This non-interventional study aims to use artificial intelligence to improve the prediction of transcatheter heart valve interventions and optimize patient outcomes. It is based on the analysis of retrospective data from various specialized centers worldwide.
The ENVISAGE study is a non-interventional, retrospective research study designed to validate an artificial intelligence (AI)-based framework for the automated analysis of cardiac imaging data, including multi-slice cardiac computed tomography (CT) and transesophageal echocardiography (TEE). The primary objective is to predict the success of transcatheter heart valve interventions, including aortic, mitral, and tricuspid valve interventions (TAVI, TMVI, M-TEER, T-TEER). The AI framework developed in this study will rely on deep learning algorithms, particularly convolutional neural networks (CNNs) and other advanced models, to automatically segment critical anatomical structures and perform accurate measurements of these structures from CT and TEE images. These measurements will then be combined with pre-interventional clinical data to optimize patient selection and intervention planning, as well as to predict surgical outcomes with high accuracy. AI will also aim to reduce human error and inter-observer variability in the interpretation of cardiac images, which could significantly improve clinical outcomes.
Study Type
OBSERVATIONAL
Enrollment
21,000
Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes
Montefiore Medical Center New York
New York, New York, United States
RECRUITINGMontreal Heart Institute, 5000 Rue Bélanger, Montréal
Montreal, Quebec, Canada
RECRUITINGSt Michael's Hospital Toronto
Toronto, Canada
RECRUITINGSt Paul's Hospital Vancouver
Vancouver, Canada
RECRUITINGCentre Hospitalier Universitaire (CHU) de Bordeaux, 12 rue Dubernat 33404 Talence cedex
Bourdeaux, France
RECRUITINGCHU Lille
Lille, France
RECRUITINGCHU Marseille
Marseille, France
RECRUITINGCentre Cardiologique du Nord Paris
Paris, France
RECRUITINGInstitut Cardiovasculaire Paris-Sud Paris
Paris, France
RECRUITINGCentre Hospitalier Universitaire Rennes
Rennes, France
RECRUITING...and 5 more locations
Accuracy of transcatheter AI predictions
Validation of artificial intelligence algorithms for automatic segmentation of anatomic structures and imaging measurements, and prediction of the success of transcatheter interventions. Output of AI algorithm: * Sizes, types, and number of devices to be implanted * Device success * Percentage risk of permanent pacemaker implantation (for TAVI and TTVI) * Percentage risk of 30-day (para)valvular regurgitation for TAVI, and residual regurgitation for M-TEER and T-TEER * Single leaflet detachment for M-TEER and T-TEER * Left ventricular outflow tract obstruction for TMVI. Key success indicators: * First, independent retrospective validation dataset AI algorithms predict procedural outcome with \>90% accuracy and low inter-reader observer variability when compared to measured procedural outcome. * Second independent retrospective dataset, perform a study to validate AI algorithms with \>90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.
Time frame: Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes.
Performance of AI algorithms in CT and TEE image analysis
Development and evaluation of AI algorithm training platform for data analysis of patients undergoing transcatheter valve procedures. Comparison of AI model performance with existing benchmarks and manual analyses
Time frame: Through study completion, an average of 2 years (retrospective analysis and validation of algorithms).
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