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In patients with obstructive coronary artery disease, the functionalsignificance of a coronary artery stenosis needs to be determined to guidetreatment. This is typically established through fractional flow reserve (FFR)measurement, performed during invasive coronary angiography (ICA). We present amethod for automatic and non-invasive detection of patients requiring ICA,employing deep unsupervised analysis of complete coronary arteries in cardiacCT angiography (CCTA) images. We retrospectively collected CCTA scans of 187patients, 137 of them underwent invasive FFR measurement in 192 differentcoronary arteries. These FFR measurements served as a reference standard forthe functional significance of the coronary stenosis. The centerlines of thecoronary arteries were extracted and used to reconstruct straightenedmulti-planar reformatted (MPR) volumes. To automatically identify arteries withfunctionally significant stenosis that require ICA, each MPR volume was encodedinto a fixed number of encodings using two disjoint 3D and 1D convolutionalautoencoders performing spatial and sequential encodings, respectively.Thereafter, these encodings were employed to classify arteries using a supportvector machine classifier. The detection of coronary arteries requiringinvasive evaluation, evaluated using repeated cross-validation experiments,resulted in an area under the receiver operating characteristic curve of $0.81\pm 0.02$ on the artery-level, and $0.87 \pm 0.02$ on the patient-level. Theresults demonstrate the feasibility of automatic non-invasive detection ofpatients that require ICA and possibly subsequent coronary artery intervention.This could potentially reduce the number of patients that unnecessarily undergoICA.

Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography