Year: 2025
Type: article
Abstract: PURPOSE OF REVIEW: In this paper we review recent advancements in the diagnosis and management of atrial fibrillation through machine learning (ML). RECENT FINDINGS: Machine learning models developed from clinical records, electrocardiograms (ECGs) as well as data from implantable and wearable devices can now detect and even predict new-onset atrial fibrillation. Other models have improved prediction of stroke risk, increased the success of electrical cardioversions and facilitated catheter ablation of AF. Machine learning presents exciting new opportunities to enhance detection and management of atrial fibrillation. However, these developments need to be weighed against considerations of generalizability, equity, and transparency of these models for real-world utilization in clinical practice. We suggest targeted approaches for evaluation and utilization of ML models to allow for informed clinical implementation.
Source: Current Atherosclerosis Reports
Authors: Vahid Yazdi, Vishnu Kadiyala, Sumeet S. Chugh
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,190
Funder: Cedars-Sinai Medical Center
OpenAlex ID: W4416775742