ArticleIndian journal of anaesthesia2022
Automated versus manual B-lines counting, left ventricular outflow tract velocity time integral and inferior vena cava collapsibility index in COVID-19 patients.
Article in Indian journal of anaesthesia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- AI-enhanced guidance demonstrated improvement in novices' Apical-4-chamber and Apical-5-chamber views.BMC medical education · 2025Trial
- The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.Emergency radiology · 2025Article
- Performance of a point-of-care ultrasound platform for artificial intelligence-enabled assessment of pulmonary B-lines.Cardiovascular ultrasound · 2025Article
- Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study.Frontiers in veterinary science · 2025Article
- Continuing professional development for primary care physicians: a pre-post study on lung point-of-care ultrasound curriculum.BMC medical education · 2024Article
- Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients.Journal of personalized medicine · 2024Review
- Trends in research inIndian journal of anaesthesia · 2023Article
- Optimising clinical outcomes with innovative research in the intensive care unit.Indian journal of anaesthesia · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, 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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Registered trials
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