In this paper, we developed a new approach to solve the problem of infective endocarditis (IE) diagnostics based on intelligent analysis of patients’ echocardiography images. The approach is based on echocardiography segmentation results and detection of valvular anomalies (namely vegetations). In this article for the first time investigates CNNs and Visual Transformers (ViT) based segmentation methods within the framework of the vegetation segmentation task on echocardiography images. Additionally, ensemble methods for combining segmentation models using a new method of models competition for data points were proposed. Furthermore, we investigated methods for aggregating the results of the ensemble based on a new meta-model, pointwise weighted aggregation, which weighs the results of each model pixel by pixel. The last proposed step was to automatically calculate the volume of segmented vegetation to determine the degree of disease and the need for urgent surgical intervention. For the studied and proposed methods, the following ensemble segmentation accuracy was achieved on the test dataset: iou 0.7822, dice score 0.886. The proposed empirical algorithm for calculating the volume of vegetations provided the basis for further improvements of the studied approach. The results obtained indicate the great potential of the developed approaches in clinical practice.