{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:33:35Z","timestamp":1783528415999,"version":"3.55.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,7]]},"DOI":"10.7759\/cureus.94065","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:22:06Z","timestamp":1759882926000},"source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence in Electrocardiography: From Automated Arrhythmia Detection to Predicting Hidden Cardiovascular Disease"],"prefix":"10.7759","author":[{"given":"Ramy","family":"Elantary","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samar","family":"Othman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01811855","article-title":"The galvanometric recording of the human electrocardiogram, and an assessment of the application of the capillary electrometer in physiology [Article in German]","volume":"99","author":"Einthoven W","year":"1903","unstructured":"Einthoven W. The galvanometric recording of the human electrocardiogram, and an assessment of the application of the capillary electrometer in physiology [Article in German]. Pfl\u00fcger Arch. 1903, 99:472-80. 10.1007\/BF01811855","journal-title":"Pfl\u00fcger Arch"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4160-3774-3.X1000-3","article-title":"Chou\u2019s electrocardiography in clinical practice 6th ed","author":"Surawicz B","year":"2008","unstructured":"Surawicz B, Knilans TK. Chou\u2019s electrocardiography in clinical practice 6th ed. Saunders, Philadelphia, PA; 2008. 10.1016\/B978-1-4160-3774-3.X1000-3"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2020.3989","article-title":"Accuracy of physician electrocardiogram interpretation: a systematic review and meta-analysis","volume":"180","author":"Cook DA","year":"2020","unstructured":"Cook DA, Oh SY, Pusic MV. Accuracy of physician electrocardiogram interpretation: a systematic review and meta-analysis. JAMA Intern Med. 2020, 180:1461-71. 10.1001\/jamainternmed.2020.3989","journal-title":"JAMA Intern Med"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.0000145144.56673.59","article-title":"Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical-Care Nurses","volume":"110","author":"Drew BJ","year":"2004","unstructured":"Drew BJ, Califf RM, Funk M, et al.. Practice standards for electrocardiographic monitoring in hospital settings: an American Heart Association scientific statement from the Councils on Cardiovascular Nursing, Clinical Cardiology, and Cardiovascular Disease in the Young: endorsed by the International Society of Computerized Electrocardiology and the American Association of Critical-Care Nurses. Circulation. 2004, 110:2721-46. 10.1161\/01.CIR.0000145144.56673.59","journal-title":"Circulation"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0240-2","article-title":"Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram","volume":"25","author":"Attia ZI","year":"2019","unstructured":"Attia ZI, Kapa S, Lopez-Jimenez F, et al.. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019, 25:70-4. 10.1038\/s41591-018-0240-2","journal-title":"Nat Med"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.03.521","article-title":"Artificial intelligence in cardiology","volume":"71","author":"Johnson KW","year":"2018","unstructured":"Johnson KW, Torres Soto J, Glicksberg BS, et al.. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018, 71:2668-79. 10.1016\/j.jacc.2018.03.521","journal-title":"J Am Coll Cardiol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun Y","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015, 521:436-44. 10.1038\/nature14539","journal-title":"Nature"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network","volume":"25","author":"Hannun AY","year":"2019","unstructured":"Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019, 25:65-9. 10.1038\/s41591-018-0268-3","journal-title":"Nat Med"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2019.0640","article-title":"Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram","volume":"4","author":"Galloway CD","year":"2019","unstructured":"Galloway CD, Valys AV, Shreibati JB, et al.. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol. 2019, 4:428-36. 10.1001\/jamacardio.2019.0640","journal-title":"JAMA Cardiol"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01335-4","article-title":"Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial","volume":"27","author":"Yao X","year":"2021","unstructured":"Yao X, Rushlow DR, Inselman JW, et al.. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021, 27:815-9. 10.1038\/s41591-021-01335-4","journal-title":"Nat Med"},{"key":"ref11","unstructured":"U.S. Food and Drug Administration. FDA authorizes AI-ECG software for detection of low ejection fraction. (2023). https.\/\/www.fda.gov."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin C","year":"2019","unstructured":"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. 10.1038\/s42256-019-0048-x","journal-title":"Nat Mach Intell"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar P","year":"2022","unstructured":"Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022, 28:31-8. 10.1038\/s41591-021-01614-0","journal-title":"Nat Med"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15432-4","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro AH","year":"2020","unstructured":"Ribeiro AH, Ribeiro MH, Paix\u00e3o GM, et al.. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020, 11:1760. 10.1038\/s41467-020-15432-4","journal-title":"Nat Commun"},{"key":"ref15","article-title":"AF classification from a short single lead ECG recording: the PhysioNet\/Computing in Cardiology Challenge 2017","volume":"44","author":"Clifford GD","year":"2017","unstructured":"Clifford GD, Liu C, Moody B, et al.. AF classification from a short single lead ECG recording: the PhysioNet\/Computing in Cardiology Challenge 2017. Comput Cardiol. 2017, 44:1-4.","journal-title":"Comput Cardiol"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-scale assessment of a smartwatch to identify atrial fibrillation","volume":"381","author":"Perez MV","year":"2019","unstructured":"Perez MV, Mahaffey KW, Hedlin H, et al.. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019, 381:1909-17. 