Artificial intelligence in echocardiography

Authors

  • Miguel Ángel García Fernández Chair of Cardiac Imaging. Complutense University of Madrid. Madrid. Spain
  • Antonio López Farré Full Professor, Department of Medicine, Faculty of Medicine, Universidad Complutense de Madrid. Corresponding Academician of the Royal National Academy of Medicine of Spain.

DOI:

https://doi.org/10.37615/retic.v2n1a1

Keywords:

artificial intelligence, deep learning, machine learning.

Abstract

Summary of the principles and applications of artificial intelligence techniques in cardiology.

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References

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Khamis H, Zurakhov G, Azar V, et al. Automatic apical view classification of echocardiograms using a discriminative learning dictionary. Medical Image Analysis 2017; 36: 15-21. doi: https://doi.org/10.1016/j.media.2016.10.007

Madani A, Arnaout R, Mofrad M, Arnaout R. Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med 2018; 6: 1-8. doi: https://doi.org/10.1038/s41746-017-0013-1

Otani K, Nakazono A, Salgo IS, et al. Three-dimensional echocardiographic assessment of left heart chamber size and function with fully automated quantification software in patients with atrial fibrillation. J Am Soc Echocardiogr 2016; 29: 955-965. doi: https://doi.org/10.1016/j.echo.2016.06.010

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De Agustin JA, Marcos-Alberca P, Fernandez-Golfin C, et al. Direct measurement of proximal isovelocity surface area by single-beat three-dimensional color Doppler echocardiography in mitral regurgitation: a validation study. J Am Soc Echocardiogr 2012; 25: 815-823. doi: https://doi.org/10.1016/j.echo.2012.05.021

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Calleja A, Thavendiranathan P, Ionasec RI, et al. Automated quantitative 3-dimensional modeling of the aortic valve and root by 3-dimensional transesophageal echocardiography in normals, aortic regurgitation, and aortic stenosis: comparison to computed tomography in normals and clinical implications. Circ Cardiovasc Imaging 2013; 6: 99-108. doi: https://doi.org/10.1161/CIRCIMAGING.112.976993

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Omar HA, Domingos JS, Patra A, et al. Quantification of cardiac bull’s-eye map based on principal strain analysis for myocardial wall motion assessment in stress echocardiography. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018. doi: https://doi.org/10.1109/ISBI.2018.8363785

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Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 2018; 138: 1623-1635. doi: https://doi.org/10.1161/CIRCULATIONAHA.118.034338

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Published

2019-12-31

How to Cite

1.
García Fernández M Ángel, López Farré A. Artificial intelligence in echocardiography. Rev Ecocardiogr Pract Otras Tec Imag Card (RETIC) [Internet]. 2019 Dec. 31 [cited 2024 Nov. 22];2(1):1-4. Available from: https://imagenretic.org/RevEcocarPract/article/view/213

Issue

Vol. 2 No. 1 (2019): Journal of Practical Echocardiography and Other Cardiac Imaging Techniques

Section

EDITORIAL

License

Copyright (c) 2019 Miguel Ángel García Fernández, Antonio López Farré

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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