Introduction
The ECG has an incredible untapped diagnostic and prognostic potential. This easy to use, omnipresent technology has been largely unchanged for a century but still remains critical to the everyday clinical workflow. It is non-invasive, provides rapid actionable insights, easy to perform and readily available at a low cost compared to advanced cardiac imaging modalities.1 These attributes make it a go-to baseline evaluation test for many patients across the spectrum of healthcare settings.1,2
Currently, the ECG is used primarily as a diagnostic tool rather than a broad screening tool for conditions other than rhythm disorders.3-5 This is largely because standard ECG interpretation using analogue or feature-based approaches lack the negative predictive value to exclude cardiac disease (such as myocardial or valvular heart disease) in ECGs that ‘appear normal’. Furthermore, the accuracy of results depends considerably on the interpreter’s competency level. However, this could soon change as technological advances are breathing new life into this century old modality.
This potential change has come forth due to recent computational advancements which have allowed a significant improvement in the machine learning algorithms alongside a vast availability of well-annotated digitalized ECG data.6 Particularly important has been the application of a branch of machine learning known as deep learning (DL) to the ECG7 which has allowed investigators to derive new insights from these voltage-time matrices. It is also now evident that largely undefined markers of health exist that, while unapparent to an expert cardiologist, can be recognized by deep neural network (DNN) models.8 Information at the point-of-care can also be leveraged to facilitate ‘cardiologist-level’ ECG interpretation,6,9 and even go beyond human capability to determine age and sex,10 left ventricular dysfunction,11 predicting hypertrophic cardiomyopathy12 and atrial fibrillation (AF) from sinus rhythm11 amongst others.
In the context of AF, traditional clinical practices have thus far fallen short in several domains including identifying patients at risk of incident AF or those with concomitant undetected paroxysmal AF. Innovative approaches leveraging AI have the potential to provide solutions to solve some of these old problems. In this review we focus only on the roles of AI-enabled ECG (AI-ECG) as it relates to AF, the potential role of DL models in the context of current knowledge gaps, as well as the current limitations of these models.