摘要Background The computational fluid dynamics (CFD) approach has been frequently applied to compute the fractional flow reserve (FFR) using computed tomography angiography (CTA).This technique is efficient.We developed the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value out of CTA images in five minutes.This study is to evaluate the DEEP-VESSEL-FFR platform using the emerging deep leaming technique to calculate the FFR value from CTA images as an efficient method.Methods A single-center,prospective study was conducted and 63 patients were enrolled for the evaluation of the diagnostic performance of DEEPVESSEL-FFR.Automatic quantification method for the three-dimensional coronary arterial geometry and the deep learning based prediction of FFR were developed to assess the ischemic risk of the stenotic coronary arteries.Diagnostic performance of the DEEPVES-SEL-FFR was assessed by using wire-based FFR as reference standard.The primary evaluation factor was defined by using the area under receiver-operation characteristics curve (AUC) analysis.Results For per-patient level,taking the cut-off value ＜ 0.8 referring to the FFR measurement,DEEPVESSEL-FFR presented higher diagnostic performance in determining ischemia-related lesions with area under the curve of 0.928 compare to CTA stenotic severity 0.664.DEEPVESSEL-FFR correlated with FFR (R =0.686,P ＜ 0.001),with a mean difference of-0.006 ± 0.0091 (P =0.619).The secondary evaluation factors,indicating per vessel accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 87.3％,97.14％,75％,82.93％,and 95.45％,respectively.Conclusion DEEPVESSEL-FFR is a novel method that allows efficient assessment of the functional significance of coronary stenosis.