LAUSR

Background and Aims: The incorporation of artificial intelligence (AI) in point-of-care ultrasound (POCUS) has become a very useful tool to quickly assess cardiorespiratory function in coronavirus disease (COVID)-19 patients. The objective of this study was to test the agreement between manual and automated B-lines counting, left ventricular outflow tract velocity time integral (LVOT-VTI) and inferior vena cava collapsibility index (IVC-CI) in suspected or confirmed COVID-19 patients using AI integrated POCUS. In addition, we investigated the inter-observer, intra-observer variability and reliability of assessment of echocardiographic parameters using AI by a novice. Methods: Two experienced sonographers in POCUS and one novice learner independently and consecutively performed ultrasound assessment of B-lines counting, LVOT-VTI and IVC-CI in 83 suspected and confirmed COVID-19 cases which included both manual and AI methods. Results: Agreement between automated and manual assessment of LVOT-VTI, and IVC-CI were excellent [intraclass correlation coefficient (ICC) 0.98, P < 0.001]. Intra-observer reliability and inter-observer reliability of these parameters were excellent [ICC 0.96-0.99, P < 0.001]. Moreover, agreement between novice and experts using AI for LVOT-VTI and IVC-CI assessment was also excellent [ICC 0.95-0.97, P < 0.001]. However, correlation and intra-observer reliability between automated and manual B-lines counting was moderate [(ICC) 0.52-0.53, P < 0.001] and [ICC 0.56-0.69, P < 0.001], respectively. Inter-observer reliability was good [ICC 0.79-0.87, P < 0.001]. Agreement of B-lines counting between novice and experts using AI was weak [ICC 0.18, P < 0.001]. Conclusion: AI-guided assessment of LVOT-VTI, IVC-CI and B-lines counting is reliable and consistent with manual assessment in COVID-19 patients. Novices can reliably estimate LVOT-VTI and IVC-CI using AI software in COVID-19 patients.