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Artificial Intelligence Assisting Transcatheter Mitral Edge-to-Edge Repair
Sponsor: Mi Chen
Summary
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.
Official title: Artificial Intelligence Semantic Segmentation Technology Assisting Transcatheter Mitral Edgeto-Edge Repair
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
1500
Start Date
2025-09-01
Completion Date
2030-08-31
Last Updated
2026-06-08
Healthy Volunteers
No
Conditions
Locations (3)
Fuwai Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, China
IRCCS Policlinico San Donato
Milan, Italy
San Raffaele Hospital
Milan, Italy