{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:28:52Z","timestamp":1789738132334,"version":"4.0.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Aims<\/jats:title>\n                  <jats:p>An artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods and results<\/jats:title>\n                  <jats:p>The AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate\u2013severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 \u00b1 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group (P &amp;lt; 0.001). The AI-ECG was correlated with aortic valve area (\u03c1 = \u22120.48, R2 = 0.20), peak velocity (\u03c1 = 0.22, R2 = 0.08), and mean pressure gradient (\u03c1 = 0.35, R2 = 0.08). The AI-ECG also correlated with left ventricular (LV) mass index (\u03c1 = 0.36, R2 = 0.13), E\/e\u2032 (\u03c1 = 0.36, R2 = 0.12), and left atrium volume index (\u03c1 = 0.42, R2 = 0.12). Neither LV ejection fraction nor stroke volume index had a significant correlation with the AI-ECG. Age correlated with the AI-ECG (\u03c1 = 0.46, R2 = 0.22) and its correlation with echocardiography parameters was similar to that of the AI-ECG.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>A combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical\/functional features in the model and its identification process of AS is multifactorial.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztad009","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T15:53:56Z","timestamp":1675871636000},"page":"196-206","source":"Crossref","is-referenced-by-count":17,"title":["Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis"],"prefix":"10.1093","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7076-2340","authenticated-orcid":false,"given":"Saki","family":"Ito","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Diseases, Mayo Clinic , 200 First Street SW Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michal","family":"Cohen-Shelly","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Diseases, Mayo Clinic , 200 First Street SW Rochester, MN 55905 , USA"},{"name":"Department of Cardiology, Sheba Medical Center , Tel Hashomer , Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9706-7900","authenticated-orcid":false,"given":"Zachi I","family":"Attia","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Diseases, Mayo Clinic , 200 First Street SW Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunjung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Diseases, Mayo Clinic , 200 First Street SW Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul A","family":"Friedman","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Diseases, Mayo Clinic , 200 First Street SW Rochester, MN 55905 , 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