Item type |
文献 / Documents(1) |
公開日 |
2020-12-01 |
アクセス権 |
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アクセス権 |
open access |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
出版社版DOI |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1038/s41598-020-76359-w |
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言語 |
ja |
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関連名称 |
10.1038/s41598-020-76359-w |
出版タイプ |
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出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
タイトル |
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タイトル |
Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray |
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言語 |
en |
著者 |
楠瀬, 賢也
ヒラタ, ユキナ
ツジ, タクマサ
コトク, ジュンイチ
佐田, 政隆
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抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure (PAP) and stratify the risk of heart failure hospitalization with PH. We retrospectively enrolled a total of 900 consecutive patients with suspected PH. We trained a convolutional neural network to identify patients with elevated PAP (> 20 mmHg) as the actual value of PAP. The endpoints in this study were admission or occurrence of heart failure with elevated PAP. In an independent evaluation set for detection of elevated PAP, the area under curve (AUC) by the AI algorithm was significantly higher than the AUC by measurements of CXR images and human observers (0.71 vs. 0.60 and vs. 0.63, all p < 0.05). In patients with AI predicted PH had 2-times the risk of heart failure with PH compared with those without AI predicted PH. This preliminary work suggests that applying AI to the CXR in high risk groups has limited performance when used alone in identifying elevated PAP. We believe that this report can serve as an impetus for a future large study. |
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言語 |
en |
書誌情報 |
en : Scientific Reports
巻 10,
p. 19311,
発行日 2020-11-17
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収録物ID |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
20452322 |
出版者 |
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出版者 |
Springer Nature |
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言語 |
en |
権利情報 |
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言語 |
en |
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権利情報 |
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
EID |
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識別子 |
372449 |
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識別子タイプ |
URI |
言語 |
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言語 |
eng |