Machine learning to predict abnormal myocardial perfusion from pre-test features-Reference-Cited by-同舟云学术

Machine learning to predict abnormal myocardial perfusion from pre-test features

Published:2022-06-07 Issue:5 Volume:29 Page:2393-2403
ISSN:1071-3581
Container-title:Journal of Nuclear Cardiology
language:en
Short-container-title:J. Nucl. Cardiol.

Author:

Miller Robert J. H.,Hauser M. Timothy,Sharir Tali,Einstein Andrew J.,Fish Mathews B.,Ruddy Terrence D.,Kaufmann Philipp A.,Sinusas Albert J.,Miller Edward J.,Bateman Timothy M.,Dorbala Sharmila,Di Carli Marcelo,Huang Cathleen,Liang Joanna X.,Han Donghee,Dey Damini,Berman Daniel S.,Slomka Piotr J.

Publisher

Springer Science and Business Media LLC

Subject

Cardiology and Cardiovascular Medicine,Radiology, Nuclear Medicine and imaging

Link

https://link.springer.com/content/pdf/10.1007/s12350-022-03012-6.pdf

Reference43 articles.

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5. Otaki Y, Betancur J, Sharir T, Hu L-H, Gransar H, Liang Joanna X, et al. 5-Year prognostic value of quantitative versus visual MPI in subtle perfusion defects. JACC Cardiovasc Imaging 2020;13:774‐85.

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