Research on the Development and Application of a Preoperative Assessment Model for Transcatheter Mitral Valve Edge-to-Edge Repair Based on Visual Foundation Models
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Model Construction, Validation, and Optimization.
- Who it may be relevant to
- Registry conditions: Mitral Regurgitation (MR). Basic parameters: 18 years — 90 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Other Model Construction, Validation, and Optimization
Data from patients in this study will be used for model construction, validation, and optimization.
Primary outcome measures
- Echocardiography-related indicators [Time frame: Echocardiogram within 2-3 days after admission]
Eligibility criteria
Inclusion criteria
- Age: 18-90 years
- Patients with moderate to severe or severe mitral regurgitation as indicated by echocardiography
Exclusion criteria
- 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Other
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07247890 · 2025BJYYEC-KY205-01