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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

800 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-12-31

Study Completion Date

2027-12-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Mitral Regurgitation (MR)

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Interventions

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Model Construction, Validation, and Optimization

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

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

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

Minimum Eligible Age

18 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No