. Optic disc tilt and rotation effects on positions of superotemporal and inferotemporal retinal nerve fibre layer peaks in myopic Caucasians. Clin Exp Optom 2023; 106:845-851. [PMID: 36822600 DOI: 10.1080/08164622.2023.2171772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/18/2023] [Indexed: 02/25/2023] Open
Abstract
CLINICAL RELEVANCE In myopic eyes, the optic disc may become tilted and rotated, making glaucoma diagnosis more difficult. BACKGROUND To determine the presence of tilted optic disc, the degree of optic disc rotation, and their effects on the angular location of superotemporal and inferotemporal retinal nerve fibre layer (RNFL) peaks in healthy myopic Caucasians. METHODS Non-glaucomatous healthy myopic Caucasian eyes with an axial length > 24 mm were evaluated. ImageJ was used to quantify optic disc tilt and torsion on red-free fundus photography. The RNFL was scanned using spectral-domain optical coherence tomography. The angle of the superotemporal and inferotemporal peaks with the vertical-horizontal meridian was measured. RESULTS Fifty-four eyes of 54 individuals were evaluated. The axial length was correlated with the angular location for both the superotemporal (r = -0.549, p < 0.001) and inferotemporal (r = -0.415, p = 0.002) RNFL peaks; they were placed more temporally in eyes with higher axial lengths. For each 1 mm increase in axial length, the angle between the superotemporal peak and the temporal horizontal meridian decreased by 3.976°, and the angle between the inferotemporal apex and the temporal horizontal meridian decreased by 3.028°. The angle between the inferotemporal peak and the temporal horizontal meridian decreased by 0.231° for each 1° increase in optical disc torsion (R2 = 0.09 Regression coefficient = -0.231, p = 0.027). CONCLUSIONS The temporal shift of superior and inferior peaks, the thickening of temporal and nasal RNFL, the presence of tilted optic disc, and optic disc rotation may cause misinterpretation of the RNFL in myopic Caucasians. When evaluating peripapillary RNFL thickness in myopic individuals, it would be better to consider these to avoid misinterpretation.
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Affiliation(s)
- Ihsan Cakir
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
| | - Cigdem Altan
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
| | - Gulay Yalcinkaya
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
| | - Adem Tellioglu
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
| | - Ege Yilmaz
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
| | - Nese Alagoz
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
| | - Muhittin Taskapili
- Department of Ophthalmology, University of Health Sciences, Istanbul, Turkey; Beyoglu Eye Training and Research Hospital, İstanbul, Turkey
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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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Wu Y, Szymanska M, Hu Y, Fazal MI, Jiang N, Yetisen AK, Cordeiro MF. Measures of disease activity in glaucoma. Biosens Bioelectron 2021; 196:113700. [PMID: 34653715 DOI: 10.1016/j.bios.2021.113700] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/01/2021] [Accepted: 10/08/2021] [Indexed: 12/13/2022]
Abstract
Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.
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Affiliation(s)
- Yue Wu
- Department of Surgery and Cancer, Imperial College London, South Kensington, London, United Kingdom; Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom
| | - Maja Szymanska
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom
| | - Yubing Hu
- Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom.
| | - M Ihsan Fazal
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom
| | - Nan Jiang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Ali K Yetisen
- Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom
| | - M Francesca Cordeiro
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom; The Western Eye Hospital, Imperial College Healthcare NHS Trust (ICHNT), London, United Kingdom; Glaucoma and Retinal Neurodegeneration Group, Department of Visual Neuroscience, UCL Institute of Ophthalmology, London, United Kingdom.
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