X-ray coronary angiography is the gold standard imaging modality for the assessment
of coronary artery disease (CAD). The SYNTAX score is a recommended instrument for
therapy decision-making and predicts the postprocedural risk associated with the two
revascularization strategies: percutaneous coronary intervention (PCI) and coronary
artery bypass graft (CABG). The score requires expert assessment and manual measurements
of coronary angiograms for stenosis characterization. In this work we propose a deep
learning workflow for automated stenosis detection to facilitate the calculation of
the SYNTAX score. We use a region-based convolutional neural network for object detection,
fine-tuned on a public dataset consisting of angiography frames with annotated stenotic
regions. The model is evaluated on angiographic video sequences of complex CAD patients
from the German Heart Center of the Charite University Hospital (DHZC), Berlin. We
provide a customized graphical tool for cardiac experts that allows correction and
segment annotation of the detected stenotic regions. The model reached a precision
of 78.39% in the frame-wise object detection task on the clinical dataset. For the
task of predicting the presence of coronary stenoses at the patient level, the model
achieved a sensitivity of 49.55% for stenoses of all degrees and 59.18% for stenoses
of relevant degrees (>75%). The results suggest that our stenosis detection tool can
facilitate visual assessment of CAD in angiography data and encourage to investigate
further development towards fully automated calculation of the SYNTAX score.
