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.

翻译：背景：冠状动脉优势型分类对于SYNTAX评分评估至关重要，该评分用于判断冠状动脉疾病的复杂程度并指导最佳血运重建策略的患者选择。目的：提出一种基于右冠状动脉（RCA）造影分析的神经网络冠状动脉优势型分类算法。方法：采用卷积神经网络ConvNext和Swin Transformer对二维图像（帧）进行分类，并通过多数投票法实现冠状动脉造影视角分类。同时使用辅助网络检测无关图像并予以剔除。本数据集包含828项冠状动脉造影研究，其中192例为左优势型患者。结果：五折交叉验证得出以下优势型分类指标（p=95%）：宏平均召回率=93.1%，准确率=93.5%，宏平均F1值=89.2%。模型最常见的预测失败情况为右冠状动脉闭塞，此类病例需结合左冠状动脉（LCA）信息进行分析。另一导致错误预测的原因为冠脉直径较细合并造影视角质量欠佳。在此类情况下，冠状动脉优势型分类具有复杂性，需通过专科医师讨论方能得出准确结论。结论：基于机器学习方法仅通过右冠状动脉造影进行冠状动脉优势型分类已获得满意准确率。但为提升准确率，需在右冠状动脉闭塞时利用左冠状动脉信息，并对存在高度不确定性的病例进行识别。