Research on the Development and Application of a Preoperative Assessment Model for Transcatheter Mitral Valve Edge-to-Edge Repair Based on Visual Foundation Models

NCT07247890 | Not Yet Recruiting

• 1 other identifier: 2025BJYYEC-KY205-01
• Study Type: observational
• Target: 800
• Locations: 0 countries
• Sites: N/A

Brief Summary

Mitral regurgitation (MR) is the most prevalent valvular heart disease in China. Transcatheter edge-to-edge repair (TEER) is currently the preferred treatment for patients with severe MR who face high surgical risks. However, existing preoperative assessment methods for TEER suffer from numerous limitations, including complex measurement parameters, high technical demands, and significant subjectivity. Vision Mamba, a cutting-edge technology in the visual domain, overcomes the limitations of common computational units in convolutional neural networks and Transformers through bidirectional state space models and positional encoding, demonstrating exceptional performance in visual tasks. To date, no studies have applied Vision Mamba to ultrasound videos for constructing TEER preoperative assessment models. Our team previously established a Transformer-based evaluation model using a small, single-center cohort. This study innovatively introduces a Vision Mamba-based visual foundation model. By integrating multi-faceted, multi-modal ultrasound videos from multiple centers, we develop a one-stop preoperative TEER assessment model for MR patients \[slice identification → video analysis → multi-modal information fusion → preoperative assessment recommendation (suitable/challenging/unsuitable)\]. This model will optimize surgical patient identification and accurately screen patients with contraindications. Furthermore, the one-stop model is fast and objective, significantly improving clinical efficiency. It holds promise for deployment at primary care levels to optimize healthcare resource allocation.

Conditions

Mitral Regurgitation (MR)

Keywords

Mitral regurgitation (MR); Transcatheter Mitral Valve Edge-to-Edge Repair; Vision Mamba; Visual Foundation Model

Outcome Measures

Primary Outcomes (1)

• Echocardiography-related indicators

  Echocardiogram within 2-3 days after admission

Interventions

Model Construction, Validation, and Optimization OTHER

Data from patients in this study will be used for model construction, validation, and optimization.

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population was drawn from Beijing Hospital and Fuwai Hospital of the Chinese Academy of Medical Sciences.

You may qualify if:

• Age: 18-90 years
• Patients with moderate to severe or severe mitral regurgitation as indicated by echocardiography

You may not qualify if:

• Moderate or severe aortic stenosis or aortic regurgitation, or following aortic valve replacement
• Patients with congenital heart disease
• Poor image quality: Insufficient cross-sectional coverage or inability to perform effective mitral valve marking

Sponsors & Collaborators

• Beijing Hospital: lead

MeSH Terms

Conditions

Mitral Valve Insufficiency

Condition Hierarchy (Ancestors)

Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 2 Days
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 18, 2025
• First Posted: November 25, 2025
• Study Start: December 1, 2025
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: November 25, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share