Is machine learning the future for atrial fibrillation screening?

Pavidra Sivanandarajah; Huiyi Wu; Nikesh Bajaj; Sadia Khan; Fu Siong Ng

doi:10.1016/j.cvdhj.2022.04.001

Is machine learning the future for atrial fibrillation screening?

Cardiovasc Digit Health J. 2022 May 16;3(3):136-145. doi: 10.1016/j.cvdhj.2022.04.001. eCollection 2022 Jun.

Authors

Pavidra Sivanandarajah^{1

2}, Huiyi Wu¹, Nikesh Bajaj¹, Sadia Khan², Fu Siong Ng^{1

2}

Affiliations

¹ National Heart and Lung Institute, Imperial College London, London, United Kingdom.
² Chelsea and Westminster NHS Foundation Trust, London, United Kingdom.

Abstract

Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.

Keywords: Artificial intelligence; Atrial fibrillation; Electronic health records; Machine learning; Screening.

Publication types

Review

Abstract

Publication types

Grants and funding