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Haddad C, Baleine M, Motulsky E. An OCT-A Analysis of the Importance of Intermediate Capillary Plexus in Diabetic Retinopathy: A Brief Review. J Clin Med 2024; 13:2516. [PMID: 38731048 PMCID: PMC11084716 DOI: 10.3390/jcm13092516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/22/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Optical coherence tomography-angiography is a technique that allows us to non-invasively study in vivo the different retinal vascular networks. This allows a deeper understanding of retinal capillary anatomy and function, in addition to the pathophysiologic changes encountered in diverse diseases. The four retinal capillary layers have different anatomies and functions, implying distinct adaptation and roles in the course of the diseases. Diabetic retinopathy is the leading cause of blindness in working-age adults. Several studies have evaluated how each retinal capillary layer is specifically affected according to the stage of the disease. Unfortunately, too few studies have considered the intermediate capillary plexus as a separate layer, as it has often been incorporated in another layer. In this review, we shed light on the potential role the intermediate capillary plexus plays in the physiopathology of diabetic retinal disease as well as its potential use in grading diabetic retinopathy and its clinical added value in estimating the disease prognosis.
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Affiliation(s)
| | | | - Elie Motulsky
- Department of Ophthalmology, Hôpital Universitaire de Bruxelles, Erasme Hospital, 1070 Brussels, Belgium; (C.H.); (M.B.)
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2
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Das V, Zhang F, Bower AJ, Li J, Liu T, Aguilera N, Alvisio B, Liu Z, Hammer DX, Tam J. Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography. COMMUNICATIONS MEDICINE 2024; 4:68. [PMID: 38600290 PMCID: PMC11006674 DOI: 10.1038/s43856-024-00483-1] [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: 04/18/2023] [Accepted: 03/13/2024] [Indexed: 04/12/2024] Open
Abstract
BACKGROUND In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput. METHODS Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study. RESULTS We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges. CONCLUSIONS The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.
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Affiliation(s)
- Vineeta Das
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Furu Zhang
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Andrew J Bower
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Joanne Li
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tao Liu
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nancy Aguilera
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Bruno Alvisio
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Zhuolin Liu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Daniel X Hammer
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Johnny Tam
- National Eye Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
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Li M, Huang K, Xu Q, Yang J, Zhang Y, Ji Z, Xie K, Yuan S, Liu Q, Chen Q. OCTA-500: A retinal dataset for optical coherence tomography angiography study. Med Image Anal 2024; 93:103092. [PMID: 38325155 DOI: 10.1016/j.media.2024.103092] [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: 12/23/2022] [Revised: 11/10/2023] [Accepted: 01/22/2024] [Indexed: 02/09/2024]
Abstract
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.
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Affiliation(s)
- Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Qiuzhuo Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Jiadong Yang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Yuhan Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Zexuan Ji
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Keren Xie
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Qinghuai Liu
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
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4
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Hormel TT, Liang GB, Wei X, Guo Y, Gao M, Wang J, Huang D, Bailey ST, Hwang TS, Jia Y. Visualizing features with wide-field volumetric OCT angiography. OPTICS EXPRESS 2024; 32:10329-10347. [PMID: 38571248 PMCID: PMC11018334 DOI: 10.1364/oe.510640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/16/2024] [Accepted: 02/14/2024] [Indexed: 04/05/2024]
Abstract
Optical coherence tomography (OCT) and its extension OCT angiography (OCTA) have become essential clinical imaging modalities due to their ability to provide depth-resolved angiographic and tissue structural information non-invasively and at high resolution. Within a field of view, the anatomic detail available is sufficient to identify several structural and vascular pathologies that are clinically relevant for multiple prevalent blinding diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and vein occlusions. The main limitation in contemporary OCT devices is that this field of view is limited due to a fundamental trade-off between system resolution/sensitivity, sampling density, and imaging window dimensions. Here, we describe a swept-source OCT device that can capture up to a 12 × 23-mm field of view in a single shot and show that it can identify conventional pathologic features such as non-perfusion areas outside of conventional fields of view. We also show that our approach maintains sensitivity sufficient to visualize novel features, including choriocapillaris morphology beneath the macula and macrophage-like cells at the inner limiting membrane, both of which may have implications for disease.
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Affiliation(s)
- Tristan T. Hormel
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
| | - Guangru B. Liang
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
| | - Xiang Wei
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Yukun Guo
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Min Gao
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
| | - Jie Wang
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
| | - David Huang
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
| | - Steven T. Bailey
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
| | - Thomas S. Hwang
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
| | - Yali Jia
- Casey Eye Institutue, Oregon Health and Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA
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5
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Waheed NK, Rosen RB, Jia Y, Munk MR, Huang D, Fawzi A, Chong V, Nguyen QD, Sepah Y, Pearce E. Optical coherence tomography angiography in diabetic retinopathy. Prog Retin Eye Res 2023; 97:101206. [PMID: 37499857 DOI: 10.1016/j.preteyeres.2023.101206] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
There remain many unanswered questions on how to assess and treat the pathology and complications that arise from diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) is a novel and non-invasive three-dimensional imaging method that can visualize capillaries in all retinal layers. Numerous studies have confirmed that OCTA can identify early evidence of microvascular changes and provide quantitative assessment of the extent of diseases such as DR and its complications. A number of informative OCTA metrics could be used to assess DR in clinical trials, including measurements of the foveal avascular zone (FAZ; area, acircularity, 3D para-FAZ vessel density), vessel density, extrafoveal avascular zones, and neovascularization. Assessing patients with DR using a full-retinal slab OCTA image can limit segmentation errors and confounding factors such as those related to center-involved diabetic macular edema. Given emerging data suggesting the importance of the peripheral retinal vasculature in assessing and predicting DR progression, wide-field OCTA imaging should also be used. Finally, the use of automated methods and algorithms for OCTA image analysis, such as those that can distinguish between areas of true and false signals, reconstruct images, and produce quantitative metrics, such as FAZ area, will greatly improve the efficiency and standardization of results between studies. Most importantly, clinical trial protocols should account for the relatively high frequency of poor-quality data related to sub-optimal imaging conditions in DR and should incorporate time for assessing OCTA image quality and re-imaging patients where necessary.
