Artificial intelligence; CT-derived fractional flow reserve; Coronary artery disease; Coronary computed tomography angiography; Deep learning; Humans; Male; Female; Retrospective Studies; Middle Aged; Fractional Flow Reserve, Myocardial; Aged; Reproducibility of Results; Predictive Value of Tests; Sensitivity and Specificity; Deep Learning; Computed Tomography Angiography/methods; Coronary Angiography/methods; Coronary Artery Disease/diagnostic imaging; Myocardial Ischemia/diagnostic imaging; Computed Tomography Angiography; Coronary Angiography; Myocardial Ischemia; Radiology, Nuclear Medicine and Imaging
Abstract :
[en] OBJECTIVES: Fractional flow reserve (FFR) and instantaneous wave-Free Ratio (iFR) pressure measurements during invasive coronary angiography (ICA) are the gold standard for assessing vessel-specific ischemia. Artificial intelligence has emerged to compute FFR based on coronary computed tomography angiography (CCTA) images (CT-FFRAI). We assessed a CT-FFRAI deep learning model for the prediction of vessel-specific ischemia compared to invasive FFR/iFR measurements.
MATERIALS AND METHODS: We retrospectively selected 322 vessels from 275 patients at two centers who underwent CCTA and invasive FFR and/or iFR measurements during ICA within three months. A junior and senior radiologist at each center supervised vessel centerline-building to generate curvilinear reformats that were processed for CT-FFRAI binary outcomes (≤ 0.80 or > 0.80) prediction. Reliability for CT-FFRAI outcomes based on radiologists' supervision was assessed with Cohen's κ. Diagnostic values of CT-FFRAI were calculated using invasive FFR ≤ 0.80 (n = 224) and invasive iFR ≤ 0.89 (n = 238) as the gold standard. A multinomial logistic regression model, including all false-positive and false-negative cases, assessed the impact of patient- and CCTA-related factors on diagnostic values of CT-FFRAI.
RESULTS: Concordance for CT-FFRAI binary outcomes was substantial (κ = 0.725, p < 0.001). Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT-FFRAI in predicting vessel-specific ischemia on a per-vessel analysis, based on senior radiologists' evaluations, were 85% (58/68) and 91% (78/86), 82% (128/156) and 78% (119/152), 67% (58/86) and 70% (78/111), 93% (128/138) and 94% (119/127), and 83% (186/224) and 83% (197/238), respectively. Coronary calcifications significantly reduced the diagnostic accuracy of CT-FFRAI (p < 0.001; OR, 1.002; 95% CI 1.001-1.003).
CONCLUSION: CT-FFRAI demonstrates high diagnostic performance in predicting vessel-specific coronary ischemia compared to invasive FFR and iFR. Coronary calcifications negatively affect specificity, suggesting that further improvements in spatial resolution could enhance accuracy.
KEY POINTS: Question How accurately can a new deep learning model (CT-FFRAI) assess vessel-specific ischemia from CCTA non-invasively compared to two validated pressure measurements during invasive coronary angiography? Findings CT-FFRAI achieved high diagnostic accuracy in predicting vessel-specific ischemia, with high sensitivity and negative predictive value, independent of scanner type and radiologists' experience. Clinical relevance CT-FFRAI provides a non-invasive alternative to Fractional Flow Reserve and instantaneous wave-Free Ratio measurements during invasive coronary angiography for detecting vessel-specific ischemia, potentially reducing the need for invasive procedures, lowering healthcare costs, and improving patient safety.
Disciplines :
Cardiovascular & respiratory systems
Author, co-author :
Peters, Benjamin ; UHasselt, School of Medicine and Life Sciences, Agoralaan, 3590, Diepenbeek, Belgium. Benjamin.peters@jessazh.be ; Department of Radiology, Jessa Ziekenhuis, Stadsomvaart 11, 3500, Hasselt, Belgium. Benjamin.peters@jessazh.be
Symons, Rolf; Department of Radiology, Imelda Hospital, Bonheiden, Belgium
Oulkadi, Sanad; Department of Radiology, Jessa Ziekenhuis, Stadsomvaart 11, 3500, Hasselt, Belgium ; Department of Radiology, University Hospital Antwerpen, Edegem, Belgium
Van Breda, Annelies; Department of Radiology, Imelda Hospital, Bonheiden, Belgium ; Department of Radiology, University Hospital Leuven, Leuven, Belgium
Bataille, Yoann; Department of Cardiology, Jessa Ziekenhuis, Hasselt, Belgium
Kayaert, Peter; Department of Cardiology, Jessa Ziekenhuis, Hasselt, Belgium
Dewilde, Willem; Department of Cardiology, Imelda Hospital, Bonheiden, Belgium
De Wilder, Kenneth; Department of Cardiology, Imelda Hospital, Bonheiden, Belgium
Franssen, Wouter M A; SMRC Sports Medical Research Center, BIOMED Biomedical Research Institute, School 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
Ghekiere, Olivier; UHasselt, School of Medicine and Life Sciences, Agoralaan, 3590, Diepenbeek, Belgium ; Department of Radiology, Jessa Ziekenhuis, Stadsomvaart 11, 3500, Hasselt, Belgium
Language :
English
Title :
Diagnostic performance of a coronary CT angiography-based deep learning model for the prediction of vessel-specific ischemia.
Publication date :
April 2026
Journal title :
European Radiology
ISSN :
0938-7994
eISSN :
1432-1084
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
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