Automatic assessment of left ventricular function for hemodynamic monitoring using artificial intelligence and transesophageal echocardiography
Author:
Funder
Helse Midt-Norge
Publisher
Springer Science and Business Media LLC
Subject
Anesthesiology and Pain Medicine,Critical Care and Intensive Care Medicine,Health Informatics,Anesthesiology and Pain Medicine,Critical Care and Intensive Care Medicine,Health Informatics
Link
https://link.springer.com/content/pdf/10.1007/s10877-023-01118-x.pdf
Reference35 articles.
1. Cecconi M, De Backer D, Antonelli M, et al. Consensus on circulatory shock and hemodynamic monitoring. Task force of the European Society of Intensive Care Medicine. Intensive Care Med. 2014;40:1795–815. https://doi.org/10.1007/s00134-014-3525-z.
2. Cole GD, Dhutia NM, Shun-Shin MJ, et al. Defining the real-world reproducibility of visual grading of left ventricular function and visual estimation of left ventricular ejection fraction: impact of image quality, experience and accreditation. Int J Cardiovasc Imaging. 2015;31:1303–14. https://doi.org/10.1007/s10554-015-0659-1.
3. Salte IM, Østvik A, Olaisen SH, et al. Deep learning for improved precision and reproducibility of left ventricular strain in echocardiography: a test-retest study. J Am Soc Echocardiogr. 2023;36:788–99. https://doi.org/10.1016/j.echo.2023.02.017.
4. Cecconi M, Rhodes A, Poloniecki J, et al. Bench-to-bedside review: the importance of the precision of the reference technique in method comparison studies—with specific reference to the measurement of cardiac output. Crit Care. 2009;13:201. https://doi.org/10.1186/cc7129.
5. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020;580:252–6. https://doi.org/10.1038/s41586-020-2145-8.
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