Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials

In this observational cohort study, the authors aimed to determine the potential impact of machine learning (ML) CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of coronary CT angiography (CCTA) in patients with obstructive coronary artery disease (CAD). It was found that the implementation of on-site CT-FFR may change management and help to improve diagnostic efficiency and effectiveness in patients with obstructive CAD.

Key points

• The availability of on-site CT-FFR in the diagnostic evaluation of patients with obstructive CAD on CCTA would have significantly reduced the number of patients requiring additional testing compared with CCTA alone.
• The implementation of on-site CT-FFR would have changed the initial management strategy significantly in the patients with obstructive CAD on CCTA.
• Restricting ICA to patients with a positive CT-FFR would have significantly reduced the ICA rate in patients with obstructive CAD on CCTA.

Article: Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials

Authors: Fay M. A. Nous, Ricardo P. J. Budde, Marisa M. Lubbers, Yuzo Yamasaki, Isabella Kardys, Tobias A. Bruning, Jurgen M. Akkerhuis, Marcel J. M. Kofflard, Bas Kietselaer, Tjebbe W. Galema & Koen Nieman

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European Radiology