Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Abstract We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomograph...
Guardado en:
Autores principales: | Jinho Lee, Young Kook Kim, Ahnul Ha, Sukkyu Sun, Yong Woo Kim, Jin-Soo Kim, Jin Wook Jeoung, Ki Ho Park |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/13bcdaeaa2c84e4db26f61e511de5669 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Relationship Between Central Corneal Thickness and Ganglionic-Inner Plexiform Cell Layer and Retinal Nerve Fibre Layer Thickness in Normal Subjects
por: Al Saad M, et al.
Publicado: (2021) -
Smooth borders between inner nuclear layer and outer plexiform layer predict fewer macular edema recurrences in branch retinal vein occlusion
por: Hirofumi Sasajima, et al.
Publicado: (2021) -
Assessment of patient specific information in the wild on fundus photography and optical coherence tomography
por: Marion R. Munk, et al.
Publicado: (2021) -
Ultrawide-field fundus photography of the first reported case of gyrate atrophy from Australia
por: Moloney TP, et al.
Publicado: (2014) -
An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
por: Aziz-ur-Rehman, et al.
Publicado: (2021)