  The standard non-invasive imaging technique used to assess the severity and
extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography
Angiography (CCTA). However, manual grading of each patient's CCTA according to
the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and
operator-dependent, especially in borderline cases. This work proposes a fully
automated, and visually explainable, deep learning pipeline to be used as a
decision support system for the CAD screening procedure. The pipeline performs
two classification tasks: firstly, identifying patients who require further
clinical investigations and secondly, classifying patients into subgroups based
on the degree of stenosis, according to commonly used CAD-RADS thresholds. The
pipeline pre-processes multiplanar projections of the coronary arteries,
extracted from the original CCTAs, and classifies them using a fine-tuned
Multi-Axis Vision Transformer architecture. With the aim of emulating the
current clinical practice, the model is trained to assign a per-patient score
by stacking the bi-dimensional longitudinal cross-sections of the three main
coronary arteries along channel dimension. Furthermore, it generates visually
interpretable maps to assess the reliability of the predictions. When run on a
database of 1873 three-channel images of 253 patients collected at the Monzino
Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for
the two classification tasks, respectively. According to our knowledge, this is
the first model trained to assign CAD-RADS scores learning solely from patient
scores and not requiring finer imaging annotation steps that are not part of
the clinical routine.
