Have a personal or library account? Click to login
Implementation of Artificial Intelligence for Predicting Atrial Fibrillation – A Review Cover

Implementation of Artificial Intelligence for Predicting Atrial Fibrillation – A Review

Open Access
|Dec 2025

References

  1. Lee KH, Ko BG, Jin YB, Chang WJ. Explainable Paroxysmal Atrial Fibrillation Diagnosis Using Electrocardiogram with Artificial Intelligence. Europace. 2023 May 24;25(Supplemen t_1):euad122.526. doi: 10.1093/europace/euad122.526
  2. Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. The Lancet. 2009 Feb;373(9665):739-745. doi: 10.1016/S0140-6736(09)60443-8
  3. Wright JD, Folsom AR, Coresh J, et al. The ARIC (Atherosclerosis Risk In Communities) Study. Journal of the American College of Cardiology. 2021 Jun;77(23):2939-2959. doi: 10.1016/j. jacc.2021.04.035
  4. Alonso A, Krijthe BP, Aspelund T, et al. Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE-AF Consortium. JAHA. 2013 Mar 12;2(2):e000102. doi: 10.1161/JAHA.112.000102
  5. Goudis C, Daios S, Dimitriadis F, Liu T. CHARGE-AF: A Useful Score For Atrial Fibrillation Prediction? CCR. 2023 Mar;19(2):e010922208402. doi: 10.2174/1573403X186662209 01102557
  6. Lin F, Zhang P, Chen Y, et al. Artificial-intelligence-based risk prediction and mechanism discovery for atrial fibrillation using heart beat-to-beat intervals. Med. 2024 May;5(5):414-431.e5. doi: 10.1016/j.medj.2024.02.006
  7. Hygrell T, Viberg F, Dahlberg E, et al. An artificial intelligencebased model for prediction of atrial fibrillation from singlelead sinus rhythm electrocardiograms facilitating screening. EP Europace. 2023 Apr 15;25(4):1332-1338. doi: 10.1093/europace/euad036
  8. Hill NR, Groves L, Dickerson C, et al. Identification of undiagnosed atrial fibrillation using a machine learning riskprediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England. European Heart Journal – Digital Health. 2022 Jul 6;3(2):195-204. doi: 10.1093/ehjdh/ztac009
  9. Chen L, Huang SH, Wang TH, et al. Deep learning-based automatic left atrial appendage filling defects assessment on cardiac computed tomography for clinical and subclinical atrial fibrillation patients. Heliyon. 2023 Jan;9(1):e12945. doi: 10.1016/j.heliyon.2023.e12945
  10. Grout RW, Hui SL, Imler TD, et al. Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED). BMC Med Inform Decis Mak. 2021 Dec;21(1):112. doi: 10.1186/s12911-021-01482-1
  11. Baek YS, Lee SC, Choi WI, Kim DH. Prediction of atrial fibrillation from normal ECG using artificial intelligence in patients with unexplained stroke. European Heart Journal. 2020 Nov 1;41(Supplement_2):ehaa946.0348. doi: 10.1093/ehjci/ehaa946.0348
  12. Pujadas ER, Raisi-Estabragh Z, Szabo L, et al. Atrial fibrillation prediction by combining ECG markers and CMR radiomics. Sci Rep. 2022 Nov 7;12(1):18876. doi: 10.1038/s41598-022-21663-w
  13. Yagi N, Suzuki S, Hirota N, Arita T, Otuka T, Yamashita T. Prediction of persistent form of atrial fibrillation using left atrial morphology on preprocedural computed tomography: application of radiomics. European Heart Journal. 2022 Oct 3;43(Supplement_2):ehac544.327. doi: 10.1093/eurheartj/ehac544.327
  14. Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschr Elektrophys. 2022 Mar;33(1):34-41. doi: 10.1007/s00399-022-00839-x
  15. Arfat Y, Mittone G, Esposito R, Cantalupo B, De Ferrari GM, Aldinucci M. Machine learning for cardiology. Minerva Cardiol Angiol [Internet]. 2022 Mar [cited 2024 May 20];70(1). doi: 10.23736/S2724-5683.21.05709-4
  16. Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. Journal of Medical Imaging and Radiation Sciences. 2019 Dec;50(4):477-487. doi: 10.1016/j.jmir.2019.09.005
  17. Warraich HJ, Gandhavadi M, Manning WJ. Mechanical Discordance of the Left Atrium and Appendage: A Novel Mechanism of Stroke in Paroxysmal Atrial Fibrillation. Stroke. 2014 May;45(5):1481-1484. doi: 10.1161/STROKEAHA.114.004800
  18. Melzi P, Tolosana R, Cecconi A, et al. Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization. Sci Rep. 2021 Nov 23;11(1):22786. doi: 10.1038/s41598-021-02179-1
  19. Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet. 2019 Sep;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0
