Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence

Abstract

Background: Coronary flow reserve (CFR) has prognostic value in patients with coronary artery dis-ease. However, its measurement is complex, and automatic methods for CFR computation are scarcely available. We developed an automatic method for CFR computation based on coronary angiography and assessed its feasibility. Methods: Coronary angiographies from the Corelab database were annotated by experienced analysts. A convolutional neural network (CNN) model was trained for automatic segmentation of the main coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold cross-validation. Subsequently, the CNN model was implemented into a prototype software package for automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angio-graphies performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and its agreement with the manual computational method based on frame count (CFRmanual). Results: Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was suc-cessfully computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was 18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and CFRmanual, with a mean difference of 0.12 ± 0.53. Conclusions: Automatic computation of the CFR based on coronary angiography is feasible. This method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess microcirculatory function. (Cardiol J 2023; 30, 3: 369–378).