  To decrease patient waiting time for diagnosis of the Coronary Artery
Disease, automatic methods are applied to identify its severity using Coronary
Computed Tomography Angiography scans or extracted Multiplanar Reconstruction
(MPR) images, giving doctors a second-opinion on the priority of each case. The
main disadvantage of previous studies is the lack of large set of data that
could guarantee their reliability. Another limitation is the usage of
handcrafted features requiring manual preprocessing, such as centerline
extraction. We overcome both limitations by applying a different automated
approach based on ShuffleNet V2 network architecture and testing it on the
proposed collected dataset of MPR images, which is bigger than any other used
in this field before. We also omit centerline extraction step and train and
test our model using whole curved MPR images of 708 and 105 patients,
respectively. The model predicts one of three classes: 'no stenosis' for
normal, 'non-significant' - 1-50% of stenosis detected, 'significant' - more
than 50% of stenosis. We demonstrate model's interpretability through
visualization of the most important features selected by the network. For
stenosis score classification, the method shows improved performance comparing
to previous works, achieving 80% accuracy on the patient level. Our code is
publicly available.
