Towards standardizing mitral transcatheter edge-to-edge repair with deep-learning algorithm: a comprehensive multi-model strategy

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.