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

Miller, R. J. H., Hauser, M. T., 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., Huang, C., Liang, J. X., Han, D., Dey, D., Berman, D. S., & Slomka, P. J. (2022). Machine learning to predict abnormal myocardial perfusion from pre-test features. Journal of Nuclear Cardiology, 29(5), 2393–2403. https://doi.org/10.1007/s12350-022-03012-6
Authors:
Robert J. H. Miller
M. Timothy Hauser
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
Cathleen Huang
Joanna X. Liang
Donghee Han
Damini Dey
Daniel S. Berman
Piotr J. Slomka
Affiliated Authors:
Andrew J. Einstein
Subjects:
Author Keywords:
myocardial perfusion imaging
artificial intelligence
machine learning
cad
pet
spect
image analysis
Publication Type:
Article
Unique ID:
10.1007/s12350-022-03012-6
PMID:
Publication Date:
Data Source:
Scopus

Record Created: