| Item type |
文献 / Documents(1) |
| 公開日 |
2022-02-01 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1371/journal.pone.0227240 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1371/journal.pone.0227240 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography |
|
言語 |
en |
| タイトル別表記 |
|
|
その他のタイトル |
Accurate tomographic detection of myopic macular diseases |
|
言語 |
en |
| 著者 |
ソガワ, タカヒロ
タブチ, ヒトシ
ナガサト, ダイスケ
マスモト, ヒロキ
イクノ, ヤスシ
オオスギ, ヒデハル
イシトビ, ナオフミ
三田村, 佳典
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs. high myopia (HM)], and OCT images with myopic macular lesions [e.g., myopic choroidal neovascularization (mCNV) and retinoschisis (RS)]. A total of 910 SS-OCT images were included in the study as follows and analyzed by k-fold cross-validation (k = 5) using DL's renowned model, Visual Geometry Group-16: nHM, 146 images; HM, 531 images; mCNV, 122 images; and RS, 111 images (n = 910). The binary classification of OCT images with or without myopic macular lesions; the binary classification of HM images and images with myopic macular lesions (i.e., mCNV and RS images); and the ternary classification of HM, mCNV, and RS images were examined. Additionally, sensitivity, specificity, and the area under the curve (AUC) for the binary classifications as well as the correct answer rate for ternary classification were examined.
The classification results of OCT images with or without myopic macular lesions were as follows: AUC, 0.970; sensitivity, 90.6%; specificity, 94.2%. The classification results of HM images and images with myopic macular lesions were as follows: AUC, 1.000; sensitivity, 100.0%; specificity, 100.0%. The correct answer rate in the ternary classification of HM images, mCNV images, and RS images were as follows: HM images, 96.5%; mCNV images, 77.9%; and RS, 67.6% with mean, 88.9%.Using noninvasive, easy-to-obtain swept-source OCT images, the DL model was able to classify OCT images without myopic macular lesions and OCT images with myopic macular lesions such as mCNV and RS with high accuracy. The study results suggest the possibility of conducting highly accurate screening of ocular diseases using artificial intelligence, which may improve the prevention of blindness and reduce workloads for ophthalmologists. |
|
言語 |
en |
| 書誌情報 |
en : PLOS ONE
巻 15,
号 4,
p. e0227240,
発行日 2020-04-16
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
19326203 |
| 出版者 |
|
|
出版者 |
PLOS |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
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, and reproduction in any medium, provided the original author and source are credited. |
| EID |
|
|
識別子 |
377002 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
pone_15_4_e0227240.pdf (1.71 MB)
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