Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical coherence tomography
Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that...
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| Published in: | Frontiers in ophthalmology Vol. 4; p. 1497848 |
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| Abstract | Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause.
The bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study.
Incorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION.
This study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model's ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model's diagnostic capabilities. |
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| AbstractList | IntroductionGlaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause.MethodsThe bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study.ResultsIncorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION.ConclusionThis study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model’s ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model’s diagnostic capabilities. Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause. The bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study. Incorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION. This study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model's ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model's diagnostic capabilities. Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause.IntroductionGlaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We propose a booster Variational Autoencoder (bVAE) to capture spatial variations in RGC loss and generate latent space (LS) montage maps that visualize different degrees and spatial patterns of optic nerve bundle injury. Furthermore, the bVAE model is capable of tracking the spatial pattern of RGC thinning over time and classifying the underlying cause.The bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study.MethodsThe bVAE model consists of an encoder, a display decoder, and a booster decoder. The encoder decomposes input ganglion cell layer (GCL) thickness maps into two display latent variables (dLVs) and eight booster latent variables (bLVs). The dLVs capture primary spatial patterns of RGC thinning, while the display decoder reconstructs the GCL map and creates the LS montage map. The bLVs add finer spatial details, improving reconstruction accuracy. XGBoost was used to analyze the dLVs and bLVs, estimating normal/abnormal GCL thinning and classifying diseases (glaucoma, ON, and NAION). A total of 10,701 OCT macular scans from 822 subjects were included in this study.Incorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION.ResultsIncorporating bLVs improved reconstruction accuracy, with the image-based root-mean-square error (RMSE) between input and reconstructed GCL thickness maps decreasing from 5.55 ± 2.29 µm (two dLVs only) to 4.02 ± 1.61 µm (two dLVs and eight bLVs). However, the image-based structural similarity index (SSIM) remained similar (0.91 ± 0.04), indicating that just two dLVs effectively capture the main GCL spatial patterns. For classification, the XGBoost model achieved an AUC of 0.98 for identifying abnormal spatial patterns of GCL thinning over time using the dLVs. Disease classification yielded AUCs of 0.95 for glaucoma, 0.84 for ON, and 0.93 for NAION, with bLVs further increasing the AUCs to 0.96 for glaucoma, 0.93 for ON, and 0.99 for NAION.This study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model's ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model's diagnostic capabilities.ConclusionThis study presents a novel approach to visualizing and quantifying GCL thinning patterns in optic neuropathies using the bVAE model. The combination of dLVs and bLVs enhances the model's ability to capture key spatial features and predict disease progression. Future work will focus on integrating additional image modalities to further refine the model's diagnostic capabilities. |
| Author | Woods, Brian Linton, Edward F. Szanto, David Wall, Michael Wang, Jui-Kai Johnson, Brett A. Chen, Zhi Pouw, Andrew Zhang, Honghai Kwon, Young H. Kardon, Randy H. Kupersmith, Mark J. Garvin, Mona K. |
