{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T19:38:47Z","timestamp":1789501127415,"version":"build-2803163510"},"reference-count":42,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["associationofanaesthetists-publications.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Anaesthesia"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Point\u2010of\u2010care transthoracic echocardiography performed by anaesthetists can influence peri\u2010operative management but is constrained by time, the need for measurements and advanced skill requirements. Artificial intelligence\u2010driven analysis may streamline this assessment and yield benefits to clinicians and patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this prospective multicentre observational study, adult patients who were referred for pre\u2010operative echocardiography were enrolled. Anaesthetists certified in echocardiography acquired a predefined 12\u2010view protocol. The same studies were uploaded to the US2.AI cloud platform, which generated measurements and categorical classifications for 10 key echocardiographic parameters. Manual measurements by clinicians served as the reference standard.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 206 enrolled patients, 202 (98%) with adequate image quality were analysed. Agreement between artificial intelligence\u2010 and clinician\u2010derived continuous measurements was good to excellent for most parameters, with intraclass correlation coefficient values 0.605\u20130.956 (p\u2009&lt;\u20090.001). Left ventricular ejection fraction was strongly correlated (r\u2009=\u20090.845, p\u2009&lt;\u20090.001) with a mean difference of \u20101.9%. The US2.AI software classified left ventricular systolic function correctly in 180\/201 (91%) patients and left ventricular diastolic dysfunction in 193\/201 (96%) patients. Correlations for right ventricular size and function, and right atrial size were strong (r\u2009=\u20090.860, 0.743 and 0.842, all p\u2009&lt;\u20090.001) with small mean differences. The US2.AI software identified all patients with pulmonary hypertension (n\u2009=\u200910) and severe aortic stenosis (n\u2009=\u20096) correctly. Agreement for inferior vena cava collapsibility (r\u2009=\u20090.641) and cardiac output (r\u2009=\u20090.675) was moderate with low mean bias. Cohen's \u03ba for categorical classifications was statistically significant for all parameters (p\u2009&lt;\u20090.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Using a limited predefined image sequence, anaesthetists can obtain most information essential for peri\u2010operative decision\u2010making. Agreement between US2.AI and clinicians was high for 10 echocardiographic parameters. These findings support integrating US2.AI into peri\u2010operative echocardiography workflows, with further studies needed to assess its impact on clinical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/anae.70235","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T03:00:47Z","timestamp":1778036447000},"page":"1219-1227","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence\u2010assisted peri\u2010operative echocardiography: a multicentre observational study"],"prefix":"10.1111","volume":"81","author":[{"given":"Deepak Prakash","family":"Borde","sequence":"first","affiliation":[{"name":"Department of Cardiac Anaesthesia Ozone Anaesthesia Group, Care CIIGMA Hospital  Chatrapati Sambhajinagar (Aurangabad) Maharashtra 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