Cargando…
A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans
PURPOSE: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performan...
Autores principales: | Russakoff, Daniel B., Mannil, Suria S., Oakley, Jonathan D., Ran, An Ran, Cheung, Carol Y., Dasari, Srilakshmi, Riyazzuddin, Mohammed, Nagaraj, Sriharsha, Rao, Harsha L., Chang, Dolly, Chang, Robert T. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347026/ https://www.ncbi.nlm.nih.gov/pubmed/32704418 http://dx.doi.org/10.1167/tvst.9.2.12 |
Ejemplares similares
-
Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets
por: Noury, Erfan, et al.
Publicado: (2022) -
Agreement and Precision of Wide and Cube Scan Measurements between Swept-source and Spectral-domain OCT in Normal and Glaucoma Eyes
por: Hou, Huiyuan, et al.
Publicado: (2023) -
Agreement and precision of wide and cube scan measurements between swept-source and spectral-domain OCT in normal and glaucoma eyes
por: Hou, Huiyuan, et al.
Publicado: (2023) -
Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study
por: Sodhi, Simrat K., et al.
Publicado: (2022) -
Comparison of Rates of Progression of Macular OCT Measures in Glaucoma
por: Rabiolo, Alessandro, et al.
Publicado: (2020)