Performance of artificial intelligence-based coronary CT angiography-derived fractional flow reserve across cohorts with different prevalence of obstructive coronary artery disease.

Authors

Casper F Coerkamp, Merel Gommers, Richard A P Takx, Roel S Driessen, Victor A Verpalen, Paul Knaapen, Ivana Išgum, Nick S Nurmohamed, José P S Henriques, Nils R Planken

Published in

Journal of cardiovascular computed tomography. Oct 19, 2026. Epub Oct 19, 2026.

Abstract

Coronary computed tomography angiography (CCTA) provides accurate anatomical assessment of coronary stenosis severity but has limited ability to determine lesion-specific ischemia as assessed by invasive fractional flow reserve (FFR). We aimed to evaluate the diagnostic performance of an artificial intelligence-based deep learning CCTA-derived FFR model (CT-FFR_AI) in cohorts undergoing CCTA and invasive FFR as part of a study protocol and a real-world cohort undergoing invasive FFR based on suspected coronary artery disease on CCTA in routine clinical care.
Diagnostic performance of CT-FFR_AI (CorEx, SPIMED-AI) was evaluated using an intention-to-diagnose approach and compared with CCTA using 50% and 70% stenosis thresholds, with invasive FFR as reference standard. In the PACIFIC-1 study cohort, 208 patients underwent CCTA and invasive FFR assessment of all major coronary vessels irrespective of CCTA findings. The real-world cohort included 202 patients with at least one CCTA-defined lesion ≥70% stenosis who underwent ICA with invasive FFR.
In the PACIFIC-1 cohort (612 vessels), per-vessel diagnostic accuracy was comparable between CT-FFR_AI (77%), when using a CCTA based ≥50% stenosis threshold (76%; P = 0.689), and when using a ≥70% stenosis threshold (74%; P = 0.183). In the real-world cohort (325 vessels), CT-FFR_AI demonstrated higher per-vessel diagnostic accuracy (63%) compared with CCTA using ≥50% and ≥70% stenosis thresholds (52% and 57%, respectively; P < 0.001 and P = 0.034). Net reclassification improvement for CT-FFR_AI beyond CCTA alone using ≥50% and ≥70% stenosis thresholds was 7.9% (P = 0.256) and 17.7% (P = 0.012) in the PACIFIC-1 cohort, compared with 23.2% (P < 0.001) and 13.6% (P = 0.070) in the real-world cohort, respectively.
CT-FFR_AI may outperform anatomic assessment alone, with diagnostic accuracy depending on the prevalence of obstructive coronary artery disease and the range of stenosis severities among vessels undergoing invasive FFR assessment.

PMID:
42897082
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.

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