Journal article
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
Frontiers in ophthalmology, Vol.4, 1497848
02/03/2025
DOI: 10.3389/fopht.2024.1497848
PMCID: PMC11830743
PMID: 39963427
Abstract
Introduction: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Details
- Title: Subtitle
- 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
- Creators
- Jui-Kai Wang - University of IowaBrett A. Johnson - University of IowaZhi Chen - University of IowaHonghai Zhang - University of IowaDavid Szanto - Icahn School of Medicine at Mount SinaiBrian Woods - Ollscoil na Gaillimhe – University of GalwayMichael Wall - University of IowaYoung H. Kwon - University of IowaEdward F. Linton - University of IowaAndrew Pouw - University of Iowa, Ophthalmology and Visual SciencesMark J. Kupersmith - Icahn School of Medicine at Mount SinaiMona K. Garvin - University of IowaRandy H. Kardon - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Frontiers in ophthalmology, Vol.4, 1497848
- DOI
- 10.3389/fopht.2024.1497848
- PMID
- 39963427
- PMCID
- PMC11830743
- NLM abbreviation
- Front Ophthalmol (Lausanne)
- ISSN
- 2674-0826
- eISSN
- 2674-0826
- Publisher
- FRONTIERS MEDIA SA; LAUSANNE
- Grant note
- Department of Veteran Affairs Center for the Prevention and Treatment of Visual Loss, Rehabilitation Research and Development (RRD): I50RX003002, RRD I01RX003797, RRD I01RX001786 National Institutes of Health (NIH): R01EY031544, R01EY023279 New York Eye and Ear Infirmary Foundation, New York, N.Y., National Eye Institute (NEI): R21EY032522
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported, in part, by the Department of Veteran Affairs Center for the Prevention and Treatment of Visual Loss, Rehabilitation Research and Development (RR&D) I50RX003002, RR&D I01RX003797, RR&D I01RX001786, National Institutes of Health (NIH) R01EY031544, and R01EY023279, The New York Eye and Ear Infirmary Foundation, New York, N.Y., National Eye Institute (NEI) R21EY032522.
- Language
- English
- Date published
- 02/03/2025
- Academic Unit
- Neurology; Electrical and Computer Engineering; Iowa Neuroscience Institute; The Iowa Institute for Biomedical Imaging; Ophthalmology and Visual Sciences
- Record Identifier
- 9984786454102771
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