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.

使用基于冠状动脉的神经网络方法，利用右冠状动脉(RCA)血管造影图像进行心脏支配分类算法的研究并使用机器学习方法成功实现了满意的准确性，然而，在RCA闭塞的情况下，为了获得更高的准确性，有必要利用左冠状动脉(LCA)的信息并检测高度不确定的情况。

基于神经网络的右冠动脉造影图像冠脉主导性分类