Trial reportJournal of the American Heart Association2026
TEERAI-Pre: A Multiview Artificial Intelligence Model for Preoperative Assessment of Transcatheter Edge-to-Edge Mitral Valve Repair Using Multiview, Multimodal Echocardiography.
Trial report in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05508438 (A Prospective, Multicenter, Objective Performance Criteria Clinical Study to Evaluate the Efficacy and Safety of the Transfemoral Mitral Valve Repair System in the Treatment of Patients With Moderately Severe and Severe Functional Mitral Regurgitation), which is not on this map. Not yet cited in PubMed.
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A Prospective, Multicenter, Objective Performance Criteria Clinical Study to Evaluate the Efficacy and Safety of the Transfemoral Mitral Valve Repair System in the Treatment of Patients With Moderately Severe and Severe Functional Mitral Regurgitation(FMR)
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11 authors.
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Abstract
backgroundTranscatheter edge-to-edge mitral valve repair is a key therapeutic option for patients with severe symptomatic mitral regurgitation at high surgical risk. This prospective study aimed to develop a novel end-to-end deep learning model for preoperative artificial intelligence assessment in transcatheter edge-to-edge mitral valve repair (TEERAI-pre) candidates using multiview, multimodal echocardiography.
methodsTEERAI-pre, a video vision transformer-based classification model, predicts morphological suitability for transcatheter edge-to-edge mitral valve repair from multiview, multimodal echocardiography. A transformer-based feature-level fusion module was designed in TEERAI-pre to integrate multiview, multimodal features for final prediction. An internal data set of 633 patients (7997 transthoracic echocardiographic videos; 766 pulsed-wave Doppler images) was split for 5-fold cross-validation. An external data set of 150 patients (1735 transthoracic echocardiographic videos; 169 pulsed-wave Doppler images) across 2 hospitals evaluated generalizability. Reference standards were provided by 2 experienced valvular cardiologists per international guidelines.
resultsOn the internal data set, TEERAI-pre achieved 75.0% accuracy (95% CI, 71.7%-78.4%) for classifying red (unsuitable), yellow (challenging), and green (ideal) zones, with 77.1% precision, 75.5% recall, and 76.2% F1 score. External validation yielded 73.3% accuracy, 74.0% precision, and 74.0% recall. Multiview multimodal integration improved performance. Binary classification (red versus green) showed TEERAI-pre matched senior experts and outperformed intermediate/junior echocardiologists. Feature-level fusion outperformed output-level fusion and single-view model. Backbone selection and calibration analysis confirmed robust performance.
conclusionsTEERAI-pre demonstrates strong performance in transcatheter edge-to-edge mitral valve repair preoperative assessment using transthoracic echocardiographic videos and images, supporting more accurate patient selection and enhancing clinical workflow efficiency. REGISTRATION: URL: clinicaltrials.gov; Unique Identifier: NCT05508438.
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