{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:43:43Z","timestamp":1787168623191,"version":"build-2736575974"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Cardiovasc Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based GLS might overcome these challenges. Our aims are to explore the agreements between AI-based GLS and conventional GLS, and to assess whether the agreements were influenced by expertise levels, cardiac remodeling and cardiovascular diseases\/risks. Echocardiographic images in the apical four-chamber view of left ventricle were retrospectively analyzed based on AI-based GLS in patients treated with chemotherapy, and correlation between AI-based GLS (Caas Qardia, Pie Medical Imaging) and conventional GLS (Vivid E9\/VividE95, GE Healthcare) were assessed. The agreement between unexperienced physicians (\u201cGLS beginner\u201d) and experienced echocardiographer were also assessed. Among 94 patients (mean age 69\u2009\u00b1\u200912 years, 73% female), mean left ventricular ejection fraction was 64\u2009\u00b1\u20096%, 14% of patients had left ventricular hypertrophy, and 21% had left atrial enlargement. Mean GLS was \u2212\u200915.9\u2009\u00b1\u20093.4% and \u2212\u200919.0\u2009\u00b1\u20093.7% for the AI and conventional method, respectively. There was a moderate correlation between these methods (rho\u2009=\u20090.74;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01), and bias was \u2212\u20093.1% (95% limits of agreement: -8.1 to 2.0). The reproducibility between GLS beginner and an experienced echocardiographer was numerically better in the AI method than the conventional method (inter-observer agreement\u2009=\u20090.82 vs. 0.68). The agreements were consistent across abnormal cardiac structure and function categories (p-for-interaction\u2009&gt;\u20090.10). In patients treated with chemotherapy. AI-based GLS was moderately correlated with conventional GLS and provided a numerically better reproducibility compared with conventional GLS, regardless of different levels of expertise.\n                  <\/jats:p>","DOI":"10.1007\/s10554-024-03178-9","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T06:09:51Z","timestamp":1721714991000},"page":"1903-1910","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy"],"prefix":"10.1007","volume":"40","author":[{"given":"Asuka","family":"Kuwahara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoichi","family":"Iwasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatake","family":"Kobayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryu","family":"Takagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoshi","family":"Yamada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Kubo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuhiro","family":"Satomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuhiro","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"3178_CR1","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3322\/caac.21349","volume":"66","author":"KD Miller","year":"2016","unstructured":"Miller KD, Siegel RL, Lin CC et al (2016) Cancer treatment and survivorship statistics, 2016. 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