Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks

TL;DR

This paper presents a CNN-based deep learning system that automates the detection and analysis of coronary stenosis in invasive angiography images, achieving real-time performance and outperforming traditional visual assessment methods.

Contribution

The authors developed the first automated machine learning system capable of performing QCA tasks and significantly improving over visual assessment in real-time coronary stenosis analysis.

Findings

Localization accuracy of 72.7%

Dice coefficient of 0.704

C-statistic of 0.825

Abstract

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.