Is machine learning the future for atrial fibrillation screening?

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 s...

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Detalles Bibliográficos
Autores principales: Sivanandarajah, Pavidra, Wu, Huiyi, Bajaj, Nikesh, Khan, Sadia, Ng, Fu Siong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204790/
https://www.ncbi.nlm.nih.gov/pubmed/35720677
http://dx.doi.org/10.1016/j.cvdhj.2022.04.001
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author Sivanandarajah, Pavidra
Wu, Huiyi
Bajaj, Nikesh
Khan, Sadia
Ng, Fu Siong
author_facet Sivanandarajah, Pavidra
Wu, Huiyi
Bajaj, Nikesh
Khan, Sadia
Ng, Fu Siong
author_sort Sivanandarajah, Pavidra
collection PubMed
description 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.
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spelling pubmed-92047902022-06-18 Is machine learning the future for atrial fibrillation screening? Sivanandarajah, Pavidra Wu, Huiyi Bajaj, Nikesh Khan, Sadia Ng, Fu Siong Cardiovasc Digit Health J Review 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. Elsevier 2022-05-16 /pmc/articles/PMC9204790/ /pubmed/35720677 http://dx.doi.org/10.1016/j.cvdhj.2022.04.001 Text en © 2022 Heart Rhythm Society. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Sivanandarajah, Pavidra
Wu, Huiyi
Bajaj, Nikesh
Khan, Sadia
Ng, Fu Siong
Is machine learning the future for atrial fibrillation screening?
title Is machine learning the future for atrial fibrillation screening?
title_full Is machine learning the future for atrial fibrillation screening?
title_fullStr Is machine learning the future for atrial fibrillation screening?
title_full_unstemmed Is machine learning the future for atrial fibrillation screening?
title_short Is machine learning the future for atrial fibrillation screening?
title_sort is machine learning the future for atrial fibrillation screening?
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204790/
https://www.ncbi.nlm.nih.gov/pubmed/35720677
http://dx.doi.org/10.1016/j.cvdhj.2022.04.001
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