Key result
Automated AI-guided assessment of LVOT-VTI and IVC-CI demonstrated excellent agreement with manual assessment (ICC 0.987 and 0.99, respectively) in spontaneously breathing patients with COVID-19.
Why the study?
To evaluate the agreement between manual and automated AI-integrated POCUS measurements (B-lines, LVOT-VTI, IVC-CI) and assess inter- and intra-observer reliability, including novice use, in COVID-19 patients.
Does automated AI-integrated POCUS agree with manual assessment for measuring LVOT-VTI, IVC-CI, and B-lines in patients with suspected or confirmed COVID-19?
Observational (n=83)
Single-blind
No
Does automated AI-integrated POCUS agree with manual assessment for measuring LVOT-VTI, IVC-CI, and B-lines in patients with suspected or confirmed COVID-19?
Effect estimate: ICC 0.987 (95% CI 0.980-0.99)
p-value: p=<0.001
AI-integrated POCUS allows for reliable automated assessment of LVOT-VTI and IVC-CI, comparable to manual assessment and accessible to novice users, though automated B-line counting remains less reliable.
May support automated LVOT-VTI and IVC-CI by novices in COVID-19; leaves open B-line reliability pending prospective validation.
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.
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Damodaran et al. (2022) conducted an observational in COVID-19 (n=83). Automated AI-integrated point-of-care ultrasound (POCUS) vs. Manual point-of-care ultrasound (POCUS) was evaluated on Agreement between manual and automated left ventricular outflow tract velocity time integral (LVOT-VTI) (ICC 0.987, 95% CI 0.980-0.99, p=<0.001). Automated AI-guided assessment of LVOT-VTI and IVC-CI demonstrated excellent agreement with manual assessment (ICC 0.987 and 0.99, respectively) in spontaneously breathing patients with COVID-19.
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