Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images

• Material Type: 博士論文
• Title: Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images
• Author/Editor: 千葉, 章代
• Author Heading: 千葉, 章代
• Publication, Distribution, etc.: Springer Nature Switzerland AG.
• Publication Date: 2021-03-11
• Publication Date (W3CDTF): 2021-03-11
• Alternative Title: 心筋血流画像を読影する際に、初心者が熟練者と同様の解釈を得るための人工知能の有用性
• Periodical title: The International Journal of Cardiovascular Imaging
• ISSN (Periodical Title): ISSN : 1573-0743
• Degree grantor/type: Nagasaki University (長崎大学)
• Date Granted: 2021-06-02
• Dissertation Number: 甲医歯薬第1351号
• Degree Type: 博士(医学)
• Text Language Code: eng
• Subject Heading: Artificial neural network; Myocardial perfusion images; Artificial intelligence; Nuclear medicine; Cardiology imaging
• Target Audience: 一般
• Note (General): This study examined whether using an artificial neural network (ANN) helps beginners in diagnostic cardiac imaging to achieve similar results to experts when interpreting stress myocardial perfusion imaging (MPI). One hundred and thirty-eight patients underwent stress MPI with Tc-labeled agents. An expert and a beginner interpreted stress/rest MPI with or without the ANN and the results were compared. The myocardium was divided into 5 regions (the apex; septum; anterior; lateral, and inferior regions), and the defect score of myocardial blood flow was evaluated from 0 to 4, and SSS, SRS, and SDS were calculated. The ANN effect, defined as the difference in each of these scores between with and without the ANN, was calculated to investigate the influence of ANN on the interpreters' performance. We classified 2 groups (insignificant perfusion group and significant perfusion group) and compared them. In the same way, classified 2 groups (insignificant ischemia group and significant ischemia group) and compared them. Besides, we classified 2 groups (normal vessels group and multi-vessels group) and compared them. The ANN effect was smaller for the expert than for the beginner. Besides, the ANN effect for insignificant perfusion group, insignificant ischemia group and multi-vessels group were smaller for the expert than for the beginner. On the other hand, the ANN effect for significant perfusion group, significant ischemia group and normal vessels group were no significant. When interpreting MPI, beginners may achieve similar results to experts by using an ANN. Thus, interpreting MPI with ANN may be useful for beginners. Furthermore, when beginners interpret insignificant perfusion group, insignificant ischemia group and multi-vessel group, beginners may achieve similar results to experts by using an ANN.; 長崎大学学位論文 学位記番号:博(医歯薬)甲第1351号 学位授与年月日:令和3年6月2日; Author: A. Chiba, T. Kudo, R. Ideguchi, M. Altay, S. Koga, T. Yonekura, A. Tsuneto, M. Morikawa, S. Ikeda, H. Kawano, Y. Koide, M. Uetani and K. Maemura; Citation: The International Journal of Cardiovascular Imaging, Article in Press; 2021; identifier:Nagasaki University (長崎大学), 博士(医学) (2021-06-02)
• Source: https://nagasaki-u.repo.nii.ac.jp/?action=repository_action_common_download&item_id=26670&item_no=1&attribute_id=70&file_no=1 （fulltext）; https://nagasaki-u.repo.nii.ac.jp/?action=repository_action_common_download&item_id=26670&item_no=1&attribute_id=70&file_no=1
• Access Restrictions: インターネット公開
• Rights (production): © The Author(s) 2021. Open Access 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/.
• Related Material: http://hdl.handle.net/10069/00040834
• Related Material (DOI): 10.1007/s10554-021-02209-z
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 長崎大学 : 長崎大学学術研究成果リポジトリ