Network-Based Approach for Modeling and Analyzing Coronary Angiography

TL;DR

This paper introduces a novel network-based method to model and analyze coronary angiograms, aiming to improve automation and reduce observer variability in interpreting these images.

Contribution

It proposes modeling the cardiovascular system as a complex network derived from angiography images, enabling comprehensive analysis beyond traditional visual inspection methods.

Findings

Network analysis reveals differences between healthy and diseased arteries.

Graph structure assessments can assist in automated diagnosis.

Modeling as a complex network offers new insights into coronary health.

Abstract

Significant intra-observer and inter-observer variability in the interpretation of coronary angiograms are reported. This variability is in part due to the common practices that rely on performing visual inspections by specialists (e.g., the thickness of coronaries). Quantitative Coronary Angiography (QCA) approaches are emerging to minimize observer's error and furthermore perform predictions and analysis on angiography images. However, QCA approaches suffer from the same problem as they mainly rely on performing visual inspections by utilizing image processing techniques. In this work, we propose an approach to model and analyze the entire cardiovascular tree as a complex network derived from coronary angiography images. This approach enables to analyze the graph structure of coronary arteries. We conduct the assessments of network integration, degree distribution, and controllability on a healthy and a diseased coronary angiogram. Through our discussion and assessments, we propose modeling the cardiovascular system as a complex network is an essential phase to fully automate the interpretation of coronary angiographic images. We show how network science can provide a new perspective to look at coronary angiograms.