In the context of atrial fibrillation (AF), traditional clinical
practices have thus far fallen short in several domains such as
identifying patients at risk of incident AF or patients with concomitant
undetected paroxysmal AF. Novel approaches leveraging artificial
intelligence have the potential to provide new tools to deal with some
of these old problems. In this review we focus on the roles of
artificial intelligence-enabled ECG pertaining to AF, potential roles of
deep learning (DL) models in the context of current knowledge gaps, as
well as limitations of these models. One key area where DL models can
translate to better patient outcomes is through automated ECG
interpretation. Further, we overview some of the challenges facing AF
screening and the harms and benefits of screening. In this context, a
unique model was developed to detect underlying hidden AF from sinus
rhythm and is discussed in detail with its potential uses. Knowledge
gaps also remain regarding the best ways to monitor patients with ESUS
and who would benefit most from oral anticoagulation. The AI-enabled AF
model is one potential way to tackle this complex problem as it could be
used to identify a subset of high-risk ESUS patients likely to benefit
from empirical oral anticoagulation. Role of DL models assessing AF
burden from long duration ECG data is also discussed as a way of guiding
management. There is a trend towards the use of consumer-grade
wristbands and watches to detect AF from photoplethysmography data.
However, ECG currently remains the gold standard to detect arrythmias
including AF. Lastly, role of adequate external validation of the models
and clinical trials to study true performance is discussed.
CORRIGENDUM: Antiplatelet Effect of Single Antiplatelet Therapy With Prasugrel and Oral Anticoagulation After Stent Implantation in a Rabbit Arteriovenous Shunt Model
