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Ng Yin Ling C, Zhu X, Ang M. Artificial intelligence in myopia in children: current trends and future directions. Curr Opin Ophthalmol 2024; 35:463-471. [PMID: 39259652 DOI: 10.1097/icu.0000000000001086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
PURPOSE OF REVIEW Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management. RECENT FINDINGS The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment. SUMMARY Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.
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Affiliation(s)
| | - Xiangjia Zhu
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University
- NHC Key Laboratory of Myopia and Related Eye Diseases; Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences
- Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, China
| | - Marcus Ang
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, Singapore
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Tan B, Chua J, Wong D, Liu X, Ismail M, Schmetterer L. Techniques for imaging the choroid and choroidal blood flow in vivo. Exp Eye Res 2024; 247:110045. [PMID: 39154819 DOI: 10.1016/j.exer.2024.110045] [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: 05/26/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 08/20/2024]
Abstract
The choroid, which is a highly vascularized layer between the retina and sclera, is essential for supplying oxygen and nutrients to the outer retina. Choroidal vascular dysfunction has been implicated in numerous ocular diseases, including age-related macular degeneration, central serous chorioretinopathy, polypoidal choroidal vasculopathy, and myopia. Traditionally, the in vivo assessment of choroidal blood flow relies on techniques such as laser Doppler flowmetry, laser speckle flowgraphy, pneumotonometry, laser interferometry, and ultrasonic color Doppler imaging. While the aforementioned methods have provided valuable insights into choroidal blood flow regulation, their clinical applications have been limited. Recent advancements in optical coherence tomography and optical coherence tomography angiography have expanded our understanding of the choroid, allowing detailed visualization of the larger choroidal vessels and choriocapillaris, respectively. This review provides an overview of the available techniques that can investigate the choroid and its blood flow in vivo. Future research should combine these techniques to comprehensively image the entire choroidal microcirculation and develop robust methods to quantify choroidal blood flow. The potential findings will provide a better picture of choroidal hemodynamics and its effect on ocular health and disease.
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Affiliation(s)
- Bingyao Tan
- Singapore Eye Research Institute, National Eye Centre, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, National Eye Centre, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Damon Wong
- Singapore Eye Research Institute, National Eye Centre, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Xinyu Liu
- Singapore Eye Research Institute, National Eye Centre, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Munirah Ismail
- Singapore Eye Research Institute, National Eye Centre, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, National Eye Centre, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; School of Chemical and Biomedical Engineering, Nanyang Technological University (NTU), Singapore; Centre for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria; Rothschild Foundation Hospital, Paris, France.
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Meng J, Song Y, He W, Lu ZL, Chen Y, Wei L, Zhang K, Qi J, Du Y, Lu Y, Zhu X. A Novel Artificial Intelligence-Based Classification of Highly Myopic Eyes Based on Visual Function and Fundus Features. Transl Vis Sci Technol 2024; 13:12. [PMID: 39235401 PMCID: PMC11379094 DOI: 10.1167/tvst.13.9.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Purpose To develop a novel classification of highly myopic eyes using artificial intelligence (AI) and investigate its relationship with contrast sensitivity function (CSF) and fundus features. Methods We enrolled 616 highly myopic eyes of 616 patients. CSF was measured using the quantitative CSF method. Myopic macular degeneration (MMD) was graded according to the International META-PM Classification. Thickness of the macula and peripapillary retinal nerve fiber layer (p-RNFL) were assessed by fundus photography and optical coherence tomography, respectively. Classification was performed by combining CSF and fundus features with principal component analysis and k-means clustering. Results With 83.35% total variance explained, highly myopic eyes were classified into four AI categories. The percentages of AI categories 1 to 4 were 14.9%, 37.5%, 36.2%, and 11.4%, respectively. Contrast acuity of the eyes in AI category 1 was the highest, which decreased by half in AI category 2. For AI categories 2 to 4, every increase in category led to a decrease of 0.23 logarithm of the minimum angle of resolution in contrast acuity. Compared with those in AI category 1, eyes in AI category 2 presented a higher percentage of MMD2 and thinner temporal p-RNFL. Eyes in AI categories 3 and 4 presented significantly higher percentage of MMD ≥ 3, thinner nasal macular thickness and p-RNFL (P < 0.05). Multivariate regression showed AI category 4 had higher MMD grades and thinner macular compared with AI category 3. Conclusions We proposed an AI-based classification of highly myopic eyes with clear relevance to visual function and fundus features. Translational Relevance This classification helps to discover the early hidden visual deficits of highly myopic patients, becoming a useful tool to evaluate the disease comprehensively.
