Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest

PurposeTo evaluate the diagnostic accuracy of a deep learning autoencoder-based model utilizing regions of interest (ROI) from optical coherence tomography (OCT) texture enface images for detecting glaucoma in myopic eyes.MethodsThis cross-sectional study included a total of 453 eyes from 315 partic...

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Bibliographic Details
Main Authors:	Christopher Bowd, Akram Belghith, Mark Christopher, Makoto Araie, Aiko Iwase, Goji Tomita, Kyoko Ohno-Matsui, Hitomi Saito, Hiroshi Murata, Tsutomu Kikawa, Kazuhisa Sugiyama, Tomomi Higashide, Atsuya Miki, Toru Nakazawa, Makoto Aihara, Tae-Woo Kim, Christopher Kai Shun Leung, Robert N. Weinreb, Linda M. Zangwill
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2025-08-01
Series:	Frontiers in Ophthalmology
Subjects:	glaucoma myopia optical coherence tomography deep learning artificial intelligence diagnosis
Online Access:	https://www.frontiersin.org/articles/10.3389/fopht.2025.1624015/full
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Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest

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