| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-04-15 |
| タイトル |
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|
タイトル |
Enhancing cervical cancer cytology screening via artificial intelligence innovation |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 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 |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
Kurita, Yuki
Meguro, Shiori
Kosugi, Isao
Enomoto, Yasunori
Kawasaki, Hideya
Kano, Tomoaki
Saitoh, Takeji
Shinmura, Kazuya
Iwashita, Toshihide
|
| 書誌情報 |
en : Scientific Reports
巻 14,
p. 19535,
発行日 2024-08-22
|
| 出版者 |
|
|
出版者 |
Springer Nature |
|
言語 |
en |
| 権利 |
|
|
権利情報 |
(C) The Author(s) 2024 |
| 権利 |
|
|
権利情報 |
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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://creative commons.org/licenses/by-nc-nd/4.0/. |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
A double-check process helps prevent errors and ensures quality control. However, it may lead to decreased personal accountability, reduced effort, and declining quality checks. Introducing an artificial intelligence (AI)-based system in such scenarios could effectively address the risk of oversights. This study introduces an innovative AI-integrated workflow for cervical cytology screening that substantially improves efficiency and reduces the burden on cytologists. The AI model prioritizes cases for review based on anomaly scores and streamlines the first screening process to approximately 10 s per case. The model enhances the identification of high-risk cases via detailed microscopic observation, high anomaly scores cases, and a targeted review of low-score cases. The workflow highlights its capability for rapid, accurate, and less labor-intensive evaluations, demonstrating the potential to transform cervical cancer screening. This study highlights the importance of AI in modern medical diagnostics, particularly in areas with a high demand for accuracy and efficiency. |
| 注記 |
|
|
内容記述 |
令和6年 浜松医科大学論文賞：優秀論文賞（学生特別枠）
受賞者：栗田 佑希 |
| EISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2045-2322 |
| PubMed番号 |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
PMID |
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関連識別子 |
39174613 |
| 出版社DOI |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1038/s41598-024-70670-6 |
| 著者版フラグ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
Sci Rep-14-19535.pdf (2.8 MB)
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