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Affiliation(s)
- Jiaqi Meng
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Yunxiao Song
- University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Wenwen He
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Zhong-Lin Lu
- Division of Arts and Sciences, New York University Shanghai, Shanghai, China
- Center for Neural Science and Department of Psychology, New York University, New York, USA
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
| | - Yuxi Chen
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Ling Wei
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Keke Zhang
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Jiao Qi
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Yu Du
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Yi Lu
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Xiangjia Zhu
- Eye Institute, Eye and Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China
- Key Laboratory of Myopia, Ministry of Health, Shanghai, China
- Key Laboratory of Visual Impairment and Restoration, Shanghai, China
- Key NHC key Laboratory of Myopia (Fudan University), Shanghai, China
- Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
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Ye H, Tang R, Fang W, Di Y, Qiao T. Clinical outcomes of posterior scleral reinforcement in Chinese high myopia children. Sci Rep 2024; 14:16479. [PMID: 39013945 PMCID: PMC11252263 DOI: 10.1038/s41598-024-67078-7] [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: 12/28/2023] [Accepted: 07/08/2024] [Indexed: 07/18/2024] Open
Abstract
We aim to observe the posterior scleral reinforcement (PSR) clinical outcomes of children with high myopia and analyze the retinal vessel alteration before and after PSR by using angiography optical coherence tomography (angio-OCT). Fifty-six pediatric participants (112 eyes) clinically diagnosed high myopia were recruited and were treated by PSR in Shanghai Children's Hospital from June 1, 2021 to May 1, 2023. The average age ranged from 5.42 to 14.83 years (mean 8.83 years) and mean follow up duration was 8.7 months (3-24 months). The axial length (AL) was significantly shortened after PSR (p < 0.05). The spherical equivalent (SE) and the best-corrected visual acuity (BCVA) were also improved without severe rejection in the follow-up. Compared with baseline, angio-OCT parafoveal vessel indices including vascular area density (VAD) and vascular skeleton density (VSD) on the superficial capillary plexus layer (SCPL), as well as VAD and vessel perimeter index (VPI) on the deep capillary plexus layer (DCPL), were significantly increased after PSR surgery (p < 0.05). VPI on the SCPL, vascular diameter index (VDI) and VSD on the DCPL were also improved without statistical difference after PSR. The VSD on SCPL, VAD on DCPL of the right eyes and the VPI on SCPL of the left eyes were significantly increased after PSR (p < 0.05). PSR surgery can shorten the AL and can stable BCVA and SE in high myopia children. The angio-OCT parameters indicated that the retinal microcirculation supply was significantly improved after PSR.
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Affiliation(s)
- Haiyun Ye
- Department of Ophthalmology, Shanghai Children's Hospital, School of medicine, Shanghai Jiao Tong University, No. 355 Luding Road, Shanghai, 200062, China
| | - Ruizhi Tang
- Ghent University Centre for X-Ray Tomography (UGCT), Proeftuinstraat 86/N12, 9000, Ghent, Belgium
| | - Wangyi Fang
- Department of Ophthalmology, Shanghai Children's Hospital, School of medicine, Shanghai Jiao Tong University, No. 355 Luding Road, Shanghai, 200062, China
| | - Yue Di
- Department of Ophthalmology, Shanghai Children's Hospital, School of medicine, Shanghai Jiao Tong University, No. 355 Luding Road, Shanghai, 200062, China
| | - Tong Qiao
- Department of Ophthalmology, Shanghai Children's Hospital, School of medicine, Shanghai Jiao Tong University, No. 355 Luding Road, Shanghai, 200062, China.
