  Coronary CT angiography (CCTA) has established its role as a non-invasive
modality for the diagnosis of coronary artery disease (CAD). The CAD-Reporting
and Data System (CAD-RADS) has been developed to standardize communication and
aid in decision making based on CCTA findings. The CAD-RADS score is determined
by manual assessment of all coronary vessels and the grading of lesions within
the coronary artery tree.
  We propose a bottom-up approach for fully-automated prediction of this score
using deep-learning operating on a segment-wise representation of the coronary
arteries. The method relies solely on a prior fully-automated centerline
extraction and segment labeling and predicts the segment-wise stenosis degree
and the overall calcification grade as auxiliary tasks in a multi-task learning
setup.
  We evaluate our approach on a data collection consisting of 2,867 patients.
On the task of identifying patients with a CAD-RADS score indicating the need
for further invasive investigation our approach reaches an area under curve
(AUC) of 0.923 and an AUC of 0.914 for determining whether the patient suffers
from CAD. This level of performance enables our approach to be used in a
fully-automated screening setup or to assist diagnostic CCTA reading,
especially due to its neural architecture design -- which allows comprehensive
predictions.
