Approximations of the performances obtained by using the classical statistics used by humans compared to those obtained by AI for the three different predictions
Note that the score attributed to the human for detection is necessarily 100% since it is the human who sets the reference value (the ground truth). AI: artificial intelligence
Declarations
Author contributions
JMG: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. CG and FM: Software, Validation, Funding acquisition. HB and SC: Validation, Writing—review & editing, Supervision. All authors read and approved the submitted version.
Conflicts of interest
The authors declare that they have no conflicts of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publication
Not applicable.
Availability of data and materials
Not applicable.
Funding
This work was supported in part by the French Community of Belgium [FRIA funding: FC 038733]; this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement [No 101034383]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.
References
Carrasco-Ramiro F, Peiró-Pastor R, Aguado B. Human genomics projects and precision medicine.Gene Ther. 2017;24:551–61. [DOI] [PubMed]
Leopold JA, Loscalzo J. Emerging Role of Precision Medicine in Cardiovascular Disease.Circ Res. 2018;122:1302–15. [DOI] [PubMed] [PMC]
Jen MY, Shahrokhi M, Varacallo MA. Predictive Medicine. Treasure Island (FL): StatPearls Publishing; 2025. [PubMed]
Lubitz SA, Faranesh AZ, Selvaggi C, Atlas SJ, McManus DD, Singer DE, et al. Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study.Circulation. 2022;146:1415–24. [DOI] [PubMed] [PMC]
Kalarus Z, Mairesse GH, Sokal A, Boriani G, Średniawa B, Casado-Arroyo R, et al. Searching for atrial fibrillation: looking harder, looking longer, and in increasingly sophisticated ways. An EHRA position paper.Europace. 2023;25:185–98. [DOI] [PubMed] [PMC]
Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al.; Apple Heart Study Investigators. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation.N Engl J Med. 2019;381:1909–17. [DOI] [PubMed] [PMC]
Christophersen IE, Yin X, Larson MG, Lubitz SA, Magnani JW, McManus DD, et al. A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study.Am Heart J. 2016;178:45–54. [DOI] [PubMed] [PMC]
Batta A, Hatwal J, Batta A, Verma S, Sharma YP. Atrial fibrillation and coronary artery disease: An integrative review focusing on therapeutic implications of this relationship.World J Cardiol. 2023;15:229–43. [DOI] [PubMed] [PMC]
Sharma YP, Batta A, Makkar K, Hatwal J, A Gawalkar A, Kaur N, et al. Angiographic profile and outcomes in persistent non-valvular atrial fibrillation: A study from tertiary care center in North India.Indian Heart J. 2022;74:7–12. [DOI] [PubMed] [PMC]
Wang N, Yu Y, Sun Y, Zhang H, Wang Y, Chen C, et al. Acquired risk factors and incident atrial fibrillation according to age and genetic predisposition.Eur Heart J. 2023;44:4982–93. [DOI] [PubMed] [PMC]
Reddy YNV, Borlaug BA, Gersh BJ. Management of Atrial Fibrillation Across the Spectrum of Heart Failure With Preserved and Reduced Ejection Fraction.Circulation. 2022;146:339–57. [DOI] [PubMed]
Batta A, Sharma YP, Hatwal J, Panda P, Kumar BGV, Bhogal S. Predictors of dementia amongst newly diagnosed non-valvular atrial fibrillation patients.Indian Heart J. 2022;74:505–9. [DOI] [PubMed] [PMC]
Jabbour G, Nolin-Lapalme A, Tastet O, Corbin D, Jordà P, Sowa A, et al. Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.Eur Heart J. 2024;45:4920–34. [DOI] [PubMed] [PMC]