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Yii F, Nguyen L, Strang N, Bernabeu MO, Tatham AJ, MacGillivray T, Dhillon B. Factors associated with pathologic myopia onset and progression: A systematic review and meta-analysis. Ophthalmic Physiol Opt 2024; 44:963-976. [PMID: 38563652 DOI: 10.1111/opo.13312] [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: 10/04/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE To synthesise evidence across studies on factors associated with pathologic myopia (PM) onset and progression based on the META-analysis for Pathologic Myopia (META-PM) classification framework. METHODS Findings from six longitudinal studies (5-18 years) were narratively synthesised and meta-analysed, using odds ratio (OR) as the common measure of association. All studies adjusted for baseline myopia, age and sex at a minimum. The quality of evidence was rated using the Grades of Recommendation, Assessment, Development and Evaluation framework. RESULTS Five out of six studies were conducted in Asia. There was inconclusive evidence of an independent effect (or lack thereof) of ethnicity and sex on PM onset/progression. The odds of PM onset increased with greater axial length (pooled OR: 2.03; 95% CI: 1.71-2.40; p < 0.001), older age (pooled OR: 1.07; 1.05-1.09; p < 0.001) and more negative spherical equivalent refraction, SER (OR: 0.77; 0.68-0.87; p < 0.001), all of which were supported by an acceptable level of evidence. Fundus tessellation was found to independently increase the odds of PM onset in a population-based study (OR: 3.02; 2.58-3.53; p < 0.001), although this was only supported by weak evidence. There was acceptable evidence that greater axial length (pooled OR: 1.23; 1.09-1.39; p < 0.001), more negative SER (pooled OR: 0.87; 0.83-0.92; p < 0.001) and higher education level (pooled OR: 3.17; 1.36-7.35; p < 0.01) increased the odds of PM progression. Other baseline factors found to be associated with PM progression but currently supported by weak evidence included age (pooled OR: 1.01), severity of myopic maculopathy (OR: 3.61), intraocular pressure (OR: 1.62) and hypertension (OR: 0.21). CONCLUSIONS Most PM risk/prognostic factors are not supported by an adequate evidence base at present (an indication that PM remains understudied). Current factors for which an acceptable level of evidence exists (limited in number) are unmodifiable in adults and lack personalised information. More longitudinal studies focusing on uncovering modifiable factors and imaging biomarkers are warranted.
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Affiliation(s)
- Fabian Yii
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Curle Ophthalmology Laboratory, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - Linda Nguyen
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Niall Strang
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, UK
| | - Miguel O Bernabeu
- Centre for Medical Informatics, Usher Institute, The University of Edinburgh, Edinburgh, UK
- The Bayes Centre, The University of Edinburgh, Edinburgh, UK
| | - Andrew J Tatham
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Princess Alexandra Eye Pavilion, NHS Lothian, Edinburgh, UK
| | - Tom MacGillivray
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Curle Ophthalmology Laboratory, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
| | - Baljean Dhillon
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Curle Ophthalmology Laboratory, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK
- Princess Alexandra Eye Pavilion, NHS Lothian, Edinburgh, UK
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Li Y, Yip M, Ning Y, Chung J, Toh A, Leow C, Liu N, Ting D, Schmetterer L, Saw SM, Jonas JB, Chia A, Ang M. Topical Atropine for Childhood Myopia Control: The Atropine Treatment Long-Term Assessment Study. JAMA Ophthalmol 2024; 142:15-23. [PMID: 38019503 PMCID: PMC10690578 DOI: 10.1001/jamaophthalmol.2023.5467] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 10/05/2023] [Indexed: 11/30/2023]
Abstract
Importance Clinical trial results of topical atropine eye drops for childhood myopia control have shown inconsistent outcomes across short-term studies, with little long-term safety or other outcomes reported. Objective To report the long-term safety and outcomes of topical atropine for childhood myopia control. Design, Setting, and Participants This prospective, double-masked observational study of the Atropine for the Treatment of Myopia (ATOM) 1 and ATOM2 randomized clinical trials took place at 2 single centers and included adults reviewed in 2021 through 2022 from the ATOM1 study (atropine 1% vs placebo; 1999 through 2003) and the ATOM2 study (atropine 0.01% vs 0.1% vs 0.5%; 2006 through 2012). Main Outcome Measures Change in cycloplegic spherical equivalent (SE) with axial length (AL); incidence of ocular complications. Results Among the original 400 participants in each original cohort, the study team evaluated 71 of 400 ATOM1 adult participants (17.8% of original cohort; study age, mean [SD] 30.5 [1.2] years; 40.6% female) and 158 of 400 ATOM2 adult participants (39.5% of original cohort; study age, mean [SD], 24.5 [1.5] years; 42.9% female) whose baseline characteristics (SE and AL) were representative of the original cohort. In this study, evaluating ATOM1 participants, the mean (SD) SE and AL were -5.20 (2.46) diopters (D), 25.87 (1.23) mm and -6.00 (1.63) D, 25.90 (1.21) mm in the 1% atropine-treated and placebo groups, respectively (difference of SE, 0.80 D; 95% CI, -0.25 to 1.85 D; P = .13; difference of AL, -0.03 mm; 95% CI, -0.65 to 0.58 mm; P = .92). In ATOM2 participants, the mean (SD) SE and AL was -6.40 (2.21) D; 26.25 (1.34) mm; -6.81 (1.92) D, 26.28 (0.99) mm; and -7.19 (2.87) D, 26.31 (1.31) mm in the 0.01%, 0.1%, and 0.5% atropine groups, respectively. There was no difference in the 20-year incidence of cataract/lens opacities, myopic macular degeneration, or parapapillary atrophy (β/γ zone) comparing the 1% atropine-treated group vs the placebo group. Conclusions and Relevance Among approximately one-quarter of the original participants, use of short-term topical atropine eye drops ranging from 0.01% to 1.0% for a duration of 2 to 4 years during childhood was not associated with differences in final refractive errors 10 to 20 years after treatment. There was no increased incidence of treatment or myopia-related ocular complications in the 1% atropine-treated group vs the placebo group. These findings may affect the design of future clinical trials, as further studies are required to investigate the duration and concentration of atropine for childhood myopia control.
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Affiliation(s)
- Yong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Michelle Yip
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yilin Ning
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Joey Chung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Angeline Toh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Cheryl Leow
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Daniel Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore
- Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Seang-Mei Saw
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Jost B. Jonas
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Audrey Chia
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
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Wang Y, Du R, Xie S, Chen C, Lu H, Xiong J, Ting DSW, Uramoto K, Kamoi K, Ohno-Matsui K. Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes. JAMA Ophthalmol 2023; 141:1117-1124. [PMID: 37883115 PMCID: PMC10603576 DOI: 10.1001/jamaophthalmol.2023.4786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/01/2023] [Indexed: 10/27/2023]
Abstract
Importance High myopia is a global concern due to its escalating prevalence and the potential risk of severe visual impairment caused by pathologic myopia. Using artificial intelligence to estimate future visual acuity (VA) could help clinicians to identify and monitor patients with a high risk of vision reduction in advance. Objective To develop machine learning models to predict VA at 3 and 5 years in patients with high myopia. Design, Setting, and Participants This retrospective, single-center, cohort study was performed on patients whose best-corrected VA (BCVA) at 3 and 5 years was known. The ophthalmic examinations of these patients were performed between October 2011 and May 2021. Thirty-four variables, including general information, basic ophthalmic information, and categories of myopic maculopathy based on fundus and optical coherence tomography images, were collected from the medical records for analysis. Main Outcomes and Measures Regression models were developed to predict BCVA at 3 and 5 years, and a binary classification model was developed to predict the risk of developing visual impairment at 5 years. The performance of models was evaluated by discrimination metrics, calibration belts, and decision curve analysis. The importance of relative variables was assessed by explainable artificial intelligence techniques. Results A total of 1616 eyes from 967 patients (mean [SD] age, 58.5 [14.0] years; 678 female [70.1%]) were included in this analysis. Findings showed that support vector machines presented the best prediction of BCVA at 3 years (R2 = 0.682; 95% CI, 0.625-0.733) and random forest at 5 years (R2 = 0.660; 95% CI, 0.604-0.710). To predict the risk of visual impairment at 5 years, logistic regression presented the best performance (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.816-0.912). The baseline BCVA (logMAR odds ratio [OR], 0.298; 95% CI, 0.235-0.378; P < .001), prior myopic macular neovascularization (OR, 3.290; 95% CI, 2.209-4.899; P < .001), age (OR, 1.578; 95% CI, 1.227-2.028; P < .001), and category 4 myopic maculopathy (OR, 4.899; 95% CI, 1.431-16.769; P = .01) were the 4 most important predicting variables and associated with increased risk of visual impairment at 5 years. Conclusions and Relevance Study results suggest that developing models for accurate prediction of the long-term VA for highly myopic eyes based on clinical and imaging information is feasible. Such models could be used for the clinical assessments of future visual acuity.
