Inteligencia artificial en ecocardiografía
DOI:
https://doi.org/10.37615/retic.v2n1a1Palabras clave:
machine learning, deep learning, inteligencia artificial.Resumen
Resumen de los principios y las aplicaciones de las técnicas de inteligencia artificial en Cardiologia
Descargas
Métricas
Citas
Kaplan A, Haenlein M, Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence, Business Horizons 2019; 62 (1): 15-25. doi: https://doi.org/10.1016/j.bushor.2018.08.004
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
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2019 Miguel Ángel García Fernández, Antonio López Farré
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
RETIC se distribuye bajo la licencia Creative Commons Reconocimiento-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0 que permite compartir, copiar y redistribuir el material en cualquier medio o formato, bajo los siguientes términos:
- Reconocimiento: debe otorgar el crédito correspondiente, proporcionar un enlace a la licencia e indicar si se realizaron cambios. Puede hacerlo de cualquier manera razonable, pero no de ninguna manera que sugiera que el licenciante lo respalda a usted o su uso.
- No comercial: no puede utilizar el material con fines comerciales.
- No Derivados: si remezcla, transforma o construye sobre el material, no puede distribuir el material modificado.
- Sin restricciones adicionales: no puede aplicar términos legales o medidas tecnológicas que restrinjan legalmente a otros de hacer cualquier cosa que permita la licencia.
