Atrial Fibrillation

AI can now identify atrial fibrillation through sinus rhythm

• Jeroen M L Hendriks
• Larissa Fabritz

Published:August 01, 2019DOI:https://doi.org/10.1016/S0140-6736(19)31719-2

Atrial fibrillation is a substantial health-care challenge and is considered to be a global pandemic, as prevalence rates have increased greatly 

1 

and atrial fibrillation-related hospitalisations outnumber those of major cardiac conditions such as heart failure and myocardial infarction. 

2 

Atrial fibrillation confers an increased risk of stroke and mortality; it therefore needs to be detected not only to manage the arrhythmia but also to prevent comorbidities and death. 

3 

A 10-second, 12-lead electrocardiograph (ECG) in current clinical practice is unlikely to reveal possible atrial fibrillation if not present in this short monitoring time. Silent or undetected atrial fibrillation is common and the few screening methods available are demanding in terms of time and resources. Continuous monitoring by means of loop recorders is often indicated, particularly in case of embolic stroke of undetermined source (ESUS). 

4 

Novel and user-friendly wearables to identify arrhythmias have emerged with recent digital advances: wearable ECG technology using automated photoplethysmography algorithms have shown feasible and accurate cardiac rhythm detection and can aid in monitoring the dynamic burden of time spent in atrial fibrillation, 

5 

while mobile atrial fibrillation applications are available for patients and health-care professionals for education and guidance in management. 

6