{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:22:24Z","timestamp":1790691744320,"version":"4.1.0"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Echocardiography"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Assessment of left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) plays a key role in the diagnosis of cardiac amyloidosis (CA). However, manual measurements are time consuming and prone to variability. We aimed to assess whether fully automated artificial intelligence (AI) calculation of LVEF and GLS provide similar estimates and can identify abnormalities in agreement with conventional manual methods, in patients with pre\u2010clinical and clinical CA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We identified 51 patients (age 80\u00a0\u00b1\u00a010 years, 53% male) with confirmed CA according to guidelines, who underwent echocardiography before and\/or at the time of CA diagnosis (median (IQR) time between observations 3.87 (1.93, 5.44 years). LVEF and GLS were quantified from the apical 2\u2010 and 4\u2010chamber views using both manual and fully automated methods (EchoGo Core 2.0, Ultromics). Inter\u2010technique agreement was assessed using linear regression and Bland\u2010Altman analyses and two\u2010way ANOVA. The diagnostic accuracy and time for detecting abnormalities (defined as LVEF \u2264 50% and GLS \u2265 \u221215.1%, respectively) using AI was assessed by comparisons to manual measurements as a reference.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>There were no significant differences in manual and automated LVEF and GLS values in either pre\u2010CA (<jats:italic>p<\/jats:italic>\u00a0=\u00a0.791 and <jats:italic>p<\/jats:italic>\u00a0=\u00a0.105, respectively) or at diagnosis (<jats:italic>p<\/jats:italic>\u00a0=\u00a0.463 and <jats:italic>p<\/jats:italic>\u00a0=\u00a0.722). The two methods showed strong correlation on both the pre\u2010CA (<jats:italic>r<\/jats:italic>\u00a0=\u00a0.78 and <jats:italic>r<\/jats:italic>\u00a0=\u00a0.83) and CA echoes (<jats:italic>r<\/jats:italic>\u00a0=\u00a0.74 and <jats:italic>r<\/jats:italic>\u00a0=\u00a0.80) for LVEF and GLS, respectively. The sensitivity and specificity of AI\u2010derived indices for detecting abnormal LVEF were 83% and 86%, respectively, in the pre\u2010CA echo and 70% and 79% at CA diagnosis. The sensitivity and specificity of AI\u2010derived indices for detecting abnormal GLS was 82% and 86% in the pre\u2010CA echo and 100% and 67% at the time of CA diagnosis. There was no significant difference in the relationship between LVEF (<jats:italic>p<\/jats:italic>\u00a0=\u00a0.99) and GLS (<jats:italic>p<\/jats:italic>\u00a0=\u00a0.19) and time to abnormality between the two methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Fully automated AI\u2010calculated LVEF and GLS are comparable to manual measurements in patients pre\u2010CA and at the time of CA diagnosis. The widespread implementation of automated LVEF and GLS may allow for more rapid assessment in different disease states with comparable accuracy and reproducibility to manual methods.<\/jats:p><\/jats:sec>","DOI":"10.1111\/echo.15516","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T15:53:09Z","timestamp":1673279589000},"page":"188-195","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Artificial intelligence based left ventricular ejection fraction and global longitudinal strain in cardiac amyloidosis"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4317-8725","authenticated-orcid":false,"given":"Juan I.","family":"Cotella","sequence":"first","affiliation":[{"name":"University of Chicago Medical Center  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeremy A.","family":"Slivnick","sequence":"additional","affiliation":[{"name":"University of Chicago Medical Center  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Sanderson","sequence":"additional","affiliation":[{"name":"Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford, OX4 2SU  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristiane","family":"Singulane","sequence":"additional","affiliation":[{"name":"University of Chicago Medical Center  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamie","family":"O'Driscoll","sequence":"additional","affiliation":[{"name":"Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford, OX4 2SU  Oxford UK"},{"name":"Canterbury Christ Church University School of Psychology, Politics and Sociology  Canterbury UK"},{"name":"Department of Cardiology St George's University Hospitals NHS Foundation Trust  Tooting London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico M.","family":"Asch","sequence":"additional","affiliation":[{"name":"Cardiovascular Core Labs MedStar Health Research Institute  Washington DC Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6424-7860","authenticated-orcid":false,"given":"Karima","family":"Addetia","sequence":"additional","affiliation":[{"name":"University of Chicago Medical Center  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary","family":"Woodward","sequence":"additional","affiliation":[{"name":"Ultromics Ltd, 4630 Kingsgate, Cascade Way, Oxford Business Park South, Oxford, OX4 2SU  Oxford UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto M.","family":"Lang","sequence":"additional","affiliation":[{"name":"University of Chicago Medical Center  Chicago Illinois 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