Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score

Objectives The machine learning ischemia risk score (ML-IRS) is a machine learning–based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can pre...

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Veröffentlicht in:	European radiology Jg. 31; H. 3; S. 1227 - 1235
Hauptverfasser:	Kwan, Alan C., McElhinney, Priscilla A., Tamarappoo, Balaji K., Cadet, Sebastien, Hurtado, Cecilia, Miller, Robert J. H., Han, Donghee, Otaki, Yuka, Eisenberg, Evann, Ebinger, Joseph E., Slomka, Piotr J., Cheng, Victor Y., Berman, Daniel S., Dey, Damini
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021 Springer Nature B.V
Schlagworte:	Aged Algorithms Angiography Artificial intelligence Blood vessels Calcification Cardiac Catheterization Computed tomography Computed Tomography Angiography Coronary Angiography Coronary artery Coronary Artery Disease - diagnostic imaging Coronary Artery Disease - surgery Coronary Stenosis - diagnostic imaging Coronary Stenosis - surgery Density Diagnostic Radiology Emergency medical care Emergency medical services Female Fractional Flow Reserve, Myocardial Heart diseases Heart surgery Humans Imaging Internal Medicine Interventional Radiology Ischemia Learning algorithms Machine Learning Male Medical imaging Medicine Medicine & Public Health Middle Aged Neuroradiology Prediction models Predictive Value of Tests Prospective Studies Radiology Reclassification Risk analysis Risk Factors Severity of Illness Index Stenosis Ultrasound Cardiac catheterization Coronary revascularization Coronary CT angiography Artificial intelligence Machine learning
ISSN:	0938-7994, 1432-1084, 1432-1084
Online-Zugang:	Volltext
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