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AlShawabkeh M, AlRyalat SA, Al Bdour M, Alni’mat A, Al-Akhras M. The utilization of artificial intelligence in glaucoma: diagnosis versus screening. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1368081. [PMID: 38984126 PMCID: PMC11182276 DOI: 10.3389/fopht.2024.1368081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 02/20/2024] [Indexed: 07/11/2024]
Abstract
With advancements in the implementation of artificial intelligence (AI) in different ophthalmology disciplines, it continues to have a significant impact on glaucoma diagnosis and screening. This article explores the distinct roles of AI in specialized ophthalmology clinics and general practice, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models. Screening models prioritize sensitivity to detect potential glaucoma cases efficiently, while diagnostic models emphasize specificity to confirm disease with high accuracy. AI applications, primarily using machine learning (ML) and deep learning (DL), have been successful in detecting glaucomatous optic neuropathy from colored fundus photographs and other retinal imaging modalities. Diagnostic models integrate data extracted from various forms of modalities (including tests that assess structural optic nerve damage as well as those evaluating functional damage) to provide a more nuanced, accurate and thorough approach to diagnosing glaucoma. As AI continues to evolve, the collaboration between technology and clinical expertise should focus more on improving specificity of glaucoma diagnostic models to assess ophthalmologists to revolutionize glaucoma diagnosis and improve patients care.
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Affiliation(s)
| | - Saif Aldeen AlRyalat
- Department of Ophthalmology, The University of Jordan, Amman, Jordan
- Department of Ophthalmology, Houston Methodist Hospital, Houston, TX, United States
| | - Muawyah Al Bdour
- Department of Ophthalmology, The University of Jordan, Amman, Jordan
| | - Ayat Alni’mat
- Department of Ophthalmology, Al Taif Eye Center, Amman, Jordan
| | - Mousa Al-Akhras
- Department of Computer Information Systems, School of Information Technology and Systems, The University of Jordan, Amman, Jordan
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Choi JY, Kim H, Kim JK, Lee IS, Ryu IH, Kim JS, Yoo TK. Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era. Med Biol Eng Comput 2024; 62:449-463. [PMID: 37889431 DOI: 10.1007/s11517-023-02952-6] [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: 04/21/2023] [Accepted: 10/14/2023] [Indexed: 10/28/2023]
Abstract
Recently, fundus photography (FP) is being increasingly used. Corneal curvature is an essential factor in refractive errors and is associated with several pathological corneal conditions. As FP-based examination systems have already been widely distributed, it would be helpful for telemedicine to extract information such as corneal curvature using FP. This study aims to develop a deep learning model based on FP for corneal curvature prediction by categorizing corneas into steep, regular, and flat groups. The EfficientNetB0 architecture with transfer learning was used to learn FP patterns to predict flat, regular, and steep corneas. In validation, the model achieved a multiclass accuracy of 0.727, a Matthews correlation coefficient of 0.519, and an unweighted Cohen's κ of 0.590. The areas under the receiver operating characteristic curves for binary prediction of flat and steep corneas were 0.863 and 0.848, respectively. The optic nerve and its peripheral areas were the main focus of the model. The developed algorithm shows that FP can potentially be used as an imaging modality to estimate corneal curvature in the post-COVID-19 era, whereby patients may benefit from the detection of abnormal corneal curvatures using FP in the telemedicine setting.
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Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | | | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Jung Soo Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Tae Keun Yoo
- Department of Refractive Surgery, B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.
- Research and Development Department, VISUWORKS, Seoul, South Korea.
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Soh ZD, Tan M, Nongpiur ME, Xu BY, Friedman D, Zhang X, Leung C, Liu Y, Koh V, Aung T, Cheng CY. Assessment of angle closure disease in the age of artificial intelligence: A review. Prog Retin Eye Res 2024; 98:101227. [PMID: 37926242 DOI: 10.1016/j.preteyeres.2023.101227] [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: 05/31/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 11/07/2023]
Abstract
Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.
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Affiliation(s)
- Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore.
| | - Mingrui Tan
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*Star), 1 Fusionopolis Way, 138632, Singapore.
| | - Monisha Esther Nongpiur
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
| | - Benjamin Yixing Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, 1450 San Pablo St #4400, Los Angeles, CA, 90033, USA.
| | - David Friedman
- Department of Ophthalmology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Massachusetts Eye and Ear, Mass General Brigham, 243 Charles Street, Boston, MA, 02114, USA.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat Sen University, No. 54 Xianlie South Road, Yuexiu District, Guangzhou, China.
| | - Christopher Leung
- Department of Ophthalmology, School of Clinical Medicine, The University of Hong Kong, Cyberport 4, 100 Cyberport Road, Hong Kong; Department of Ophthalmology, Queen Mary Hospital, 102 Pok Fu Lam Road, Hong Kong.
| | - Yong Liu
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*Star), 1 Fusionopolis Way, 138632, Singapore.
| | - Victor Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 7, 119228, Singapore.
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 7, 119228, Singapore.
