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ID 115680
Author
Masumoto, Hiroki Tsukazaki Hospital
Tabuchi, Hitoshi Tsukazaki Hospital
Nakakura, Shunsuke Tsukazaki Hospital
Ohsugi, Hideharu Tsukazaki Hospital
Enno, Hiroki Rist Inc.
Ishitobi, Naofumi Tsukazaki Hospital
Ohsugi, Eiko Tsukazaki Hospital
Keywords
Neural network
Retinitis pigmentosa
Screening system
Ultrawide-filed pseudocolor imaging
Ultrawide-field autofluorescence
Content Type
Journal Article
Description
Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K-fold cross validation (K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953–1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994–1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%–100.0%]) and 99.1% (95% CI [96.1%–99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%–100%]) and 99.5% (95% CI [96.8%–99.9%]), respectively. Heatmaps were in accordance with the clinician’s observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images.
Journal Title
PeerJ
ISSN
21678359
Volume
7
Start Page
e6900
Published Date
2019-05-07
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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language
eng
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departments
Medical Sciences