Analysis of vortex ring formation in the heart chamber by instantaneous vortex deviation based on convolutional neural network

The formation of vortex rings during the left ventricle (LV) filling is an optimized mechanism for blood transport, and vorticity important measure a healthy heart LV. There relationship between abnormal diastolic structure impaired LV, hence identification vital understanding underlying physical flow. However, due to lack quantitative methods, defining, computing mapping ventricular vortices has not been rigorously studied previously. In this paper, novel method detection based on convolutional neural network (CNN) created, which enables determination boundary integrates local global flow fields. We have used CNN-based vector (VFM) quantify vorticity. clinical application our methodology subjects uremic patients, we find differences in strength position patients with uremia cardiomyopathy. Our results can accurately indicate role intracardiac flow, provide new insights into within structure.