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ID 115681
Author
Nagasawa, Toshihiko Tsukazaki Hospital
Tabuchi, Hitoshi Tsukazaki Hospital
Masumoto, Hiroki Tsukazaki Hospital
Enno, Hiroki Rist Inc.
Ohsugi, Hideharu Tsukazaki Hospital
Keywords
Wide-angle ocular fundus camera
Macular holes
Deep learning
Optos
Convolutional neural network
Algorithm
Wide- angle camera
Content Type
Journal Article
Description
We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning.
Journal Title
PeerJ
ISSN
21678359
Volume
6
Start Page
e5696
Published Date
2018-10-22
Rights
This is an open access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
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DOI (Published Version)
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language
eng
TextVersion
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departments
Medical Sciences