Increased efficiency in evaluating CRT effectiveness based on AI.

 

The introduction of automated ECG interpretation into patient qualification for resynchronization therapy aims to markedly improve the success rate of the treatment. By addressing the prevalent issue of cardiovascular disease, the project seeks to contribute significantly to reducing the impact of heart failure and, in turn, enhance the overall cardiovascular health of the community.

 

Background

Cardiovascular disease is the most common cause of death in adult Poles. Over the past 15 years, it has accounted for approximately 45-50% of deaths. One of the main challenges is heart failure. The solution for some patients may be CRT resynchronization therapy, which involves inserting electrodes into the heart to stimulate both ventricles.

Objective

To create a new, innovative system for the automatic evaluation, analysis and interpretation of electrocardiographic signals to assess the effectiveness of resynchronization in CRT therapy.

Methods

The research team will record patients’ ECG signals. Then, these records will be analyzed by Cardiomatics, and the obtained material will serve as a training base for the artificial intelligence algorithm. Based on these data, artificial intelligence will help doctors make the right decisions when selecting patients for surgery in the future.

Results

Implementation of artificial intelligence (AI) in the appropriate qualification of patients for CRT resynchronization therapy will help increase the number of people for whom CRT therapy will bring the expected results.

 

Coordinating Center: Medical University of Warsaw

Peer-reviewed article: “Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy”

Source: ClinicalTrials.gov

Project co-financed by the European Union through the European Regional Development Fund under the Smart Growth Operational Programme. The project is carried out as part of the National Center for Research and Development: Fast Track: “1/1.1.1/2018 SS Big/MSP/JN 4”.