Page 7 - RETIC_NOV_2019_Vol_2_1

Revista de ecocardiografía
práctica y otras técnicas de imagen cardíaca

Referencias:
1. 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.
2. 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.
3. 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.
4. Otani K, Nakazono A, Salgo IS, et al. Three-dimensional echocardiographic assessment of left heart chamber size and function with fully automated quantifi-
cation software in patients with atrial fibrillation. J Am Soc Echocardiogr 2016; 29: 955-965.
5. 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.
6. 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.
7. Kagiyama N, Toki M, Hara M, et al. Efficacy and accuracy of novel automated mitral valve quantification: three-dimensional transesophageal echocardiogra-
phic study. Echocardiography 2016; 33: 756-763.
8. Calleja A, Thavendiranathan P, Ionasec RI, et al. Automated quantitative 3-dimensional modeling of the aortic valve and root by 3-dimensional transesopha-
geal echocardiography in normals, aortic regurgitation, and aortic stenosis: comparison to computed tomography in normals and clinical implications. Circ
Cardiovasc Imaging 2013; 6: 99-108.
9. 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.
10. 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.
11. 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
12. Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 2018;
138: 1623-1635.

RETIC 2019 (2); 1: I-V V