  The determination of a coronary stenosis and its severity in current clinical
workflow is typically accomplished manually via physician visual assessment
(PVA) during invasive coronary angiography. While PVA has shown large
inter-rater variability, the more reliable and accurate alternative of
Quantitative Coronary Angiography (QCA) is challenging to perform in real-time
due to the busy workflow in cardiac catheterization laboratories. We propose a
deep learning approach based on Convolutional Neural Networks (CNN) that
automatically characterizes and analyzes coronary stenoses in real-time by
automating clinical tasks performed during QCA. Our deep learning methods for
localization, segmentation and classification of stenosis in still-frame
invasive coronary angiography (ICA) images of the right coronary artery (RCA)
achieve performance of 72.7% localization accuracy, 0.704 dice coefficient and
0.825 C-statistic in each respective task. Integrated in an end-to-end
approach, our model's performance shows statistically significant improvement
in false discovery rate over the current standard in real-time clinical
stenosis assessment, PVA. To the best of the authors' knowledge, this is the
first time an automated machine learning system has been developed that can
implement tasks performed in QCA, and the first time an automated machine
learning system has demonstrated significant improvement over the current
clinical standard for rapid RCA stenosis analysis.
