Related Experiment Video
Updated: Aug 4, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation
Daniel Pipilas1,2,3, Samuel Freesun Friedman2,4, Shaan Khurshid5,6,7
1Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
Purpose Of Review:
Atrial fibrillation (AF) is a major public health problem associated with preventable morbidity. Artificial intelligence (AI) is emerging as potential tool to prioritize individuals at increased risk for AF for preventive interventions. This review summarizes recent advances in the use of AI models to estimate AF risk.
Recent Findings:
Several AI-enabled models have been recently developed which can discriminate AF risk with reasonable accuracy. AI models utilizing the electrocardiogram waveform appear to extract predictive information which is additive beyond traditional clinical risk factors. By identifying individuals at higher risk for AF, AI-based models may improve the efficiency of preventive efforts (e.g., screening, risk factor modification) intended to reduce risk of AF and associated morbidity.
