Cargando…
Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images
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,...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510218/ https://www.ncbi.nlm.nih.gov/pubmed/31119087 http://dx.doi.org/10.7717/peerj.6900 |
_version_ | 1783417392653664256 |
---|---|
author | Masumoto, Hiroki Tabuchi, Hitoshi Nakakura, Shunsuke Ohsugi, Hideharu Enno, Hiroki Ishitobi, Naofumi Ohsugi, Eiko Mitamura, Yoshinori |
author_facet | Masumoto, Hiroki Tabuchi, Hitoshi Nakakura, Shunsuke Ohsugi, Hideharu Enno, Hiroki Ishitobi, Naofumi Ohsugi, Eiko Mitamura, Yoshinori |
author_sort | Masumoto, Hiroki |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-6510218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-65102182019-05-22 Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images Masumoto, Hiroki Tabuchi, Hitoshi Nakakura, Shunsuke Ohsugi, Hideharu Enno, Hiroki Ishitobi, Naofumi Ohsugi, Eiko Mitamura, Yoshinori PeerJ Ophthalmology 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. PeerJ Inc. 2019-05-07 /pmc/articles/PMC6510218/ /pubmed/31119087 http://dx.doi.org/10.7717/peerj.6900 Text en © 2019 Masumoto et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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. |
spellingShingle | Ophthalmology Masumoto, Hiroki Tabuchi, Hitoshi Nakakura, Shunsuke Ohsugi, Hideharu Enno, Hiroki Ishitobi, Naofumi Ohsugi, Eiko Mitamura, Yoshinori Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
title | Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
title_full | Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
title_fullStr | Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
title_full_unstemmed | Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
title_short | Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
title_sort | accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images |
topic | Ophthalmology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510218/ https://www.ncbi.nlm.nih.gov/pubmed/31119087 http://dx.doi.org/10.7717/peerj.6900 |
work_keys_str_mv | AT masumotohiroki accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT tabuchihitoshi accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT nakakurashunsuke accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT ohsugihideharu accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT ennohiroki accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT ishitobinaofumi accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT ohsugieiko accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages AT mitamurayoshinori accuracyofadeepconvolutionalneuralnetworkindetectionofretinitispigmentosaonultrawidefieldimages |