| AuthorAffiliation | 5 Department of Neurology, Icahn School of Medicine at Mount Sinai , New York, NY , United States 3 Department of Electrical and Computer Engineering, University of Iowa , Iowa City, IA , United States 9 Department of Neurosurgery, Icahn School of Medicine at Mount Sinai , New York, NY , United States 4 Iowa Institute for Biomedical Imaging, University of Iowa , Iowa City, IA , United States 1 Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System , Iowa City, IA , United States 6 Department of Ophthalmology, University Hospital Galway , Galway , Ireland 7 Department of Physics, School of Natural Sciences, University of Galway , Galway , Ireland 2 Department of Ophthalmology and Visual Sciences, University of Iowa , Iowa City, IA , United States 8 Department of Ophthalmology, Icahn School of Medicine at Mount Sinai , New York, NY , United States |
| AuthorAffiliation_xml | – name: 6 Department of Ophthalmology, University Hospital Galway , Galway , Ireland – name: 8 Department of Ophthalmology, Icahn School of Medicine at Mount Sinai , New York, NY , United States – name: 4 Iowa Institute for Biomedical Imaging, University of Iowa , Iowa City, IA , United States – name: 2 Department of Ophthalmology and Visual Sciences, University of Iowa , Iowa City, IA , United States – name: 3 Department of Electrical and Computer Engineering, University of Iowa , Iowa City, IA , United States – name: 7 Department of Physics, School of Natural Sciences, University of Galway , Galway , Ireland – name: 1 Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System , Iowa City, IA , United States – name: 5 Department of Neurology, Icahn School of Medicine at Mount Sinai , New York, NY , United States – name: 9 Department of Neurosurgery, Icahn School of Medicine at Mount Sinai , New York, NY , United States |
| Author_xml | – sequence: 1 givenname: Jui-Kai surname: Wang fullname: Wang, Jui-Kai – sequence: 2 givenname: Brett A. surname: Johnson fullname: Johnson, Brett A. – sequence: 3 givenname: Zhi surname: Chen fullname: Chen, Zhi – sequence: 4 givenname: Honghai surname: Zhang fullname: Zhang, Honghai – sequence: 5 givenname: David surname: Szanto fullname: Szanto, David – sequence: 6 givenname: Brian surname: Woods fullname: Woods, Brian – sequence: 7 givenname: Michael surname: Wall fullname: Wall, Michael – sequence: 8 givenname: Young H. surname: Kwon fullname: Kwon, Young H. – sequence: 9 givenname: Edward F. surname: Linton fullname: Linton, Edward F. – sequence: 10 givenname: Andrew surname: Pouw fullname: Pouw, Andrew – sequence: 11 givenname: Mark J. surname: Kupersmith fullname: Kupersmith, Mark J. – sequence: 12 givenname: Mona K. surname: Garvin fullname: Garvin, Mona K. – sequence: 13 givenname: Randy H. surname: Kardon fullname: Kardon, Randy H. |
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| Keywords | optic neuritis (ON) retinal ganglion cell (RGC) loss non-arteritic anterior ischemic optic neuropathy (NAION) variational autoencoder (VAE) glaucoma optical coherence tomography (OCT) |
| Language | English |
| License | Copyright © 2025 Wang, Johnson, Chen, Zhang, Szanto, Woods, Wall, Kwon, Linton, Pouw, Kupersmith, Garvin and Kardon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Yukihiro Shiga, University of Montreal Hospital Research Centre (CRCHUM), Canada These authors have contributed equally to this work and share senior/last authorship Fabio Lavinsky, University of the Rio dos Sinos Valley, Brazil Reviewed by: Takashi Nishida, University of California, San Diego, United States |
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| References | Wang (B36) 2015 Daniel (B35) 2021 Hood (B2) 2013; 32 Burgess (B16) 2018 Wang (B37) 2017; 58 Mandal (B27) 2021; 1 Shon (B21) 2022; 11 Wy (B7) 2024; 33 Wang (B34) 2009; 26 Wang (B33) 2004; 13 Hallett (B20) 2020 Mishra (B11) 2020; 10 Asaoka (B26) 2020; 3 Hou (B17) 2016 Berchuck (B23) 2019 Wang (B6) 2021 B32 Yadav (B12) 2022; 8 Chen (B5) 2018; 3 Bouchacourt (B18) 2018 Chen (B10) 2024; 15 Wang (B29) 2024; 13 Kingma (B15) 2019; 12 Kardon (B1) 2011; 31 Dotan (B9) 2013; 7 He (B13) 2023; 14 Schorr (B8) 2022; 30 Mwanza (B14) 2011; 52 Huang (B19) 2018 Agharezaei (B22) 2023; 13 Zheng (B25) 2019 Kupersmith (B31) 2024; 131 Chen (B30) 2016 Kupersmith (B3) 2016; 57 Mohammadzadeh (B28) 2024; 4 Odaibo (B24) 2019; 1907 Kupersmith (B4) 2016; 22 |
