Related Experiment Video
Updated: Aug 2, 2026
A Simplified Stepwise Approach to Echo Guidance during Percutaneous Mitral Valve Repair
Published on: October 16, 2021
Towards standardizing mitral transcatheter edge-to-edge repair with deep-learning algorithm: a comprehensive
Silvia Corona1, Théo Godefroy2, Olivier Tastet2
1Structural Heart Valve Center, Montreal Heart Institute, Montreal, QC, Canada.
Background:
Severe mitral valve regurgitation requires comprehensive evaluation for optimal treatment. Initial screening uses transthoracic echocardiography (TTE), followed by transesophageal echocardiography (TEE) to determine eligibility for adequate intervention. Mitral Transcatheter Edge-to-Edge Repair (M-TEER) indications are based on detailed and quality valve and sub-valvular apparatus assessment, including anatomy and regurgitation pathophysiology.
Aim:
To develop AI algorithms for standardizing M-TEER eligibility assessment using TTE and TEE echocardiograms, supporting all stages of mitral valve regurgitation evaluation to assist non-expert centers throughout the entire process, from severe mitral valve regurgitation diagnostic to M-TEER procedure.
Methods:
Three deep learning algorithms were developed using echocardiographic data from M-TEER patients performed at Montreal Heart Institute (2018-2025). 1. ECHO-PREP was trained to identify key diagnostic views in TTE (n = 530) and diagnostic and procedural views in TEE (n = 2,222) examinations to determine the level of quality images needed to do a M-TEER. 2. 4D TEE segmentation with automated mitral valve area (MVA) quantification (n = 221), and 3. 2D TEE scallop-level segmentation of leaflets and sub-valvular structures (n = 992).
Results:
Preliminary results on test sets showed 95.7% accuracy in TTE view classification and 91% accuracy for TEE view classification. The 4D segmentation module demonstrated excellent agreement with manual MVA measurements (R = 0.84, p < 0.001), successfully discriminating patients undergoing M-TEER from those referred for surgical replacement (p = 0.046 for AI predictions). The 2D scallop-level analysis achieved a mean Dice score of 0.534 across 11 anatomical structures, with better performance in commonly represented configurations (e.g., A2-P2, P1-A2-P3).
Conclusion:
ECHO-PREP demonstrates the feasibility of an integrated AI-assisted workflow for MR assessment, combining quality control, dynamic 4D valve quantification, and scallop-level anatomy interpretation. These results support the potential of AI to standardize M-TEER eligibility, reduce inter-observer variability, and provide decision support across centers with different levels of expertise.
