Machine Learning in Invasive and Noninvasive Coronary Angiography
Author:
Funder
National Heart, Lung, and Blood Institute
Publisher
Springer Science and Business Media LLC
Subject
Cardiology and Cardiovascular Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11883-023-01178-z.pdf
Reference59 articles.
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2. Stone GW, Maehara A, Lansky AJ, de Bruyne B, Cristea E, Mintz GS, et al. A prospective natural-history study of coronary atherosclerosis. N Engl J Med. 2011;364(3):226–35. https://doi.org/10.1056/NEJMoa1002358.
3. Davies JE, Sen S, Dehbi HM, Al-Lamee R, Petraco R, Nijjer SS, et al. Use of the instantaneous wave-free ratio or fractional flow reserve in PCI. N Engl J Med. 2017;376(19):1824–34. https://doi.org/10.1056/NEJMoa1700445.
4. Nørgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol. 2014;63(12):1145–55. https://doi.org/10.1016/j.jacc.2013.11.043.
5. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013;61(22):2233–41. https://doi.org/10.1016/j.jacc.2012.11.083.
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