Observational studyAnaesthesia2026
Artificial intelligence-assisted peri-operative echocardiography: a multicentre observational study.
Observational study in Anaesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPoint-of-care transthoracic echocardiography performed by anaesthetists can influence peri-operative management but is constrained by time, the need for measurements and advanced skill requirements. Artificial intelligence-driven analysis may streamline this assessment and yield benefits to clinicians and patients.
methodsIn this prospective multicentre observational study, adult patients who were referred for pre-operative echocardiography were enrolled. Anaesthetists certified in echocardiography acquired a predefined 12-view 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.
resultsOf 206 enrolled patients, 202 (98%) with adequate image quality were analysed. Agreement between artificial intelligence- and clinician-derived continuous measurements was good to excellent for most parameters, with intraclass correlation coefficient values 0.605-0.956 (p < 0.001). Left ventricular ejection fraction was strongly correlated (r = 0.845, p < 0.001) with a mean difference of -1.9%. 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 = 0.860, 0.743 and 0.842, all p < 0.001) with small mean differences. The US2.AI software identified all patients with pulmonary hypertension (n = 10) and severe aortic stenosis (n = 6) correctly. Agreement for inferior vena cava collapsibility (r = 0.641) and cardiac output (r = 0.675) was moderate with low mean bias. Cohen's κ for categorical classifications was statistically significant for all parameters (p < 0.001). DISCUSSION: Using a limited predefined image sequence, anaesthetists can obtain most information essential for peri-operative decision-making. Agreement between US2.AI and clinicians was high for 10 echocardiographic parameters. These findings support integrating US2.AI into peri-operative echocardiography workflows, with further studies needed to assess its impact on clinical outcomes.
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