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Updated: Aug 21, 2026
In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
Published on: February 25, 2022
Artificial Intelligence-Driven Angiographic Quantification of Fractional Flow Reserve: Proof-of-Concept Validation
Seung Hun Lee1, Dong Hyun Gim2, Doyeon Hwang3
1Department of Cardiology, Chonnam National University Hospital Chonnam National University Medical School Gwangju Korea.
Background:
Angiography-based fractional flow reserve (FFR) techniques offer wire-free alternatives but often require manual segmentation and 3-dimensional reconstruction. Although an artificial intelligence-driven approach automates these steps, validation remains limited. This study investigated the diagnostic performance of an artificial intelligence-driven angiography-based FFR (medipixel FFR [MPFFR]), compared with quantitative flow ratio (QFR) in predicting functionally significant coronary artery stenosis defined by FFR ≤0.80.
Methods:
A total of 599 vessels from 452 patients who underwent clinically indicated FFR measurement were prospectively enrolled from 5 university hospitals in Korea. MPFFR used automated processes in frame selection, artificial intelligence contour detection, corresponding points matching, 3-dimensional reconstruction, and analytical hemodynamic modeling. The primary end point was diagnostic accuracy for detecting FFR ≤0.80. Secondary end points were target vessel failure (composite of cardiac death, target-vessel myocardial infarction, and target-vessel revascularization) at 2 years.
Results:
Mean analysis time of MPFFR was 12.5±1.7 seconds and manual correction was needed in 32 vessels (5.3%). MPFFR showed similar diagnostic performance with QFR (correlation with FFR; MPFFR versus QFR: R=0.885 versus R=0.860, P for comparison=0.011; area under the curve to predict FFR ≤0.80; 0.949 versus 0.953, P for comparison=0.631). At a median follow-up of 2 years (interquartile range, 1.6-2.6 years), patients with MPFFR ≤0.80 had higher risk of target vessel failure than those with MPFFR >0.80 (4.5% versus 0.8%; adjusted hazard ratio, 5.94 [95% CI, 1.27-27.91]; P=0.024). C-index to predict target vessel failure was comparable between MPFFR and QFR (0.770 versus 0.753, P for comparison=0.469).
Conclusions:
In this multicenter registry, an artificial intelligence-driven angiography-based FFR demonstrated comparable diagnostic accuracy with QFR in identifying functionally significant stenosis, and similar prognostic ability with QFR in terms of target vessel failure at 2 years.
Registration:
Multicenter QFR Registry; Unique Identifier: NCT03791788.