Three-dimensional automated, machine-learning-based left heart chamber metrics: reference values and cut-offs derived from a group of healthy subjects
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10554-024-03176-x.pdf
Reference5 articles.
1. Tsang W, Salgo IS, Medvedofsky D, et al (2016) Transthoracic 3D echocardiographic left heart chamber quantification using an automated adaptive analytics algorithm. JACC Cardiovasc. Imaging 9:769–782. https://doi.org/10.1016/j.jcmg.2015.12.020
2. Barbieri A, Albini A, Chiusolo S, et al. (2022) Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases. J Clin Med 11:7363. https://doi.org/10.3390/jcm11247363
3. Italiano G, Tamborini G, Fusini L, et al. (2021) Feasibility and accuracy of the automated software for dynamic quantification of left ventricular and atrial volumes and function in a large unselected population. J Clin Med 10:5030. https://doi.org/10.3390/jcm10215030
4. Addetia K, Miyoshi T, Amuthan V, et al. (2022) Normal Values of Left Ventricular Size and Function on Three-Dimensional Echocardiography: Results of the World Alliance Societies of Echocardiography Study. J Am Soc Echocardiogr 35:449–459. https://doi.org/10.1016/j.echo.2021.12.004
5. Dong T, Wang TKM (2024). Nuances in Defining Normal Ranges for Chamber Quantification With Cardiovascular Magnetic Resonance. Circ Cardiovasc Imaging Feb;17(2):e016488. https://doi.org/10.1161/CIRCIMAGING.124.016488
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