Artificial intelligence-assisted interpretation of systolic function by echocardiogram

• Material Type: 博士論文
• Title: Artificial intelligence-assisted interpretation of systolic function by echocardiogram
• Author/Editor: Yamaguchi, Natsumi; Kosaka, Yoshitaka; Haga, Akihiro; Sata, Masataka; Kusunose, Kenya
• Author Heading: Yamaguchi, Natsumi; Kosaka, Yoshitaka; Haga, Akihiro; Sata, Masataka; Kusunose, Kenya
• Publication, Distribution, etc.: BMJ Publishing Group|British Cardiovascular Society
• Publication Date: 2023-07-17
• Publication Date (W3CDTF): 2023-07-17
• Alternative Title: 心エコー図法における人工知能補助下の収縮能評価
• Periodical title: Open Heart
• No. or year of volume/issue: 10 2
• Volume: 10
• Issue: 2
• Pages: e002287-
• ISSN (Periodical Title): ISSN : 2053-3624
• Degree grantor/type: 徳島大学
• Date Granted: 2024-03-22
• Dissertation Number: 甲第3794号
• Degree Type: 博士（医学）
• Text Language Code: eng
• Subject Heading: echocardiography; artificial; intelligence; ejection fraction; heart failure; diagnostic ability
• Target Audience: 一般
• Note (General): Objective: Precise and reliable echocardiographic assessment of left ventricular ejection fraction (LVEF) is needed for clinical decision-making. Recently, artificial intelligence (AI) models have been developed to estimate LVEF accurately. The aim of this study was to evaluate whether an AI model could estimate an expert read of LVEF and reduce the interinstitutional variability of level 1 readers with the AI-LVEF displayed on the echocardiographic screen. Methods: This prospective, multicentre echocardiographic study was conducted by five cardiologists of level 1 echocardiographic skill (minimum level of competency to interpret images) from different hospitals. Protocol 1: Visual LVEFs for the 48 cases were measured without input from the AI-LVEF. Protocol 2: the 48 cases were again shown to all readers with inclusion of AI-LVEF data. To assess the concordance and accuracy with or without AI-LVEF, each visual LVEF measurement was compared with an average of the estimates by five expert readers as a reference. Results: A good correlation was found between AI-LVEF and reference LVEF (r=0.90, p<0.001) from the expert readers. For the classification LVEF, the area under the curve was 0.95 on heart failure with preserved EF and 0.96 on heart failure reduced EF. For the precision, the SD was reduced from 6.1±2.3 to 2.5±0.9 (p<0.001) with AI-LVEF. For the accuracy, the root-mean squared error was improved from 7.5±3.1 to 5.6±3.2 (p=0.004) with AI-LVEF. Conclusions: AI can assist with the interpretation of systolic function on an echocardiogram for level 1 readers from different institutions.
• Access Restrictions: インターネット公開
• Rights (production): This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
• Related Material (DOI): 10.1136/openhrt-2023-002287
• 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): 徳島大学 : 徳島大学機関リポジトリ

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http://repo.lib.tokushima-u.ac.jp/files/public/11/118454/20240502113317528394/k3794_abstract.pdf fulltext

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