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.