Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications

Anthony H. Kashou; Demilade A. Adedinsewo; Peter A. Noseworthy

doi:10.1146/annurev-med-042420-105906

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications

Anthony H. Kashou, Demilade A. Adedinsewo, Peter A. Noseworthy

Cardiovascular Medicine

Research output: Contribution to journal › Review article › peer-review

Abstract

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

Original language	English (US)
Pages (from-to)	355-362
Number of pages	8
Journal	Annual Review of Medicine
Volume	73
DOIs	https://doi.org/10.1146/annurev-med-042420-105906
State	Published - 2022

Keywords

Artificial intelligence
Atrial fibrillation
Convolutional neural network
Deep neural network
ECG
Electrocardiogram
Machine learning
Sinus rhythm

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)

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10.1146/annurev-med-042420-105906

Cite this

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title = "Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications",

abstract = "Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.",

keywords = "Artificial intelligence, Atrial fibrillation, Convolutional neural network, Deep neural network, ECG, Electrocardiogram, Machine learning, Sinus rhythm",

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year = "2022",

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pages = "355--362",

journal = "Annual Review of Medicine",

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T2 - A Silent Threat with Uncertain Implications

AU - Kashou, Anthony H.

AU - Adedinsewo, Demilade A.

AU - Noseworthy, Peter A.

PY - 2022

Y1 - 2022

N2 - Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

AB - Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

KW - Artificial intelligence

KW - Atrial fibrillation

KW - Convolutional neural network

KW - Deep neural network

KW - ECG

KW - Electrocardiogram

KW - Machine learning

KW - Sinus rhythm

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JO - Annual Review of Medicine

JF - Annual Review of Medicine

ER -

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications

Abstract

Keywords

ASJC Scopus subject areas

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