  20. Svennberg E, Friberg L, Frykman V, Al-Khalili F, Engdahl J, Rosenqvist M. Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP): a multicentre, parallel group, unmasked, randomised controlled trial. The Lancet. 2021 Oct;398(10310):1498-1506. doi: 10.1016/S0140-6736(21)01637-8
  21. Kemp Gudmundsdottir K, Fredriksson T, Svennberg E, et al. Stepwise mass screening for atrial fibrillation using N-terminal B-type natriuretic peptide: the STROKESTOP II study. EP Europace. 2020 Jan 1;22(1):24-32. doi: 10.1093/europace/euz255
  22. Williams K, Modi RN, Dymond A, et al. Cluster randomised controlled trial of screening for atrial fibrillation in people aged 70 years and over to reduce stroke: protocol for the pilot study for the SAFER trial. BMJ Open. 2022 Sep;12(9):e065066. doi: 10.1136/bmjopen-2022-065066
  23. Tseng AS, Noseworthy PA. Prediction of Atrial Fibrillation Using Machine Learning: A Review. Front Physiol. 2021 Oct 28;12:752317. doi: 10.3389/fphys.2021.752317
  24. Nattel S, Burstein B, Dobrev D. Atrial Remodeling and Atrial Fibrillation: Mechanisms and Implications. Circ: Arrhythmia and Electrophysiology. 2008 Apr;1(1):62-73. doi: 10.1161/CIRCEP.107.754564
  25. Xu HF, He YM, Qian YX, Zhao X, Li X, Yang XJ. Left ventricular posterior wall thickness is an independent risk factor for paroxysmal atrial fibrillation. West Indian Med J. 2011 Dec;60(6):647-652.
  26. Hirose T, Kawasaki M, Tanaka R, et al. Left atrial function assessed by speckle tracking echocardiography as a predictor of new-onset non-valvular atrial fibrillation: results from a prospective study in 580 adults. European Heart Journal – Cardiovascular Imaging. 2012 Mar 1;13(3):243-250. doi: 10.1093/ejechocard/jer251
  27. Siebermair J, Suksaranjit P, McGann CJ, et al. Atrial fibrosis in non-atrial fibrillation individuals and prediction of atrial fibrillation by use of late gadolinium enhancement magnetic resonance imaging. Cardiovasc Electrophysiol. 2019 Apr;30(4):550-556. doi: 10.1111/jce.13846
  28. Anagnostopoulos I, Kousta M, Kossyvakis C, et al. Epicardial Adipose Tissue and Atrial Fibrillation Recurrence following Catheter Ablation: A Systematic Review and Meta-Analysis. JCM. 2023 Oct 5;12(19):6369. doi: 10.3390/jcm12196369
  29. Halaţiu V-B, Benedek I, Rodean I-P, et al. Coronary Computed Tomography Angiography-Derived Modified Duke Index Is Associated with Peri-Coronary Fat Attenuation Index and Predicts Severity of Coronary Inflammation. Medicina. 2024; 60(5):765. doi: 10.3390/medicina60050765
  30. Gerculy R, Benedek I, Kovács I, et al. CT-Assessment of Epicardial Fat Identifies Increased Inflammation at the Level of the Left Coronary Circulation in Patients with Atrial Fibrillation. JCM. 2024 Feb 26;13(5):1307. doi: 10.3390/jcm13051307Journal of Cardiovascular Emergencies 2025;11(4):124-129
  31. West HW, Siddique M, Williams MC, et al. Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography. JACC: Cardiovascular Imaging. 2023 Jun;16(6):800-816. doi: 10.1016/j.jcmg.2022.11.018
  32. Antoniades C, Tousoulis D, Vavlukis M, et al. Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers. European Heart Journal. 2023 Oct 12;44(38):3827-3844. doi: 10.1093/eurheartj/ehad484
  33. Chan K, Wahome E, Tsiachristas A, et al. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study. The Lancet. 2024 Jun;403(10444):2606-2618.
  34. Sieweke JT, Hagemus J, Biber S, et al. Echocardiographic Parameters to Predict Atrial Fibrillation in Clinical Routine The EAHsy-AF Risk Score. Front Cardiovasc Med. 2022 Mar 8;9:851474. doi: 10.3389/fcvm.2022.851474
  35. Naghavi M, Yankelevitz D, Reeves AP, et al. AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CACTM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis. Journal of Cardiovascular Computed Tomography. 2024 Apr;S1934592524000790. doi: 10.1016/j. jcct.2024.05.034
DOI: https://doi.org/10.2478/jce-2025-0021 | Journal eISSN: 2457-5518 | Journal ISSN: 2457-550X
Language: English
Page range: 124 - 129
Submitted on: Jul 9, 2025
|
Accepted on: Nov 26, 2025
|
Published on: Dec 27, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Renáta Gerculy, Emanuel Blîndu, Theodora Benedek, published by Asociatia Transilvana de Terapie Transvasculara si Transplant KARDIOMED
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.