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Coronary CT angiography–derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia
Philipp L. von Knebel Doeberitz ,
Carlo N. De Cecco ,
U. Joseph Schoepf ,
Taylor M. Duguay ,
Moritz H. Albrecht ,
Marly van Assen ,
Maximilian J. Bauer ,
Rock H. Savage ,
J. Trent Pannell ,
Domenico De Santis ,
Addison A. Johnson ,
Akos Varga-Szemes ,
Richard R. Bayer ,
Stefan O. Schönberg ,
John W. Nance ,
Christian Tesche
December 6 2018
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Abstract
<p class="first" id="d3038338e261">We sought to investigate the diagnostic performance
of coronary CT angiography (cCTA)-derived
plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR)
to identify lesion-specific ischemia using invasive FFR as the reference standard.
</p>