AI Study Using Heart Imaging to Predict Clots in Atrial Fibrillation Patients
Collects heart imaging data from adults with atrial fibrillation to build an AI prediction tool.
Plain-language title generated from the registry record
Registry title: Multimodal Cardiac Imaging Registry in Patients with Atrial Fibrillation
Key facts
- Where
- Brno, CzechiaSee the site
Where in Czechia
- St. Anne HospitalBrno, CzechiaNot yet recruiting
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- University in Zielona Góra
- Trial code
- NCT06584266
- Record updated
- 4 September 2024
- Original description
The goal of this observational registry is to collect a curated dataset of multimodal imaging data that will serve for development of artificial-intelligence based solutions for prediction of risk and outcomes in patients with atrial fribrillation.Read full original descriptionShow less
Multimodal, Multicentre Registry of Clinical and Imaging Data to Develop Predictive Models Based on Artificial Intelligence to Support the Diagnostic and Therapeutic Process for Patients with Atrial Fibrillation Undergoing Catheter Ablation and Cardioversion.
The goal of this observational registry is to collect a curated dataset of multimodal imaging data that will serve for development of artificial-intelligence based solutions for prediction of risk and outcomes in patients with atrial fribrillation.
Type of study: observational study
Study Participants: Patients with atrial fibrillation or atrial flutter who undergo clinically indicated transesophageal echocardiography before catheter ablation or cardioversion.
We hypothesize, that automatic analysis of video images of transthoracic echocardiography with deep learning combined with clinical data can predict the presence of left atrial appendage thrombus (LAT). Therefore, our main aim is to create and validate an artificial intelligence model to predict the presence of LAT based on automatic analysis of transthoracic echocardiography with artificial intelligence.
Inclusion: All patients with AF or AFl in whom TEE will be performed (to assess their eligibility for cardioversion or ablation), hospitalized in a participating center during study period (all consecutive patients).
Exclusion: Age\<18, lack of informed, written consent to the TEE.