Artificial Intelligence Assisting Transcatheter Mitral Edge-to-Edge Repair

NCT07632794 | Active Not Recruiting

• 1 other identifier: 2025-00443
• Study Type: observational
• Target: 1,500
• Locations: 2 countries
• Sites: 3

Brief 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

Keywords

mitral regurgitation; transcatheter edge-to-edge repair; artificial intelligence; semantic segmentation

Outcome Measures

Primary Outcomes (2)

• Accuracy of AI-based semantic segmentation of mitral valve leaflets and MitraClip device components

  The accuracy of the deep learning model in segmenting the anterior and posterior mitral leaflets, MitraClip grippers, and clip arms on intraprocedural transesophageal echocardiography (TEE) images. Performance is benchmarked against manual annotations provided by experienced echocardiographers and quantified using the Dice similarity coefficient, sensitivity, and specificity. Target performance: ≥ 90%.

  Intraprocedural (TEE images acquired during the TEER procedure)

• Accuracy of automated real-time recognition of mitral leaflet insertion length

  The accuracy of the automated measurement system in recognizing the insertion length of the anterior and posterior mitral leaflets in two-dimensional TEE planes during the leaflet grasping process. Algorithm output is compared with manual measurements performed by experienced echocardiographers. Target performance: ≥ 95%.

  Intraprocedural (TEE images acquired during the TEER procedure)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of adult patients with degenerative or functional mitral regurgitation who underwent Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device at multiple participating high-volume structural heart centers. All data are derived from routine clinical care, including intraprocedural transesophageal echocardiographic (TEE) imaging and corresponding clinical records. Only patients with appropriate consent for research use of their data, as defined by the policy of each participating center, are included. The cohort reflects real-world TEER practice across diverse imaging environments, operator experience, and patient anatomies, supporting the development and validation of artificial intelligence-based segmentation and measurement tools with improved generalizability.

You may qualify if:

• Adult patients (≥18 years of age) at the time of the index procedure
• Confirmed diagnosis of degenerative or functional mitral regurgitation
• Underwent Transcatheter Mitral Edge-to-Edge Repair (TEER) with the MitraClip device at one of the participating centers
• Intraprocedural transesophageal echocardiographic (TEE) imaging available, complete, and of sufficient quality to support semantic segmentation and real-time measurement analyses
• Appropriate consent for research use of clinical and imaging data, as per the policy of each participating center

You may not qualify if:

• Incomplete or poor-quality intraprocedural TEE imaging unsuitable for accurate segmentation and measurement
• Ambiguous or unconfirmed diagnosis of mitral regurgitation
• Documented refusal to allow use of clinical or imaging data for research purposes
• Missing essential clinical documentation required to confirm eligibility

Sponsors & Collaborators

• Mi Chen: lead
• Chinese Academy of Medical Sciences, Fuwai Hospital: collaborator
• San Raffaele University Hospital, Italy: collaborator
• ETH Zurich (Switzerland): collaborator
• Ospedale San Donato: collaborator

Study Sites (3)

Fuwai Hospital, Chinese Academy of Medical Sciences

Beijing, Beijing Municipality, 100037, China

Location

IRCCS Policlinico San Donato

Milan, Italy

Location

San Raffaele Hospital

Milan, Italy

Location

MeSH Terms

Conditions

Mitral Valve Insufficiency

Condition Hierarchy (Ancestors)

Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases

Study Officials

• Mi Chen, MD, PhD

  HerzZentrum Hirslanden Zürich

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: NETWORK
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Dr.

Study Record Dates

• First Submitted: May 26, 2026
• First Posted: June 8, 2026
• Study Start: September 1, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): August 31, 2030
• Last Updated: June 8, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1); IT (2)