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Affiliation(s)
- Yining Wang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Ophthalmology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China
| | - Shiqi Xie
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Changyu Chen
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hongshuang Lu
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jianping Xiong
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Daniel S. W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Kengo Uramoto
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koju Kamoi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
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8
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He HL, Liu YX, Chen XY, Ling SG, Qi Y, Xiong Y, Jin ZB. Fundus Tessellated Density of Pathologic Myopia. Asia Pac J Ophthalmol (Phila) 2023; 12:604-613. [PMID: 38079255 DOI: 10.1097/apo.0000000000000642] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/04/2023] [Indexed: 12/21/2023] Open
Abstract
PURPOSE The study aimed to quantitatively evaluate the fundus tessellated density (FTD) in different categories of pathologic myopia (PM) using fundus photographs with the application of artificial intelligence. METHODS A retrospective review of 407 PM (META-PM, Category 2-Category 4) eyes was conducted, employing a biomimetic mechanism of human vision and integrated image processing technologies for FTD extraction and calculation. Different regions of interest were analyzed, including circle O4.5 (optic disc centered, diameter of 4.5 mm) and circle M1.0, M3.0, M6.0 (macular centered, diameter of 1.0, 3.0, and 6.0 mm), using 2 partitioning methods ("X" and "+"). The density of patchy (Category 3) or macular atrophy (Category 4) areas was quantified. Univariate and multivariate linear regression analyses were performed to assess the association with FTD. RESULTS The mean FTD of total PM eyes was 0.283, ranging from 0.002 to 0.500, and demonstrating a negative correlation with the PM category. In multivariate analysis, age was found to be significantly associated with FTD ( P <0.05), while axial length did not show a significant association. Fundus tessellation of circle O4.5 and circle M6.0 displayed associations with the FTD across different PM categories. The "X" partitioning method better fit the circle M6.0 region, while both methods were suitable for the circle O4.5 region. After excluding the patchy and macular atrophic areas, the mean FTD values were 0.346 in Category 2, 0.261 in Category 3, and 0.186 in Category 4. CONCLUSIONS The study revealed a decreasing trend in FTD values across different categories of PM, regardless of the presence or absence of patchy or macular atrophic areas. Quantifying FTD in PM could be a valuable tool for improving the existing PM classification system and gaining insights into the origin of posterior staphyloma and visual field defects in high myopia.
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Affiliation(s)
- Hai-Long He
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yi-Xin Liu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | | | | | - Yue Qi
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ying Xiong
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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9
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Yang M, Han J, Park JI, Hwang JS, Han JM, Yoon J, Choi S, Hwang G, Hwang DDJ. Prediction of Visual Acuity in Pathologic Myopia with Myopic Choroidal Neovascularization Treated with Anti-Vascular Endothelial Growth Factor Using a Deep Neural Network Based on Optical Coherence Tomography Images. Biomedicines 2023; 11:2238. [PMID: 37626734 PMCID: PMC10452208 DOI: 10.3390/biomedicines11082238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy.
