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Related Experiment Video

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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.

Frontiers in Network Physiology
|December 11, 2025
PubMed
Summary

AI algorithms can standardize assessment for Mitral Transcatheter Edge-to-Edge Repair (M-TEER) using echocardiograms. This technology aids in evaluating severe mitral valve regurgitation, improving consistency across medical centers.

Keywords:
4DAIM-TEERTEEdeep-learningmitral regurgitationsegmentation

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Severe mitral valve regurgitation (MR) necessitates thorough evaluation for effective treatment.
  • Transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) are crucial for assessing MR severity and guiding intervention eligibility.
  • Mitral Transcatheter Edge-to-Edge Repair (M-TEER) indications rely on detailed analysis of valve and sub-valvular structures.

Purpose of the Study:

  • To develop and validate artificial intelligence (AI) algorithms for standardizing M-TEER eligibility assessment using TTE and TEE.
  • To support all diagnostic stages of MR evaluation, from initial diagnosis to M-TEER procedure planning.
  • To assist centers with varying levels of expertise in managing severe MR.

Main Methods:

  • Development of three deep learning algorithms using echocardiographic data from M-TEER patients.
  • ECHO-PREP: Trained to classify diagnostic image quality in TTE and TEE examinations.
  • 4D TEE segmentation for automated mitral valve area (MVA) quantification and 2D TEE scallop-level segmentation of mitral valve structures.

Main Results:

  • High accuracy achieved in TTE (95.7%) and TEE (91%) view classification by ECHO-PREP.
  • 4D segmentation showed excellent agreement with manual MVA measurements (R = 0.84) and differentiated M-TEER candidates (p = 0.046).
  • 2D scallop-level analysis demonstrated feasibility with a mean Dice score of 0.534 across 11 anatomical structures.

Conclusions:

  • ECHO-PREP offers a feasible AI-assisted workflow for MR assessment, integrating quality control, quantification, and anatomical interpretation.
  • AI has the potential to standardize M-TEER eligibility assessment and reduce inter-observer variability.
  • The developed AI tools can provide valuable decision support for clinicians managing severe MR, regardless of center expertise.