Inteligencia artificial en ecocardiografía

Autores/as

  • Miguel Ángel García Fernández Cátedra de Imagen cardíaca. Universidad Complutense de Madrid. Madrid. España
  • Antonio López Farré Profesor Titular, Departamento de Medicina, Facultad de Medicina, Universidad Complutense de Madrid. Académico Correspondiente de la Real Academia Nacional de Medicina de España

DOI:

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

Palabras clave:

machine learning, deep learning, inteligencia artificial.

Resumen

Resumen de los principios y las aplicaciones de las técnicas de inteligencia artificial en Cardiologia

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Citas

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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

Tamborini G, Piazzese C, Lang RM, et al. Feasibility and accuracy of automated software for transthoracic three-dimensional left ventricular volume and function analysis: comparisons with two- dimensional echocardiography, three-dimensional transthoracic manual method, and cardiac magnetic resonance imaging. J Am Soc Echocardiogr 2017; 30: 1049-1058. doi: https://doi.org/10.1016/j.echo.2017.06.026

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

Kagiyama N, Toki M, Hara M, et al. Efficacy and accuracy of novel automated mitral valve quantification: three-dimensional transesophageal echocardiographic study. Echocardiography 2016; 33: 756-763. doi: https://doi.org/10.1111/echo.13135

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

Narula S, Shameer K, Salem Omar AM, et al. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. Journal of the American College of Cardiology 2016; 68: 2287-2295. doi: https://doi.org/10.1016/j.jacc.2016.08.062.

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

Sengupta PP, Huang YM, Bansal M, et al. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging 2016; 9(6): pii: e004330. doi: https://doi.org/10.1161/CIRCIMAGING.115.004330

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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Publicado

2019-12-31

Cómo citar

1.
García Fernández M Ángel, López Farré A. Inteligencia artificial en ecocardiografía. Rev Ecocar Pract (RETIC) [Internet]. 31 de diciembre de 2019 [citado 16 de abril de 2024];2(1):1-4. Disponible en: https://imagenretic.org/RevEcocarPract/article/view/213

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