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

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 dataset in...

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Bibliographic Details
Published in:	Scientific reports Vol. 11; no. 1; pp. 2214 - 10
Main Authors:	Asano, Shotaro, Asaoka, Ryo, Murata, Hiroshi, Hashimoto, Yohei, Miki, Atsuya, Mori, Kazuhiko, Ikeda, Yoko, Kanamoto, Takashi, Yamagami, Junkichi, Inoue, Kenji
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 26.01.2021 Nature Publishing Group Nature Portfolio
Subjects:	692/308/1426 692/308/575 Datasets Deep learning Eye Glaucoma Humanities and Social Sciences multidisciplinary Neural networks Science Science (multidisciplinary) Tomography Visual field
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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