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Kurysheva NI, Rodionova OY, Pomerantsev AL, Sharova GA. [Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis]. Vestn Oftalmol 2024; 140:82-87. [PMID: 38962983 DOI: 10.17116/oftalma202414003182] [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] [Indexed: 07/05/2024]
Abstract
This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach. The results found in the reviewed literature are contradictory, indicating that improvement of the AI models requires further research and a standardized approach. The use of neural networks for timely detection of glaucoma based on multimodal imaging will reduce the risk of blindness associated with glaucoma.
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Affiliation(s)
- N I Kurysheva
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- Ophthalmological Center of the Federal Medical-Biological Agency at the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
| | - O Ye Rodionova
- N.N. Semenov Federal Research Center for Chemical Physics, Moscow, Russia
| | - A L Pomerantsev
- N.N. Semenov Federal Research Center for Chemical Physics, Moscow, Russia
| | - G A Sharova
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- OOO Glaznaya Klinika Doktora Belikovoy, Moscow, Russia
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Zhang L, Tang L, Xia M, Cao G. The application of artificial intelligence in glaucoma diagnosis and prediction. Front Cell Dev Biol 2023; 11:1173094. [PMID: 37215077 PMCID: PMC10192631 DOI: 10.3389/fcell.2023.1173094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.
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Affiliation(s)
- Linyu Zhang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Li Tang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Min Xia
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Guofan Cao
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
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Kim T, Kim SJ, Lee BY, Cho HJ, Sa BG, Ryu IH, Kim JK, Lee IS, Han E, Kim H, Yoo TK. Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review. BMC Ophthalmol 2023; 23:59. [PMID: 36765328 PMCID: PMC9921691 DOI: 10.1186/s12886-023-02814-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/09/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND Optimal sizing for phakic intraocular lens (EVO-ICL with KS-AquaPort) implantation plays an important role in preventing postoperative complications. We aimed to formulate optimal lens sizing using ocular biometric parameters measured with a Heidelberg anterior segment optical coherence tomography (AS-OCT) device. METHODS We retrospectively analyzed 892 eyes of 471 healthy subjects treated with an intraocular collamer lens (ICL) and assigned them to either the development (80%) or validation (20%) set. We built vault prediction models using the development set via classic linear regression methods as well as partial least squares and least absolute shrinkage and selection operator (LASSO) regression techniques. We evaluated prediction abilities based on the Bayesian information criterion (BIC) to select the best prediction model. The performance was measured using Pearson's correlation coefficient and the mean squared error (MAE) between the achieved and predicted results. RESULTS Measurements of aqueous depth (AQD), anterior chamber volume, anterior chamber angle (ACA) distance, spur-to-spur distance, crystalline lens thickness (LT), and white-to-white distance from ANTERION were highly associated with the ICL vault. The LASSO model using the AQD, ACA distance, and LT showed the best BIC results for postoperative ICL vault prediction. In the validation dataset, the LASSO model showed the strongest correlation (r = 0.582, P < 0.001) and the lowest MAE (104.7 μm). CONCLUSION This is the first study to develop a postoperative ICL vault prediction and lens-sizing model based on the ANTERION. As the measurements from ANTERION and other AS-OCT devices are not interchangeable, ANTERION may be used for optimal ICL sizing using our formula. Because our model was developed based on the East Asian population, further studies are needed to explore the role of this prediction model in different populations.
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Affiliation(s)
| | | | | | | | | | - Ik Hee Ryu
- VISUWORKS, Seoul, South Korea ,Department of Refractive Surgery, B&VIIT Eye Center, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Jin Kuk Kim
- VISUWORKS, Seoul, South Korea ,Department of Refractive Surgery, B&VIIT Eye Center, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Eoksoo Han
- grid.36303.350000 0000 9148 4899Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea
| | | | - Tae Keun Yoo
- VISUWORKS, Seoul, South Korea. .,Department of Refractive Surgery, B&VIIT Eye Center, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.
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Beeche C, Gezer NS, Iyer K, Almetwali O, Yu J, Zhang Y, Dhupar R, Leader JK, Pu J. Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN). Med Phys 2023; 50:449-464. [PMID: 36184848 PMCID: PMC9868057 DOI: 10.1002/mp.16012] [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: 04/29/2021] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVE To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification. METHODS The neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories: (1) no vein occlusion ("normal"), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models. RESULTS The AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models. CONCLUSION The NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs.
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Affiliation(s)
- Cameron Beeche
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Naciye S Gezer
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kartik Iyer
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Omar Almetwali
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Juezhao Yu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Yanchun Zhang
- Shaan’xi Eye Hospital, Xi’an, Shaanxi, 710004, China
| | - Rajeev Dhupar
- Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Surgical Services Division, VA Pittsburgh Healthcare System, Pittsburgh, PA 15240
| | - Joseph K. Leader
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Sheng B, Chen X, Li T, Ma T, Yang Y, Bi L, Zhang X. An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front Public Health 2022; 10:971943. [PMID: 36388304 PMCID: PMC9650481 DOI: 10.3389/fpubh.2022.971943] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/04/2022] [Indexed: 01/25/2023] Open
Abstract
Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.
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Affiliation(s)
- Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
| | - Xiaosi Chen
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tingyao Li
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
| | - Tianxing Ma
- Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing, China
| | - Yang Yang
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Lei Bi
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Xinyuan Zhang
- Beijing Retinal and Choroidal Vascular Diseases Study Group, Beijing Tongren Hospital, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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