Aortic valve leaflets motion trajectories tracking by using echocardiography data

A new approach is proposed for evaluating the motion trajectories of aortic valve leaflets throughout the cardiac cycle based on echocardiography data, aimed at subse-quently determin-ing their mechanical properties. The non-invasive nature of this method presents a significant ad-vantage over existing experimental techniques for assessing the mechanical properties of soft tis-sues, which often involve invasive procedures and are limited by sample availability and ethical considerations. In this study, a software module was developed to track the movement of control points marked on the aortic valve leaflet, using computer vision algorithms applied to echocardiography video data. The program utilizes a neural network based on the YOLOv8 library for semantic seg-mentation and object detection, enabling precise identi-fication and real-time tracking of the valve leaflets. The software was initially validated on model data to ensure the accuracy and relia-bility of the tracking algorithm. It was then applied to real echocardiography data, suc-cessfully reconstruct-ing the trajectories of the aortic valve leaflets throughout the cardiac cycle. These trajectories can be used in conjunction with mathematical models to deter-mine the mechanical properties of the valve leaflets using optimization methods. The proposed method represents a non-invasive, efficient, and accurate means of assessing the dynamic behavior of aortic valve leaflets, potentially contributing to im-proved diagnosis and treatment planning for patients with aortic valve diseases. Further development of this approach may lead to a deeper understanding of the valve's me-chanical characteristics and enhance surgical outcomes.