Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)
Article
Salvi, Massimo, Acharya, Madhav R., Seoni, Silvia, Faust, Oliver, Tan, Ru-San, Barua, Prabal Datta, García, Salvador, Molinari, Filippo and Acharya, U. Rajendra Acharya. 2024. "Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)." WIREs Data Mining and Knowledge Discovery. 14 (3). https://doi.org/10.1002/widm.1530
Article Title | Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023) |
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ERA Journal ID | 201715 |
Article Category | Article |
Authors | Salvi, Massimo, Acharya, Madhav R., Seoni, Silvia, Faust, Oliver, Tan, Ru-San, Barua, Prabal Datta, García, Salvador, Molinari, Filippo and Acharya, U. Rajendra Acharya |
Journal Title | WIREs Data Mining and Knowledge Discovery |
Journal Citation | 14 (3) |
Article Number | e1530 |
Number of Pages | 44 |
Year | 2024 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1942-4787 |
1942-4795 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/widm.1530 |
Web Address (URL) | https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/widm.1530 |
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. |
Keywords | artificial intelligence; atrial fibrillation; biosignals; healthcare; predictive models |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Polytechnic University of Turin, Italy |
University of Southern Queensland | |
Anglia Ruskin University, United Kingdom | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
School of Business | |
University of Technology Sydney | |
University of Granada, Spain | |
School of Mathematics, Physics and Computing | |
Centre for Health Research |
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https://research.usq.edu.au/item/z5vq9/artificial-intelligence-for-atrial-fibrillation-detection-prediction-and-treatment-a-systematic-review-of-the-last-decade-2013-2023
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