| References_xml | – volume: 10 start-page: 9541 year: 2020 ident: B11 article-title: Automated retinal layer segmentation using graph-based algorithm incorporating deep-learning-derived information publication-title: Sci Rep doi: 10.1038/s41598-020-66355-5 – volume: 13 year: 2004 ident: B33 article-title: Image quality assessment: From error visibility to structural similarity publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2003.819861 – volume: 32 start-page: 1 year: 2013 ident: B2 article-title: Glaucomatous damage of the macula publication-title: Prog Retin Eye Res doi: 10.1016/j.preteyeres.2012.08.003 – volume: 14 year: 2023 ident: B13 article-title: Longitudinal deep network for consistent OCT layer segmentation publication-title: BioMed Opt Express doi: 10.1364/BOE.487518 – start-page: 1 year: 2019 ident: B23 article-title: Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder publication-title: Sci Rep doi: 10.1038/s41598-019-54653-6 – volume: 11 year: 2022 ident: B21 article-title: Development of cumulative order-preserving image transformation based variational autoencoder for anterior segment optical coherence tomography images publication-title: Trans Vision Sci Technol doi: 10.1167/tvst.11.8.30 – start-page: 1 year: 2018 ident: B19 article-title: Introvae: Introspective variational autoencoders for photographic image synthesis publication-title: Neural Inf Process Syst (NeurIPS) – volume: 26 start-page: 98 year: 2009 ident: B34 article-title: Mean squared error: Love it or leave it? A new look at signal fidelity measures publication-title: IEEE Signal Process Magazine doi: 10.1109/MSP.2008.930649 – volume: 33 year: 2024 ident: B7 article-title: Comparison of patterns of structural progression in primary open angle glaucoma and pseudoexfoliation glaucoma publication-title: J Glaucoma doi: 10.1097/IJG.0000000000002348 – volume: 1 year: 2021 ident: B27 article-title: Assessing glaucoma in retinal fundus photographs using deep feature consistent variational autoencoders publication-title: ArXiv – volume: 57 year: 2016 ident: B3 article-title: Retinal ganglion cell layer thinning within one month of presentation for non-arteritic anterior ischemic optic neuropathy publication-title: Invest Ophthalmol Visual Sci doi: 10.1167/iovs.15-18736 – year: 2015 ident: B36 article-title: Semi-automated 2D bruch's membrane shape analysis in papilledema using spectral-domain optical coherence tomography publication-title: SPIE Med Imaging – volume: 3 start-page: 35 year: 2018 ident: B5 article-title: The role of optical coherence tomography in neuro-ophthalmology publication-title: Ann Eye Sci doi: 10.21037/aes.2018.05.08 – volume: 52 year: 2011 ident: B14 article-title: Macular ganglion cell–inner plexiform layer: Automated detection and thickness reproducibility with spectral domain–optical coherence tomography in glaucoma publication-title: Invest Ophthalmol Visual Sci doi: 10.1167/iovs.11-7962 – volume-title: Proceedings of the AAAI conference on artificial intelligence year: 2018 ident: B18 article-title: Multi-level variational autoencoder: Learning disentangled representations from grouped observations doi: 10.1609/aaai.v32i1.11867 – volume: 22 year: 2016 ident: B4 article-title: Retinal ganglion cell layer thinning within one month of presentation for optic neuritis publication-title: Mult Scler doi: 10.1177/1352458515598020 – volume: 13 start-page: 1 year: 2024 ident: B29 article-title: Visualization of optic nerve structural patterns in papilledema using deep learning variational autoencoders publication-title: Trans Vision Sci Technol doi: 10.1167/tvst.13.1.13 – volume: 31 year: 2011 ident: B1 article-title: Role of the macular optical coherence tomography scan in neuro-ophthalmology publication-title: J Neuro-Ophthalmology doi: 10.1097/WNO.0b013e318238b9cb – volume: 12 year: 2019 ident: B15 article-title: An introduction to variational autoencoders publication-title: Foundations Trends Mach Learn doi: 10.1561/2200000056 – volume: 1907 year: 2019 ident: B24 article-title: Retina-VAE: Variationally decoding the spectrum of macular disease publication-title: ArXiv – volume: 8 year: 2022 