Open AccessMachine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry
Author(s) -
Lien-Hsin Hu,
Julián Betancur,
Tali Sharir,
Andrew J. Einstein,
Sabahat Bokhari,
Mathews B. Fish,
Terrence D. Ruddy,
Philipp A. Kaufmann,
Albert J. Sinusas,
Edward J. Miller,
Timothy M. Bateman,
Sharmila Dorbala,
Marcelo Di Carli,
Guido Germano,
Frédéric Commandeur,
Jiaming Liang,
Yuka Otaki,
Balaji Tamarappoo,
Damini Dey,
Daniel S. Berman,
Piotr J. Slomka
Publication year - 2019
Publication title -
european heart journal. cardiovascular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.576
H-Index - 92
eISSN - 2047-2412
pISSN - 2047-2404
DOI - 10.1093/ehjci/jez177
Subject(s) - myocardial perfusion imaging , medicine , receiver operating characteristic , coronary artery disease , nuclear medicine , revascularization , area under the curve , perfusion , single photon emission computed tomography , confidence interval , coronary angiography , cardiology , radiology , myocardial infarction
To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting.