Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images

Abstract We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field Analyzer (HFA) 24–2 test. The training d...

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Autores principales: Shotaro Asano, Ryo Asaoka, Hiroshi Murata, Yohei Hashimoto, Atsuya Miki, Kazuhiko Mori, Yoko Ikeda, Takashi Kanamoto, Junkichi Yamagami, Kenji Inoue
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/fe4bdf1312fc4483a90537b7a7d8d096
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