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

Author:

Salvi Massimo1ORCID,Acharya Madhav R.2,Seoni Silvia1ORCID,Faust Oliver3ORCID,Tan Ru‐San45ORCID,Barua Prabal Datta67ORCID,García Salvador8,Molinari Filippo1ORCID,Acharya U. Rajendra910ORCID

Affiliation:

1. Department of Electronics and Telecommunications, Politecnico di Torino Biolab, PolitoBIOMedLab Turin Italy

2. University of Southern Queensland Springfield Queensland Australia

3. School of Computing and Information Science Anglia Ruskin University Cambridge Campus Cambridge UK

4. National Heart Centre Singapore Singapore

5. Duke‐NUS Medical School Singapore

6. School of Business (Information System) University of Southern Queensland Toowoomba Queensland Australia

7. Faculty of Engineering and Information Technology University of Technology Sydney Sydney New South Wales Australia

8. Andalusian Research Institute in Data Science and Computational Intelligence, Department of Computer Science and Artificial Intelligence Universidad de Granada Granada Spain

9. School of Mathematics, Physics and Computing University of Southern Queensland Springfield Queensland Australia

10. Centre for Health Research University of Southern Queensland Springfield Queensland Australia

Abstract

AbstractAtrial 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

Publisher

Wiley

Subject

General Computer Science

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