Study of AI-Based Analysis to Improve Outcomes in Transcatheter Heart Valve Procedures Including TAVI, TMVI, TTVI, M-TEER, and T-TEER

What this Trial Is About

Researchers are evaluating an artificial intelligence AI system designed to improve the prediction of outcomes for patients undergoing transcatheter heart valve interventions for conditions affecting the aortic, mitral, and tricuspid valves. This non-interventional, retrospective study analyzes data from multiple specialized centers worldwide to validate AI algorithms that automatically analyze cardiac imaging and clinical data. The goal is to enhance patient selection, intervention planning, and outcome predictions while reducing human error and variability in image interpretation. The study collects medical imaging data, including multi-slice cardiac computed tomography CT and transesophageal echocardiography TEE, along with preoperative clinical information from patients who have undergone various heart valve procedures. These include transcatheter aortic valve implantation TAVI, transcatheter mitral valve implantation TMVI, transcatheter tricuspid valve intervention TTVI, and edge-to-edge repair procedures for the mitral M-TEER and tricuspid valves T-TEER using specific device generations. The AI framework applies deep learning, including convolutional neural networks, to segment anatomical structures and measure them accurately from images. Participants data are analyzed retrospectively to compare AI-generated automated measurements with manual assessments and to evaluate the accuracy of AI predictions against actual patient outcomes at 30 days post-procedure. The study also monitors the performance of AI algorithms throughout an average of two years of retrospective data collection. This process aims to confirm the AIs ability to support clinical decision-making and improve intervention results in heart valve disease patients.

Enhanced Valves Interventions and Safe AI Generated End Results