Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi, M; Acharya, MR; Seoni, S; Faust, O; Tan, RS; Barua, PD; García, S; Molinari, F; Acharya, UR

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi, M Acharya, MR Seoni, S Faust, O Tan, RS Barua, PD García, S Molinari, F Acharya, UR

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Publisher:: WILEY PERIODICALS, INC
Publication Type:: Journal Article
Citation:: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2024, 14, (3)
Issue Date:: 2024-05-01

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Field	Value	Language
dc.contributor.author	Salvi, M
dc.contributor.author	Acharya, MR
dc.contributor.author	Seoni, S
dc.contributor.author	Faust, O
dc.contributor.author	Tan, RS
dc.contributor.author	Barua, PD
dc.contributor.author	García, S
dc.contributor.author	Molinari, F
dc.contributor.author	Acharya, UR
dc.date.accessioned	2025-03-20T01:20:30Z
dc.date.available	2025-03-20T01:20:30Z
dc.date.issued	2024-05-01
dc.identifier.citation	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2024, 14, (3)
dc.identifier.issn	1942-4787
dc.identifier.issn	1942-4795
dc.identifier.uri	http://hdl.handle.net/10453/186027
dc.description.abstract	Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand-crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF-related tasks. However, real-world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population-based clinical trials to facilitate translation into practice. This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence.
dc.language	English
dc.publisher	WILEY PERIODICALS, INC
dc.relation.ispartof	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
dc.relation.isbasedon	10.1002/widm.1530
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0804 Data Format, 0806 Information Systems
dc.subject.classification	4605 Data management and data science
dc.title	Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	in_progress	*
dc.date.updated	2025-03-20T01:20:27Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	3

Abstract:

Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand-crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF-related tasks. However, real-world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population-based clinical trials to facilitate translation into practice. This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/186027