Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Singh, A., Miller, R. J. H., Otaki, Y., Kavanagh, P., Hauser, M. T., Tzolos, E., Kwiecinski, J., Van Kriekinge, S., Wei, C.-C., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., … Slomka, P. J. (2023). Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC: Cardiovascular Imaging, 16(2), 209–220. https://doi.org/10.1016/j.jcmg.2022.07.017
Authors:
Ananya Singh
Robert J H Miller
Yuka Otaki
Paul Kavanagh
Michael T Hauser
Evangelos Tzolos
Jacek Kwiecinski
Serge Van Kriekinge
Chih-Chun Wei
Tali Sharir
Andrew J Einstein
Mathews B Fish
Terrence D Ruddy
Philipp A Kaufmann
Albert J Sinusas
Edward J Miller
Timothy M Bateman
Sharmila Dorbala
Marcelo Di Carli
Joanna X Liang
Cathleen Huang
Donghee Han
Damini Dey
Daniel S Berman
Piotr J Slomka
Affiliated Authors:
Andrew J Einstein
Author Keywords:
artificial intelligence
deep learning
myocardial perfusion imaging
prognosis
risk prediction
Publication Type:
Article
Unique ID:
10.1016/j.jcmg.2022.07.017
PMID:
Publication Date:
Data Source:
PubMed

Record Created: