ID | 118454 |
Title Alternative | 心エコー図法における人工知能補助下の収縮能評価
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Author |
Yamaguchi, Natsumi
Tokushima University
Kosaka, Yoshitaka
Tokushima University
Sata, Masataka
Tokushima University
Tokushima University Educator and Researcher Directory
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Kusunose, Kenya
University of the Ryukyus
Tokushima University Educator and Researcher Directory
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Keywords | echocardiography
artificial
intelligence
ejection fraction
heart failure
diagnostic ability
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Content Type |
Thesis or Dissertation
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Description | 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. |
Journal Title |
Open Heart
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ISSN | 20533624
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Publisher | BMJ Publishing Group|British Cardiovascular Society
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Volume | 10
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Issue | 2
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Start Page | e002287
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Published Date | 2023-07-17
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Remark | 内容要旨・審査要旨・論文本文の公開
本論文は,著者Natsumi Yamaguchiの学位論文として提出され,学位審査・授与の対象となっている。 |
Rights | 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/.
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
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TextVersion |
ETD
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MEXT report number | 甲第3794号
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Diploma Number | 甲医第1598号
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Granted Date | 2024-03-22
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Degree Name |
Doctor of Medical Science
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Grantor |
Tokushima University
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departments |
Medical Sciences
University Hospital
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