Artificial Intelligence Semantic Segmentation Technology Assisting Transcatheter Mitral Edgeto-Edge Repair

Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device is an established minimally invasive treatment for patients with severe mitral regurgitation who are at high surgical risk. The success of TEER relies heavily on real-time transesophageal echocardiography (TEE) to guide precise clip positioning and leaflet capture. However, intraoperative image interpretation remains highly dependent on operator experience, and variability in image quality, patient anatomy, and the dynamic nature of cardiac structures continue to challenge procedural standardization across centers.

This multicenter, retrospective imaging study evaluates whether artificial intelligence (AI)-based semantic segmentation can automate the recognition of mitral valve anatomy and MitraClip device components on intraprocedural TEE images. Previously acquired TEE imaging from adult patients who underwent TEER at multiple participating high-volume structural heart centers will be pooled and analyzed. All data are derived from routine clinical care, and only patients with appropriate consent for research use of their clinical and imaging data are included.

The study has three objectives: (1) to develop deep learning models that automatically segment the anterior and posterior mitral leaflets and the MitraClip grippers and arms; (2) to automate real-time measurement of leaflet insertion length during the grasping process; and (3) to integrate three-dimensional imaging with intelligent tracking to display clip position and orientation. By drawing on a multicenter dataset, the study aims to improve the generalizability and robustness of the resulting models across diverse imaging environments, operator practices, and patient anatomies. Algorithm performance will be benchmarked against expert manual annotations using established image segmentation metrics.