Li YG, Pastori D, Farcomeni A, Yang PS, Jang E, Joung B, et al. A Simple Clinical Risk Score (C2HEST) for Predicting Incident Atrial Fibrillation in Asian Subjects: Derivation in 471,446 Chinese Subjects, With Internal Validation and External Application in 451,199 Korean Subjects.Chest. 2019;155:510–8. [DOI] [PubMed] [PMC]
Moody G, Goldberger A, McClennen S, Swiryn S. Predicting the onset of paroxysmal atrial fibrillation: the Computers in Cardiology Challenge 2001.In: Computers in Cardiology. Computers in Cardiology (CinC); 2001 Sep 23-26; Rotterdam, Netherlands. IEEE; 2001. pp. 113–6. [DOI]
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.Circulation. 2000;101:e215–20. [DOI] [PubMed]
Gilon C, Grégoire JM, Hellinckx J, Carlier S, Bersini H. Reproducibility of machine learning models for paroxysmal atrial fibrillation onset forecast.Arch Cardiovasc Dis Suppl. 2023;15:172–3. [DOI]
Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, 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.Lancet. 2019;394:861–7. [DOI] [PubMed]
Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, et al. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.Circulation. 2021;143:1287–98. [DOI] [PubMed] [PMC]
Baek YS, Lee SC, Choi W, Kim DH. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm.Sci Rep. 2021;11:12818. [DOI] [PubMed] [PMC]
Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, et al. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.Circulation. 2022;145:122–33. [DOI] [PubMed] [PMC]
Yuan N, Duffy G, Dhruva SS, Oesterle A, Pellegrini CN, Theurer J, et al. Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.JAMA Cardiol. 2023;8:1131–9. [DOI] [PubMed] [PMC]
Singh JP, Fontanarava J, de Massé G, Carbonati T, Li J, Henry C, et al. Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network.Eur Heart J Digit Health. 2022;3:208–17. [DOI] [PubMed] [PMC]
Hygrell T, Viberg F, Dahlberg E, Charlton PH, Kemp Gudmundsdottir K, Mant J, et al. An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening.Europace. 2023;25:1332–8. [DOI] [PubMed] [PMC]
Grégoire JM, Gilon C, Vaneberg N, Bersini H, Carlier S. Machine learning-based atrial fibrillation detection and onset prediction using QT-dynamicity.Physiol Meas. 2024;45:075001. [DOI] [PubMed]
Darbar D, Roden DM. Genetic mechanisms of atrial fibrillation: impact on response to treatment.Nat Rev Cardiol. 2013;10:317–29. [DOI] [PubMed] [PMC]
Huang HD, Darbar D. Genetic Risk Scores for Atrial Fibrillation: Do They Improve Risk Estimation?Can J Cardiol. 2017;33:422–4. [DOI] [PubMed] [PMC]
Kany S, Jurgens SJ, Rämö JT, Christophersen IE, Rienstra M, Chung MK, et al. Genetic testing in early-onset atrial fibrillation.Eur Heart J. 2024;45:3111–23. [DOI] [PubMed] [PMC]
Chen YH, Xu SJ, Bendahhou S, Wang XL, Wang Y, Xu WY, et al. KCNQ1 gain-of-function mutation in familial atrial fibrillation.Science. 2003;299:251–4. [DOI] [PubMed]
Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations.Nat Genet. 2018;50:1219–24. [DOI] [PubMed] [PMC]
Hughes JW, Olgin JE, Avram R, Abreau SA, Sittler T, Radia K, et al. Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation.JAMA Cardiol. 2021;6:1285–95. [DOI] [PubMed] [PMC]
Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, et al. Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.Lancet. 2022;400:1206–12. [DOI] [PubMed]
Grégoire JM, Gilon C, Carlier S, Bersini H. Role of the autonomic nervous system and premature atrial contractions in short-term paroxysmal atrial fibrillation forecasting: Insights from machine learning models.Arch Cardiovasc Dis. 2022;115:377–87. [DOI] [PubMed]
Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.Nat Mach Intell. 2019;1:206–15. [DOI] [PubMed] [PMC]