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Affiliation(s)
- Nadia K Waheed
- New England Eye Center, Tufts University School of Medicine, Boston, MA, USA.
| | - Richard B Rosen
- New York Eye and Ear Infirmary of Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yali Jia
- School of Medicine, Casey Eye Institute, Oregon Health and Science University, Portland, OR, USA
| | - Marion R Munk
- Augenarzt-Praxisgemeinschaft Gutblick AG, Pfäffikon, Switzerland
| | - David Huang
- School of Medicine, Casey Eye Institute, Oregon Health and Science University, Portland, OR, USA
| | - Amani Fawzi
- Department of Ophthalmology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Victor Chong
- Institute of Ophthalmology, University College London, London, UK
| | - Quan Dong Nguyen
- Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Yasir Sepah
- Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA
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Hormel TT, Jia Y. OCT angiography and its retinal biomarkers [Invited]. BIOMEDICAL OPTICS EXPRESS 2023; 14:4542-4566. [PMID: 37791289 PMCID: PMC10545210 DOI: 10.1364/boe.495627] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 10/05/2023]
Abstract
Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.
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Affiliation(s)
- Tristan T. Hormel
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA
| | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
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Luo M, Xu Z, Ye Z, Liang Z, Xiao H, Li Y, Li Z, Zhu Y, He Y, Zhuo Y. Deep learning for anterior segment OCT angiography automated denoising and vascular quantitative measurement. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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8
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Optical Coherence Tomography Angiography of the Intestine: How to Prevent Motion Artifacts in Open and Laparoscopic Surgery? Life (Basel) 2023; 13:life13030705. [PMID: 36983861 PMCID: PMC10055682 DOI: 10.3390/life13030705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/25/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
(1) Introduction. The problem that limits the intraoperative use of OCTA for the intestinal circulation diagnostics is the low informative value of OCTA images containing too many motion artifacts. The aim of this study is to evaluate the efficiency and safety of the developed unit for the prevention of the appearance of motion artifacts in the OCTA images of the intestine in both open and laparoscopic surgery in the experiment; (2) Methods. A high-speed spectral-domain multimodal optical coherence tomograph (IAP RAS, Russia) operating at a wavelength of 1310 nm with a spectral width of 100 μm and a power of 2 mW was used. The developed unit was tested in two groups of experimental animals—on minipigs (group I, n = 10, open abdomen) and on rabbits (group II, n = 10, laparoscopy). Acute mesenteric ischemia was modeled and then 1 h later the small intestine underwent OCTA evaluation. A total of 400 OCTA images of the intact and ischemic small intestine were obtained and analyzed. The quality of the obtained OCTA images was evaluated based on the score proposed in 2020 by the group of Magnin M. (3) Results. Without stabilization, OCTA images of the intestine tissues were informative only in 32–44% of cases in open surgery and in 14–22% of cases in laparoscopic surgery. A vacuum bowel stabilizer with a pressure deficit of 22–25 mm Hg significantly reduced the number of motion artifacts. As a result, the proportion of informative OCTA images in open surgery increased up to 86.5% (Χ2 = 200.2, p = 0.001), and in laparoscopy up to 60% (Χ2 = 148.3, p = 0.001). (4) Conclusions. The used vacuum tissue stabilizer enabled a significant increase in the proportion of informative OCTA images by significantly reducing the motion artifacts.
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Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13020326. [PMID: 36673135 PMCID: PMC9857993 DOI: 10.3390/diagnostics13020326] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the "proof-of-concept" stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.
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Ma D, Pasquale LR, Girard MJA, Leung CKS, Jia Y, Sarunic MV, Sappington RM, Chan KC. Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications. FRONTIERS IN OPHTHALMOLOGY 2023; 2:1057896. [PMID: 36866233 PMCID: PMC9976697 DOI: 10.3389/fopht.2022.1057896] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/05/2022] [Indexed: 04/16/2023]
Abstract
Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.
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Affiliation(s)
- Da Ma
- School of Medicine, Wake Forest University, Winston-Salem, NC, United States
- Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Michaël J. A. Girard
- Ophthalmic Engineering & Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Institute for Molecular and Clinical Ophthalmology, Basel, Switzerland
| | | | - Yali Jia
- Casey Eye Institute, Oregon Health & Science University, Portland, OR, United States
| | - Marinko V. Sarunic
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Rebecca M. Sappington
- School of Medicine, Wake Forest University, Winston-Salem, NC, United States
- Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Kevin C. Chan
- Departments of Ophthalmology and Radiology, Neuroscience Institute, NYU Grossman School of Medicine, NYU Langone Health, New York University, New York, NY, United States
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY, United States
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