Inteligencia artificial en imagen cardíacael futuro ya está aquí
- 1 Universidad Complutense de Madrid(España)
Revista:
Revista Argentina de Cardiología (RAC)
Año de publicación: 2019
Volumen: 87
Número: 6
Páginas: 491-495
Tipo: Artículo
Referencias bibliográficas
- 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. Bus Horiz 2019;62:15-25. http://doi.org/gf443d
- Topol E. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56. http://doi.org/gfsvzn
- Al’Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu
- Z, et al. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry Eur Heart J 2019 pii:ehz565. http://doi.org/df68
- Kang D, Dey D, Slomka PJ, Arsanjani R, Nakazato R, Ko H, et al. Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography. J Med Imaging (Bellingham). 2015;2:014003. http:// doi.org/df69
- Tesche C, De Cecco CN, Albrecht MH, Duguay TM, Bayer RR, Litwin SE, et al. Coronary CT angiography-derived fractional flow reserve. Radiology 2017;285:17-33. http://doi.org/gbz8hn
- Duguay TM, Tesche C, Vliegenthart R, De Cecco CN, Lin H,
- Albrecht MH, et al. Coronary computed tomographic angiography-derived fractional flow reserve based on machine learning for risk stratification of non-culprit coronary narrowing in Patients with Acute Coronary Syndrome. Am J Cardiol. 2017;120:1260-6. http:// doi.org/gchvwt
- Commandeur F, Goeller M, Betancur J, Cadet S, Doris M, Chen X, et al. Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT. IEEE Trans Med Imaging. 2018;37:1835-46. http://doi.org/df7b.
- A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis. Circ Cardiovasc Imaging. 2019;12:e009214. http://doi.org/df8q
- Medvedofsky D, Addetia K, Hamilton J, Leon Jimenez J, Lang
- RM, Mor-Avi V. Semi-automated echocardiographic quantification of right ventricular size and function. Int J Cardiovasc Imaging 2015;31:1149-57. http://doi.org/df7c.
- Khamis H, Zurakhov G, Azar V, Raz A, Friedman Z, Adam D. Automatic apical view classification of echocardiograms using a discriminative learning dictionary. Med Image Anal 2017;36:15-21. http://doi.org/f9qn5r
- 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. http://doi.org/gc6x52
- Otani K, Nakazono A, Salgo IS, Lang RM, Takeuchi M. 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;29:955-65. http://doi.org/f874ck
- Tamborini G, Piazzese C, Lang RM, Muratori M, Chiorino E,
- Mapelli M, 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;30:1049-58. http://doi.org/gcj3wz
- de Agustín JA, Marcos-Alberca P, Fernandez-Golfin C, Gonçalves A, Feltes G, Nuñez-Gil IJ, 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;25:815-23. http://doi.org/q2s
- Kagiyama N, Toki M, Hara M, Fukuda S, Aritaka S, Miki T, et al. Efficacy and Accuracy of Novel Automated Mitral Valve Quantification: Three-Dimensional Transesophageal Echocardiographic Study. Echocardiography 2016;33:756-63. http://doi.org/df8r
- Calleja A, Thavendiranathan P, Ionasec RI, Houle H, Liu S, Voigt I, 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. http://doi.org/df8s
- Asch FM, Poilvert N, Abraham T, Jankowski M, Cleve J, Adams M, et al. Automated echo- cardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ Cardiovasc Imaging. 2019;12:e009303. http://doi.org/df8t
- Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol2016;68:2287-95. http://doi.org/f9d23v
- Omar HA, Domingos JS, Patra A, Upton R, Leeson P, Noble JA. 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. http://doi.org/df8v
- Sengupta PP, Huang YM, Bansal M, Ashrafi A, Fisher M, Shameer K, et al. Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy. Circ Cardiovasc Imaging. 2016;9. pii:e004330. http://doi.org/df8w
- Betancur J, Rubeaux M, Fuchs TA, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: anatomic and clinical validation. J Nucl Med 2017;58:961–7. http://doi.org/gbhnpv
- Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, et al. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. JACC Cardiovasc Imaging. 2018;11:1654-63. http://doi.org/df8x
- Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M Jr, Gerlach J, et al. Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population. J Nucl Med 2013;54:221–8. http://doi.org/f4pc6s
- Arsanjani R, Xu Y, Dey D, Fish M, Dorbala S, Hayes S, et al. Improved accuracy of myocardial perfusion SPECT for the detection ofcoronary artery disease using a support vector machine algorithm. J Nucl Med 2013;54:549-55. http://doi.org/f4s9rc
- Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563-77. http:// doi.org/f8rzch
- Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P,Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006. http://doi.org/f59qdv
- Arimura H, Soufi M. A review on radiomics for personalized
- medicine in cancer treatment. Med Imaging Technol 2018;36:81-9.
- Cetim I, Petersen SE, Napel S, Camara O, González Ballester MA, Lekadir K. A radiomics approach to analyze cardiac alterations in hypertension 019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) April 8-11, 2019 http://doi.org/df8z
- Shah SJ. 20th Annual Feigenbaum Lecture: Echocardiography for Precision Medicine-Digital Biopsy to Deconstruct Biology. J Am Soc Echocardiogr 2019; 32:1379-95. http://doi.org/dg2d