Neural network-based coronary dominance classification of RCA angiograms

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

Numerical Simulation of Flare Burning of Coal of a Micro-Mill in a Steam-Oil Burner

Статья посвящена численному моделированию факельного сжигания угольного топлива в паромасляной горелке. Представлены результаты моделирования паромасляной горелки, разработанной в ИТ СО РАН, и новой факельной горелки, работающей на дизельном топливе с частичной заменой его на уголь микропомола. Сравнение результатов расчета с данными эксперимента показало их хорошее соответствие, как по уровню температуры, так и по концентрации газовых компонент продуктов горения. Результаты моделирования показали, что новая горелка устойчиво работает в широком диапазоне расхода угольной пыли. Полученные результаты численного исследования можно использовать при проектировании нового горелочного устройства, подобрав оптимальные режимы работы, для снижения на выходе концентрации вредных выбросов и недожега топливаThe article is devoted to numerical modeling of coal fuel flaring in a steam-oil burner. The results of modeling an oil-steam burner developed at IT SB RAS and a new torch burner operating on diesel fuel with partial replacement of it with micro-milled coal are presented. A comparison of the calculation results with the experimental data showed their good agreement both in terms of the temperature level and the concentration of the gas components of the combustion products. The simulation results showed that the new burner operates stably in a wide range of pulverized coal flow rates. The obtained results of a numerical study can be used in the design of a new burner device, choosing the optimal operating modes to reduce the concentration of harmful emissions and fuel underburning at the outpu