Cargando…
An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma
PURPOSE: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations with visual field (VF) loss in glaucoma. METHODS: We used paired reliable 24-2 VFs and optical coherence tomography scans of 691 eyes from 691 patients...
Autores principales: | Wang, Mengyu, Shen, Lucy Q., Pasquale, Louis R., Wang, Hui, Li, Dian, Choi, Eun Young, Yousefi, Siamak, Bex, Peter J., Elze, Tobias |
---|---|
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/PMC7453051/ https://www.ncbi.nlm.nih.gov/pubmed/32908804 http://dx.doi.org/10.1167/tvst.9.9.41 |
Ejemplares similares
-
An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging
por: Yousefi, Siamak, et al.
Publicado: (2023) -
Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence
por: Huang, Xiaoqin, et al.
Publicado: (2021) -
Assessing Surface Shapes of the Optic Nerve Head and Peripapillary Retinal Nerve Fiber Layer in Glaucoma with Artificial Intelligence
por: Saini, Chhavi, et al.
Publicado: (2022) -
Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma
por: Shi, Min, et al.
Publicado: (2023) -
The impact of artificial intelligence in the diagnosis and management of glaucoma
por: Mayro, Eileen L., et al.
Publicado: (2019)