Neural Network-Based Coronary Dominance Classification of RCA Angiograms

Abstract

Coronary arterial dominance classification is essential for SYNTAX score estimation, which is a tool used to determine the complexity of coronary artery disease and guide patient selection toward optimal revascularization strategy. We developed coronary dominance classification algorithm based on the analysis of right coronary artery (RCA) angiograms using neural network.

We employed convolutional neural network ConvNext and Swin transformer for 2D image (frames) classification, along with a majority vote for cardio angiographic view classification. An auxiliary network was also used to detect irrelevant images which were then excluded from the data set.

5-fold cross validation gave the following dominance classification metrics (p = 95%): macro recall = 93.1% ± 4.3%, accuracy = 93.5% ± 3.8%, macro F1 = 89.2% ± 5.6%. The most common case in which the model regularly failed was RCA occlusion, as it requires utilization of left coronary artery (LCA) information.

The use of machine learning approaches to classify coronary dominance based on RCA alone has been shown to be successful with satisfactory accuracy. However, for higher accuracy, it is necessary to utilize LCA information in the case of an occluded RCA and detect cases where there is high uncertainty.