Abstract
There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.
Highlights
Atrial fibrillation (AF) is the most common arrhythmia worldwide with its burden expected to continue to increase with the aging population
AF is often associated with distinct structural heart abnormalities that are apparent on cardiac imaging, including echocardiography, CT and MRI
(3) Implications of machine learning algorithms on management: While the overall aim of this review is to evaluate the role of AI in predicting AF, future studies should undoubtedly evaluate the prospective use of these algorithms to determine optimal management strategies for patients
Summary
Atrial fibrillation (AF) is the most common arrhythmia worldwide with its burden expected to continue to increase with the aging population. Numerous clinical risk scores have been proposed, incorporating readily available variables from the patient’s medical history, such as age, ethnicity, height, weight, blood pressure, smoking status, medication use, and comorbidities (Schnabel et al, 2009; Chamberlain et al, 2011; Alonso et al, 2013; Suenari et al, 2017; Li et al, 2019; Hu and Lin, 2020; Lip et al, 2020; Himmelreich et al, 2021) Abnormalities in both cardiac and inflammatory biomarkers have been shown to augment the predictive ability of clinical prediction scores (O’Neal et al, 2016). There is an abundance of clinical variables that have been shown to be predictive of AF, individually or in limited pairings
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