Research in context
Evidence before this study
There is a robust literature on screening for atrial fibrillation in the general population; we did not do a formal systematic review. However, most efforts have focused on either one-time screening with a single electrocardiograph (ECG) or the use of various implantable or wearable monitors to capture infrequent atrial fibrillation episodes over time. Some studies have evaluated discrete ECG features—often P wave characteristics—as predictors of atrial fibrillation, but no individual feature has high enough predictive value to offer clinical utility using routine statistical modelling. The intensive evaluation of the ECG afforded by a convolutional neural network might be able to detect subtle, multifaceted perturbations in the ECG. We have previously shown convolution neural networks can evaluate the resting ECG for detection of antiarrhythmic drug levels, abnormal electrolytes levels, and detection of asymptomatic left ventricular dysfunction, providing proof of concept that clinically important phenomena can be detected with artificial intelligence (AI) applications to the ECG.
Added value of this study
This is the first study to our knowledge to use a convolution neural network to identify the electrocardiographic signature of atrial fibrillation present during sinus rhythm. We used an AI model to find signals in the ECG that might be invisible to the human eye but contain important information about the presence of atrial fibrillation. The AI model was trained using the standard 10-second, 12-lead ECG alone and does not require any other inputs for atrial fibrillation risk assessment. Importantly, the detection of the atrial fibrillation signal in the ECG relies on this easily obtained 10-second recording as opposed to the more invasive loop recording or cumbersome Holter monitoring. We found that an AI model can differentiate between patients with a history of (or impending) atrial fibrillation with a high degree of accuracy using a single routine ECG. Addition of multiple ECGs within an individual patient improved the model accuracy and suggests repeated measures might yield even better performance.
Implications of all the available evidence
Our study supports the hypothesis that subtle patterns on the normal sinus rhythm ECG can suggest the presence of atrial fibrillation. The ability to identify patients with potentially undetected atrial fibrillation using an inexpensive, non-invasive, widely available, point-of-care test has important practical implications for atrial fibrillation screening and potentially for the management of patients with prior stroke of unknown cause.