Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium.
Journal:
Circulation. Cardiovascular imaging
Published Date:
Jun 1, 2018
Abstract
BACKGROUND: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease.
Authors
Keywords
Aged
Asia
Computed Tomography Angiography
Coronary Angiography
Coronary Artery Disease
Coronary Stenosis
Coronary Vessels
Deep Learning
Europe
Female
Fractional Flow Reserve, Myocardial
Humans
Male
Middle Aged
Predictive Value of Tests
Prospective Studies
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Retrospective Studies
Severity of Illness Index
United States