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Affiliation(s)
- Migyeong Yang
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
| | - Jinyoung Han
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
- Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul 03603, Republic of Korea
| | - Ji In Park
- Department of Medicine, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon 24341, Gangwon-do, Republic of Korea;
| | | | - Jeong Mo Han
- Seoul Bombit Eye Clinic, Sejong 30127, Republic of Korea;
| | - Jeewoo Yoon
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
- RAONDATA, Seoul 04615, Republic of Korea
| | - Seong Choi
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
- RAONDATA, Seoul 04615, Republic of Korea
| | - Gyudeok Hwang
- Department of Ophthalmology, Hangil Eye Hospital, Incheon 21388, Republic of Korea;
| | - Daniel Duck-Jin Hwang
- Department of Ophthalmology, Hangil Eye Hospital, Incheon 21388, Republic of Korea;
- Department of Ophthalmology, Catholic Kwandong University College of Medicine, Incheon 22711, Republic of Korea
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10
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Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
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11
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Wawer Matos PA, Reimer RP, Rokohl AC, Caldeira L, Heindl LM, Große Hokamp N. Artificial Intelligence in Ophthalmology - Status Quo and Future Perspectives. Semin Ophthalmol 2023; 38:226-237. [PMID: 36356300 DOI: 10.1080/08820538.2022.2139625] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
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Affiliation(s)
| | - Robert P Reimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Alexander C Rokohl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Ludwig M Heindl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
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12
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Sun MT, Tran M, Singh K, Chang R, Wang H, Sun Y. Glaucoma and Myopia: Diagnostic Challenges. Biomolecules 2023; 13:biom13030562. [PMID: 36979497 PMCID: PMC10046607 DOI: 10.3390/biom13030562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/06/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
The rising global prevalence of myopia is a growing concern for clinicians, as it predisposes patients to severe ocular pathologies including glaucoma. High myopia can be associated with clinical features that resemble glaucomatous damage, which make an accurate glaucoma diagnosis challenging, particularly among patients with normal intraocular pressures. These patients may also present with established visual field defects which can mimic glaucoma, and standard imaging technology is less useful in disease detection and monitoring due to the lack of normative data for these anatomically unique eyes. Progression over time remains the most critical factor in facilitating the detection of early glaucomatous changes, and thus careful longitudinal follow-up of high-risk myopic patients is the most important aspect of management. Here, we review our current understanding of the complex relationship between myopia and glaucoma, and the diagnostic challenges and limitations of current testing protocols including visual field, intraocular pressure, and imaging. Furthermore, we discuss the clinical findings of two highly myopic patients with suspected glaucoma.
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Affiliation(s)
- Michelle T Sun
- Department of Ophthalmology, School of Medicine, Stanford University, Palo Alto, CA 94305, USA
| | - Matthew Tran
- Department of Ophthalmology, School of Medicine, Stanford University, Palo Alto, CA 94305, USA
- School of Medicine, University of Nevada, Reno, NV 89557, USA
| | - Kuldev Singh
- Department of Ophthalmology, School of Medicine, Stanford University, Palo Alto, CA 94305, USA
| | - Robert Chang
- Department of Ophthalmology, School of Medicine, Stanford University, Palo Alto, CA 94305, USA
| | - Huaizhou Wang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Yang Sun
- Department of Ophthalmology, School of Medicine, Stanford University, Palo Alto, CA 94305, USA
- Palo Alto Veterans Administration, Palo Alto, CA 94304, USA
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13
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Li Y, Zheng F, Foo LL, Wong QY, Ting D, Hoang QV, Chong R, Ang M, Wong CW. Advances in OCT Imaging in Myopia and Pathologic Myopia. Diagnostics (Basel) 2022; 12:diagnostics12061418. [PMID: 35741230 PMCID: PMC9221645 DOI: 10.3390/diagnostics12061418] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Advances in imaging with optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) technology, including the development of swept source OCT/OCTA, widefield or ultra-widefield systems, have greatly improved the understanding, diagnosis, and treatment of myopia and myopia-related complications. Anterior segment OCT is useful for imaging the anterior segment of myopes, providing the basis for implantable collamer lens optimization, or detecting intraocular lens decentration in high myopic patients. OCT has enhanced imaging of vitreous properties, and measurement of choroidal thickness in myopic eyes. Widefield OCT systems have greatly improved the visualization of peripheral retinal lesions and have enabled the evaluation of wide staphyloma and ocular curvature. Based on OCT imaging, a new classification system and guidelines for the management of myopic traction maculopathy have been proposed; different dome-shaped macula morphologies have been described; and myopia-related abnormalities in the optic nerve and peripapillary region have been demonstrated. OCTA can quantitatively evaluate the retinal microvasculature and choriocapillaris, which is useful for the early detection of myopic choroidal neovascularization and the evaluation of anti-vascular endothelial growth factor therapy in these patients. In addition, the application of artificial intelligence in OCT/OCTA imaging in myopia has achieved promising results.
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Affiliation(s)
- Yong Li
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Feihui Zheng
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Qiu Ying Wong
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
| | - Daniel Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Quan V. Hoang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
- Department of Ophthalmology, Columbia University, New York, NY 10027, USA
| | - Rachel Chong
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Chee Wai Wong
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore 169856, Singapore; (Y.L.); (F.Z.); (L.L.F.); (Q.Y.W.); (D.T.); (Q.V.H.); (R.C.); (M.A.)
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
- Correspondence:
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