Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus with suspected coronary artery disease. The study population included 75 diabetic and 276 nondiabetic patients who were enrolled in the MACHINE consortium. The overall diagnostic performance of coronary CT angiography alone and in combination with CT-FFR were analyzed with direct invasive FFR comparison in 110 coronary vessels of the diabetic group and in 415 coronary vessels of the nondiabetic group. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area under the receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and in nondiabetic patients 75% (p = 0.088), showing improvement over the diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p = 0.223), respectively. In addition, the diagnostic accuracy of CT-FFR was similar between diabetic and nondiabetic patients per stratified CT-FFR group (CT-FFR amp;lt; 0.6, 0.6 to 0.69, 0.7 to 0.79, 0.8 to 0.89, amp;gt;= 0.9). The area under the curves for diabetic and nondiabetic patients were also comparable, 0.88 and 0.82 (p = 0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT angiography and accurately discriminated lesion-specific ischemia in both diabetic and nondiabetic patients suspected of coronary artery disease. (C) 2018 Elsevier Inc. All rights reserved.