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Artificial Intelligence Assisting Transcatheter Mitral Edge-to-Edge Repair

NCT07632794 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1500

Last updated 2026-06-08

No results posted yet for this study

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.

Conditions

  • Mitral Regurgitation

Sponsors & Collaborators

  • Chinese Academy of Medical Sciences, Fuwai Hospital

    collaborator OTHER

  • San Raffaele University Hospital, Italy

    collaborator OTHER

  • ETH Zurich (Switzerland)

    collaborator OTHER

  • Ospedale San Donato

    collaborator OTHER

  • Mi Chen

    lead NETWORK

Principal Investigators

  • Mi Chen, MD, PhD · HerzZentrum Hirslanden Zürich

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-09-01
Primary Completion
2026-12-31
Completion
2030-08-31

Countries

  • China
  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07632794 on ClinicalTrials.gov