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
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.
Authors
- Randy H. Kardon (ORCID: https://orcid.org/0000-0002-3173-7123)
- Brett A. Johnson (ORCID: https://orcid.org/0000-0002-9260-245X)
- Mona K. Garvin (ORCID: https://orcid.org/0000-0003-3299-3150)
- Young H. Kwon (ORCID: https://orcid.org/0000-0002-1116-8250)
- Brian Woods
- Mark J. Kupersmith
- Edward F. Linton (ORCID: https://orcid.org/0000-0001-7058-9576)
- David Szanto (ORCID: https://orcid.org/0000-0002-5838-7717)
- Michael Wall (ORCID: https://orcid.org/0000-0002-9802-4806)
- Andrew Pouw (ORCID: https://orcid.org/0000-0001-8594-7612)
- Zhi Chen (ORCID: https://orcid.org/0000-0002-4385-1484)
- Honghai Zhang (ORCID: https://orcid.org/0000-0002-5010-2502)
- Jui-Kai Wang
Institutions
- University of Iowa (US)
- Ollscoil na Gaillimhe – University of Galway (IE)
- University Hospital Galway (IE)
- Iowa City VA Health Care System (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Frontiers in Ophthalmology
- Published
- 2025-02-03
- DOI
- https://doi.org/10.3389/fopht.2024.1497848
- Citations
- 6
- Primary Topic
- Optical Coherence Tomography Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.04