ident: B12 article-title: Intraretinal layer segmentation using cascaded compressed u-nets publication-title: J Imaging doi: 10.3390/jimaging8050139 – volume: 58 year: 2017 ident: B37 article-title: Peripapillary retinal pigment epithelium layer shape changes from acetazolamide treatment in the idiopathic intracranial hypertension treatment trial publication-title: Invest Ophthalmol Visual Sci doi: 10.1167/iovs.16-21089 – volume: 15 year: 2024 ident: B10 article-title: Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve publication-title: Biomed Optics Express doi: 10.1364/BOE.516045 – volume-title: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & Data mining year: 2019 ident: B25 article-title: Glaucoma progression prediction using retinal thickness via latent space linear regression doi: 10.1145/3292500.3330757 – start-page: 1 volume-title: 2020 international joint conference on neural networks (IJCNN) year: 2020 ident: B20 article-title: Deep learning based unsupervised and semi-supervised classification for keratoconus doi: 10.1109/IJCNN48605.2020.9206694 – volume-title: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining year: 2016 ident: B30 article-title: Xgboost: A scalable tree boosting system doi: 10.1145/2939672.2939785 – volume: 3 year: 2020 ident: B26 article-title: Improving the structure-function relationship in glaucomatous visual fields by using a deep learning-based noise reduction approach publication-title: Ophthalmol Glaucoma doi: 10.1016/j.ogla.2020.01.001 – volume: 30 start-page: 1 year: 2022 ident: B8 article-title: Ms minute: Retinal optical coherence tomography for ms publication-title: Pratical Neurol – volume-title: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR). year: 2021 ident: B35 article-title: Soft-introvae: Analyzing and improving the introspective variational autoencoder doi: 10.1109/CVPR46437.2021.00437 – volume: 7 year: 2013 ident: B9 article-title: Long-term retinal nerve fiber layer changes following nonarteritic anterior ischemic optic neuropathy publication-title: Clin Ophthalmol doi: 10.2147/OPTH.S42522 – ident: B32 doi: 10.21105/joss.03021 – start-page: 1 volume-title: Neural information processing systems (NeurIPS) workshop on learning disentangled representations year: 2018 ident: B16 article-title: Understanding disentangling in β-VAE – volume: 13 start-page: 20586 year: 2023 ident: B22 article-title: Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning publication-title: Sci Rep doi: 10.1038/s41598-023-46903-5 – volume-title: Ophthalmic medical image analysis (OMIA) year: 2021 ident: B6 article-title: Representation and reconstruction of image-based structural patterns of glaucomatous defects using only two latent variables from a variational autoencoder doi: 10.1007/978-3-030-87000-3_17 – volume-title: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) year: 2016 ident: B17 article-title: Deep feature consistent variational autoencoder – volume: 131 start-page: 790 year: 2024 ident: B31 article-title: Ophthalmic and systemic factors of acute nonarteritic anterior ischemic optic neuropathy in the quark207 treatment trial publication-title: Ophthalmology doi: 10.1016/j.ophtha.2024.01.011 – volume: 4 start-page: 1 year: 2024 ident: B28 article-title: Efficacy of smoothing algorithms to enhance detection of visual field progression in glaucoma publication-title: Ophthalmol Sci doi: 10.1016/j.xops.2023.100423 |
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| Snippet | Glaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell (RGC) damage. We... IntroductionGlaucoma, optic neuritis (ON), and non-arteritic anterior ischemic optic neuropathy (NAION) produce distinct patterns of retinal ganglion cell... |
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| SubjectTerms | glaucoma non-arteritic anterior ischemic optic neuropathy (NAION) Ophthalmology optic neuritis (ON) optical coherence tomography (OCT) retinal ganglion cell (RGC) loss variational autoencoder (VAE) |
| Title | Quantifying the spatial patterns of retinal ganglion cell loss and progression in optic neuropathy by applying a deep learning variational autoencoder approach to optical coherence tomography |
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