  We propose a fully automated algorithm based on a deep learning framework
enabling screening of a coronary computed tomography angiography (CCTA)
examination for confident detection of the presence or absence of coronary
artery atherosclerosis. The system starts with extracting the coronary arteries
and their branches from CCTA datasets and representing them with multi-planar
reformatted volumes; pre-processing and augmentation techniques are then
applied to increase the robustness and generalization ability of the system. A
3-dimensional convolutional neural network (3D-CNN) is utilized to model
pathological changes (e.g., atherosclerotic plaques) in coronary vessels. The
system learns the discriminatory features between vessels with and without
atherosclerosis. The discriminative features at the final convolutional layer
are visualized with a saliency map approach to provide visual clues related to
atherosclerosis likelihood and location. We have evaluated the system on a
reference dataset representing247 patients with atherosclerosis and 246
patients free of atherosclerosis. With five-fold cross-validation,an Accuracy =
90.9%, Positive Predictive Value = 58.8%, Sensitivity = 68.9%, Specificity of
93.6%, and Negative Predictive Value (NPV) = 96.1% are achieved at the
artery/branch level with threshold 0.5. The average area under the receiver
operating characteristic curve is 0.91. The system indicates a high NPV, which
may be potentially useful for assisting interpreting physicians in excluding
coronary atherosclerosis in patients with acute chest pain.
