  Various types of atherosclerotic plaque and varying grades of stenosis could
lead to different management of patients with coronary artery disease.
Therefore, it is crucial to detect and classify the type of coronary artery
plaque, as well as to detect and determine the degree of coronary artery
stenosis. This study includes retrospectively collected clinically obtained
coronary CT angiography (CCTA) scans of 163 patients. To perform automatic
analysis for coronary artery plaque and stenosis classification, a multi-task
recurrent convolutional neural network is applied on multi-planar reformatted
(MPR) images of the coronary arteries. First, a 3D convolutional neural network
is utilized to extract features along the coronary artery. Subsequently, the
extracted features are aggregated by a recurrent neural network that performs
two simultaneous multi-class classification tasks. In the first task, the
network detects and characterizes the type of the coronary artery plaque (no
plaque, non-calcified, mixed, calcified). In the second task, the network
detects and determines the anatomical significance of the coronary artery
stenosis (no stenosis, non-significant i.e. <50% luminal narrowing, significant
i.e. >50% luminal narrowing). For detection and classification of coronary
plaque, the method achieved an accuracy of 0.77. For detection and
classification of stenosis, the method achieved an accuracy of 0.80. The
results demonstrate that automatic detection and classification of coronary
artery plaque and stenosis are feasible. This may enable automated triage of
patients to those without coronary plaque and those with coronary plaque and
stenosis in need for further cardiovascular workup.
