| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-12-18 |
| タイトル |
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|
タイトル |
Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis |
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言語 |
en |
| 言語 |
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|
言語 |
eng |
| キーワード |
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|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
gallbladder disease |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
lesions |
| キーワード |
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|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
endoscopic ultrasound |
| キーワード |
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|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
artificial intelligence |
| 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 |
| 著者 |
Takahashi, Kosuke
Ozawa, Eisuke
Shimakura, Akane
Mori, Tomotaka
Miyaaki, Hisamitsu
Nakao, Kazuhiko
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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 |
Gallbladder (GB) disease is classified into two broad categories: GB wall-thickening and protuberant lesions, which include various lesions, such as adenomyomatosis, cholecystitis, GB polyps, and GB carcinoma. This review summarizes recent advances in the differential diagnosis of GB lesions, focusing primarily on endoscopic ultrasound (EUS) and related technologies. Fundamental B-mode EUS and contrast-enhanced harmonic EUS (CH-EUS) have been reported to be useful for the diagnosis of GB diseases because they can evaluate the thickening of the GB wall and protuberant lesions in detail. We also outline the current status of EUS-guided fine-needle aspiration (EUS-FNA) for GB lesions, as there have been scattered reports on EUS-FNA in recent years. Furthermore, artificial intelligence (AI) technologies, ranging from machine learning to deep learning, have become popular in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification. In this review, we outline the current status of AI in the diagnosis of GB. |
|
言語 |
en |
| 書誌情報 |
en : Diagnostics
巻 14,
号 4,
p. art. no. 374,
発行日 2024-02-08
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| 出版者 |
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出版者 |
MDPI |
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言語 |
en |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2075-4418 |
| DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.3390/diagnostics14040374 |
| 権利 |
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権利情報 |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
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言語 |
en |
| 著者版フラグ |
|
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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 |
| 引用 |
|
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内容記述タイプ |
Other |
|
内容記述 |
Diagnostics, 14(4), art. no. 374; 2024 |
|
言語 |
en |
D14_374.pdf (5.4 MB)
