Neural network-based coronary dominance classification of RCA angiograms
Ivan Kruzhilov Egor Ikryannikov Artem Shadrin Ruslan Utegenov Galina Zubkova Ivan Bessonov
Background. Cardiac 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.
Objectives. Cardiac dominance classification algorithm based on the analysis of
right coronary artery (RCA) angiograms using neural network Method. 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. Our data set consisted of
828 angiographic studies, 192 of them being patients with left dominance.
Results. 5-fold cross validation gave the following dominance classification
metrics (p=95%): macro recall=93.1%, accuracy=93.5%, macro F1=89.2%. The most
common case in which the model regularly failed was RCA occlusion, as it
requires utilization of LCA information. Another cause for false prediction is
a small diameter combined with poor quality cardio angiographic view. In such
cases, cardiac dominance classification can be complex and may require
discussion among specialists to reach an accurate conclusion. Conclusion. The
use of machine learning approaches to classify cardiac 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.