Artificial Intelligence Assisting Transcatheter Mitral Edge-to-Edge Repair

This multicenter, retrospective study develops and validates artificial intelligence (AI)-based semantic segmentation algorithms for intraprocedural transesophageal echocardiography (TEE) during Transcatheter Mitral Edge-to-Edge Repair (TEER). Using pooled imaging data from multiple high-volume structural heart centers, the study aims to automate recognition of mitral leaflets and MitraClip components, measure leaflet insertion length in real time, and display clip position and orientation. Algorithm performance will be benchmarked against expert manual annotations.

NCT Number	NCT07632794
Lead Sponsor	Mi Chen Collaborators: Chinese Academy of Medical Sciences, Fuwai Hospital, San Raffaele University Hospital, Italy, ETH Zurich (Switzerland), Ospedale San Donato
Conditions	Mitral Regurgitation
Enrollment	1,500 participants
Start Date	2025-09-01
Primary Completion	2026-12-31 (estimated)
Study Completion	2030-08-31 (estimated)
Updated on ClinicalTrials.gov	2026-06-08