acePerformance of artificial intelligence-based coronary CT angiography-derived fractional flow reserve across cohorts with different prevalence of obstructive coronary artery disease
Background 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 suspecte…
In a real-world cohort of 202 patients, the AI CT-FFR model identified lesion-specific ischemia more accurately per vessel (63%) than CCTA stenosis thresholds of 50% (52%) or 70% (57%).
The authors state that diagnostic accuracy of the AI model depends on the prevalence of obstructive coronary artery disease and the range of stenosis severities in the vessels assessed, and it performed comparably to CCTA in the study cohort.
Evidence
- Peer-reviewedJournal of Cardiovascular Computed Tomography2026-10-19
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Truvace Impact Record TRV-2026-2078, v1: “Performance of artificial intelligence-based coronary CT angiography-derived fractional flow reserve across cohorts with different prevalence of obstructive coronary artery disease.” Truvace, 2026-10-21. /record/TRV-2026-2078 (accessed at citation time). sha256 6db2b8217520d8d6…
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