Montreal Heart Institute
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 (C…
Inclusion Criteria: Patients who have reached the age of legal majority under local laws. * For TAVI group: All patients who have had TAVI with a third generation transcatheter heart valve (THV), with an available pre-procedural optimal quality CT scan as defined by an ECG- gating CT with: 1. five to ten image volumes at cardiac phases from 5% to 95% R-R 2. 0.625 mm slice thickness 3. 0.625 mm spacing between slices 4. 0.88 mm in-plane pixel spacing * For TMVI group: Patients who have had a TMVI with a dedicated device and screen failures, with an available optimal quality CT scan.…
Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes