Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis. - 2024
Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.
Artificial intelligence; Coronary computed tomography angiography; Deep learning; Fractional flow reserve; Intermediate coronary stenoses; Humans; Male; Female; Retrospective Studies; Aged; Middle Aged; Reproducibility of Results; Proof of Concept Study; Deep Learning; Radiographic Image Interpretation, Computer-Assisted; Coronary Vessels/diagnostic imaging; Coronary Vessels/physiopathology; Software; Models, Cardiovascular; Cardiac Catheterization; Coronary Artery Disease/diagnostic imaging; Coronary Artery Disease/physiopathology; Fractional Flow Reserve, Myocardial; Coronary Stenosis/diagnostic imaging; Coronary Stenosis/physiopathology; Coronary Angiography; Predictive Value of Tests; Computed Tomography Angiography; Severity of Illness Index; Hydrodynamics; Coronary Artery Disease; Coronary Stenosis; Coronary Vessels; Radiology, Nuclear Medicine and Imaging; Cardiology and Cardiovascular Medicine
Abstract :
[en] Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.
Disciplines :
Cardiovascular & respiratory systems
Author, co-author :
Peters, Benjamin; Faculty of Medicine and Life Sciences, Hasselt University, LCRC, Agoralaan, Diepenbeek, 3590, Belgium. Benjamin.peters@jessazh.be ; Department of Radiology, Jessa Hospital, LCRC, Stadsomvaart 11, Hasselt, 3500, Belgium. Benjamin.peters@jessazh.be
Paul, Jean-François; Department of Radiology, Institut Mutualiste Montsouris, 42 Boulevard Jourdan, Paris, France
Symons, Rolf; Department of Radiology, Imelda Hospital, Bonheiden, Belgium
Franssen, Wouter M A; SMRC Sports Medical Research Center, BIOMED Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium
Nchimi Longang, Alain ; Université de Liège - ULiège > Département des sciences cliniques > Cardiologie - Pathologie spéciale et réhabilitation ; Department of Radiology, Centre Hospitalier Universitaire, Luxembourg, Luxembourg, Luxembourg
Ghekiere, Olivier; Faculty of Medicine and Life Sciences, Hasselt University, LCRC, Agoralaan, Diepenbeek, 3590, Belgium ; Department of Radiology, Jessa Hospital, LCRC, Stadsomvaart 11, Hasselt, 3500, Belgium
Language :
English
Title :
Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.
Publication date :
2024
Journal title :
International Journal of Cardiovascular Imaging
ISSN :
1569-5794
eISSN :
1875-8312
Publisher :
Springer Science and Business Media B.V., United States
This research is part of the Limburg Clinical Research Center (LCRC) Uhasselt-ZOL-Jessa, supported by Hasselt University, Jessa Hospital and Ziekenhuis Oost-Limburg.
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