What question did this study set out to answer?

This research aims to compare the diagnostic performance of AI-QCTIschemia against FFR-CT, using invasive FFR as the standard reference.

February 8, 2026

Diagnostic performance of artificial intelligence-based myocardial ischemia determination compared to FFR-CT across key clinical cohorts: a pooled analysis of four cohorts

Key Points

This research aims to compare the diagnostic performance of AI-QCTIschemia against FFR-CT, using invasive FFR as the standard reference.
Pulled data from four cohort studies with 592 patients and 1575 vessels.
Compared AI-QCTIschemia results to FFR-CT using sensitivity, specificity, and area under the curve (AUC) metrics.
Subgroup analyses were conducted by gender, age, and diabetes status.
AI-QCTIschemia showed higher diagnostic accuracy for vessel-level ischemia in comparison to FFR-CT.
60.1% of vessels exhibited over 50% stenosis, highlighting significant coronary artery disease presence.
The model was effective across various subgroups, including women, older adults, and diabetes patients.

Abstract

Abstract Introduction Artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCTIschemia), is a non-invasive tool to evaluate myocardial ischemia associated with coronary artery disease on a per vessel basis. Prior individual cohorts have demonstrated a strong diagnostic performance of AI-QCTIschemi. as compared to invasive fractional flow reserve (FFR). Purpose To evaluate the diagnostic performance of AI-QCTIschemia versus fractional flow reserve computed tomography (FFR-CT), using invasive FFR as the reference standard. Patient-level data from four cohort studies were pooled to enable assessment of clinically important subgroups: men, women, patients ≤60 or 60 years, and those with or without diabetes. Methods This analysis pooled patient and vessel level data from 592 patients and 1575 vessels from four cohorts undergoing CCTA with comparison of AI-QCTIschemia and FFR-CT. FFR-CT and invasive FFR 0.8 were considered positive for ischemia. Sensitivity, specificity, and AUC were compared between subgroups using Generalized Estimating Equations models and DeLong’s test. Uninterpretable test results were considered positive. Results Mean age was 62 years, and 32% were females. A total of 356 (60.1%) of vessels had 50% stenosis, with 230 (38.9%) involving the proximal LAD. At the vessel level, 494 (28.6%) were characterized as mild stenosis (25-49%), 270 (16%) moderate (50-69%), and 173 (10%) severe (70-99%). These results were then subclassified based on gender, age (60 or ≤60), and those with or without diabetes. Across all three key clinical cohorts, AI-QCTIschemia demonstrated higher diagnostic accuracy for detection of vessel level ischemia. Conclusion In this pooled analysis, we demonstrated that AI-QCTIschemia a model which emphasizes vessel characterization of coronary artery disease to predict myocardial ischemia showed higher diagnostic accuracy than a computational fluid dynamics-based prediction model to predict vessel level ischemia across clinically relevant subgroups: women, older adults, and diabetics.

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