Cargando…
Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography
PURPOSE: To develop deep learning (DL) models to predict best-corrected visual acuity (BCVA) from optical coherence tomography (OCT) images from patients with neovascular age-related macular degeneration (nAMD). METHODS: Retrospective analysis of OCT images and associated BCVA measurements from the...
Autores principales: | Kawczynski, Michael G., Bengtsson, Thomas, Dai, Jian, Hopkins, J. Jill, Gao, Simon S., Willis, Jeffrey R. |
---|---|
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/PMC7488630/ https://www.ncbi.nlm.nih.gov/pubmed/32974088 http://dx.doi.org/10.1167/tvst.9.2.51 |
Ejemplares similares
-
Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review
por: Yanagihara, Ryan T., et al.
Publicado: (2020) -
Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography
por: Heisler, Morgan, et al.
Publicado: (2020) -
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images
por: Cheong, Haris, et al.
Publicado: (2020) -
Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning
por: Guo, Yukun, et al.
Publicado: (2020) -
Correlation Between Retinal Microstructure Detected by Optical Coherence Tomography and Best Corrected Visual Acuity in Diabetic Retinopathy Macular Edema
por: Li, Siying, et al.
Publicado: (2022)