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ID 115190
Author
Nagasawa, Toshihiko Saneikai Tsukazaki Hospital
Tabuchi, Hitoshi Saneikai Tsukazaki Hospital
Masumoto, Hiroki Saneikai Tsukazaki Hospital
Enno, Hiroki Rist Inc.
Ohara, Zaigen Saneikai Tsukazaki Hospital
Yoshizumi, Yuki Saneikai Tsukazaki Hospital
Ohsugi, Hideharu Saneikai Tsukazaki Hospital
Keywords
Ultrawide-field fundus ophthalmoscopy
Proliferative diabetic retinopathy
Deep learning
Deep convolutional neural network
Content Type
Journal Article
Description
Purpose
We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR).
Methods
We conducted training with the DCNN using 378 photographic images (132 PDR and 246 non-PDR) and constructed a deep learning model. The area under the curve (AUC), sensitivity, and specificity were examined.
Result
The constructed deep learning model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969.
Conclusion
Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning.
Journal Title
International Ophthalmology
ISSN
15732630
NCID
AA00234448
Publisher
Springer Nature
Volume
39
Issue
10
Start Page
2153
End Page
2159
Published Date
2019-02-23
Rights
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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DOI (Published Version)
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language
eng
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departments
Medical Sciences