Medical imaging analysis via artificial intelligence algorithms
Diagnostic TestDevelopment of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes
NCT Number: NCT07213531
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.
Interested in participating?
Request Info18 year and older
All sexes
Observational
Montreal Heart Institute, 5000 Rue Bélanger, Montréal, Montreal, Quebec, Canada
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Patients who have reached the age of legal majority under local laws.
Exclusion criteria
Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes
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.
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:
Key success indicators:
Time frame: Through study completion, an average of 2 years (retrospective analysis and validation of algorithms).
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: Baseline (pre-procedural) and post-procedural (day 90) analysis
Comparison of AI's ability to standardize patient selection, treatment planning and clinical outcomes with traditional methods. Assessment of accuracy in predicting patient outcomes and reducing procedure-specific complications.
Contact information is provided by the study sponsor or research team.
Thomas Modine, MD, PhD
CONTACT
Walid Ben Ali, MD, PhD
CONTACT
Montreal Heart Institute
Other
Acronym: ENVISAGE
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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