<p>Over the past decade, coronary computed tomographic angiography (CCTA) has been the most robust non-invasive method for evaluating significant coronary stenosis. Thanks to new technologies, it is now possible to determine the fractional flow reserve (FFR) non-invasively using computed tomographic (CT) images. The aim of this work was to evaluate the incremental diagnostic value of CT-derived FFR for ischemia detection. In this retrospective monocentric study, we investigated 421 patients who underwent CCTA and subsequent ischemia testing between 04/2009 and 06/2020. Endpoint was ischemia on a coronary vessel level assessed by CMR (<i>n</i> = 20), SPECT (<i>n</i> = 225), invasive angiography (stenosis ≥ 90%; <i>n</i> = 80) or invasive FFR (positive if ≤ 0.8; <i>n</i> = 96). CT-FFR was derived from CCTA images by a machine learning (ML) based software prototype. Patients averaged 66.5 [58.2–73.6] years of age and 72.7% (<i>n</i> = 306) were male. Overall, 52.5% (<i>n</i> = 221) had hypertension and 67.9% (<i>n</i> = 286) had hypercholesteremia. Logistic regression analysis on a per vessel base showed that the diagnostic model with CT-FFR plus CCTA had significantly better-fit criteria than the diagnostic model with CCTA alone (log-likelihood χ² 230.21 vs. 192.17; p for difference &lt; 0.001). In particular, the area under curve (AUC) by receiver operating characteristics curve (ROC) analysis for CT-FFR plus CCTA (0.87) demonstrated greater discrimination of hemodynamic ischemia compared to CCTA alone (0.83; p for difference &lt; 0.0001). Combined CCTA and CT-FFR have improved diagnostic accuracy compared to CCTA alone in detecting ischemia on the coronary vessel level and thus could reduce the use of invasive coronary angiography in the future.</p>