10.1056\/NEJMoa1901183","journal-title":"N Engl J Med"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)31721-0","article-title":"An AI-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis","volume":"394","author":"Attia ZI","year":"2019","unstructured":"Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al.. An AI-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis. Lancet. 2019, 394:861-7. 10.1016\/S0140-6736(19)31721-0","journal-title":"Lancet"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.120.047829","article-title":"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","volume":"143","author":"Raghunath S","year":"2021","unstructured":"Raghunath S, Pfeifer JM, Ulloa-Cerna AE, 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. 10.1161\/CIRCULATIONAHA.120.047829","journal-title":"Circulation"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.121.057480","article-title":"ECG-based deep learning and prediction of atrial fibrillation onset","volume":"145","author":"Khurshid S","year":"2022","unstructured":"Khurshid S, Friedman S, Reeder C, et al.. ECG-based deep learning and prediction of atrial fibrillation onset. Circulation. 2022, 145:122-33. 10.1161\/CIRCULATIONAHA.121.057480","journal-title":"Circulation"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1093\/ehjdh\/ztac020","article-title":"Detection of atrial fibrillation-related electropathology by artificial intelligence: is the future already here?()","volume":"3","author":"de Groot NM","year":"2022","unstructured":"de Groot NM, van Schie MS. Detection of atrial fibrillation-related electropathology by artificial intelligence: is the future already here?(). Eur Heart J Digit Health. 2022, 3:236-7. 10.1093\/ehjdh\/ztac020","journal-title":"Eur Heart J Digit Health"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2023.02.017","article-title":"3-year outcomes after transcatheter or surgical aortic valve replacement in low-risk patients with aortic stenosis","volume":"81","author":"Forrest JK","year":"2023","unstructured":"Forrest JK, Deeb GM, Yakubov SJ, et al.. 3-year outcomes after transcatheter or surgical aortic valve replacement in low-risk patients with aortic stenosis. J Am Coll Cardiol. 2023, 81:1663-74. 10.1016\/j.jacc.2023.02.017","journal-title":"J Am Coll Cardiol"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner P","year":"2020","unstructured":"Wagner P, Strodthoff N, Bousseljot RD, Kreiseler D, Lunze FI, Samek W, Schaeffter T. PTB-XL, a large publicly available electrocardiography dataset. Sci Data. 2020, 7:154. 10.1038\/s41597-020-0495-6","journal-title":"Sci Data"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.0000000000000483","article-title":"Validation of noninvasive detection of hyperkalemia by artificial intelligence-enhanced electrocardiography in high acuity settings","volume":"19","author":"Harmon DM","year":"2024","unstructured":"Harmon DM, Liu K, Dugan J, Jentzer JC, Attia ZI, Friedman PA, Dillon JJ. Validation of noninvasive detection of hyperkalemia by artificial intelligence-enhanced electrocardiography in high acuity settings. Clin J Am Soc Nephrol. 2024, 19:952-8. 10.2215\/CJN.0000000000000483","journal-title":"Clin J Am Soc Nephrol"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104408","article-title":"ECGAug: a novel method of generating augmented annotated electrocardiogram QRST complexes and rhythm strips","volume":"134","author":"Stabenau HF","year":"2021","unstructured":"Stabenau HF, Bridge CP, Waks JW. ECGAug: a novel method of generating augmented annotated electrocardiogram QRST complexes and rhythm strips. Comput Biol Med. 2021, 134:104408. 10.1016\/j.compbiomed.2021.104408","journal-title":"Comput Biol Med"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCEP.119.007284","article-title":"Age and sex estimation using artificial intelligence from standard 12-lead ECGs","volume":"12","author":"Attia ZI","year":"2019","unstructured":"Attia ZI, Friedman PA, Noseworthy PA, et al.. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circ Arrhythm Electrophysiol. 2019, 12:e007284. 10.1161\/CIRCEP.119.007284","journal-title":"Circ Arrhythm Electrophysiol"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2021.10.025","article-title":"A new dawn in HCM: rise of the RCTs","volume":"78","author":"Masri A","year":"2021","unstructured":"Masri A. A new dawn in HCM: rise of the RCTs. J Am Coll Cardiol. 2021, 78:2533-6. 10.1016\/j.jacc.2021.10.025","journal-title":"J Am Coll Cardiol"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-09227-0","article-title":"Detecting structural heart disease from electrocardiograms using AI","volume":"644","author":"Poterucha TJ","year":"2025","unstructured":"Poterucha TJ, Jing L, Ricart RP, et al.. Detecting structural heart disease from electrocardiograms using AI. Nature. 2025, 644:221-30. 10.1038\/s41586-025-09227-0","journal-title":"Nature"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s44325-025-00078-2","article-title":"Generalizability of electrocardiographic artificial intelligence","volume":"38","author":"Karabayir I","year":"2025","unstructured":"Karabayir I, Akbilgic O. Generalizability of electrocardiographic artificial intelligence. npj Cardiovasc Health. 2025, 38:10.1038\/s44325-025-00078-2","journal-title":"npj Cardiovasc Health"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s44325-024-00031-9","article-title":"Artificial intelligence bias in the prediction and detection of cardiovascular disease","volume":"1","author":"Mihan A","year":"2024","unstructured":"Mihan A, Pandey A, Van Spall HGC. Artificial intelligence bias in the prediction and detection of cardiovascular disease. npj Cardiovasc Health. 2024, 1:10.1038\/s44325-024-00031-9","journal-title":"npj Cardiovasc Health"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"Siontis KC","year":"2021","unstructured":"Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021, 18:465-78. 10.1038\/s41569-020-00503-2","journal-title":"Nat Rev Cardiol"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2024.05.003","article-title":"Transforming cardiovascular care with\u00a0artificial intelligence: from\u00a0discovery to practice: JACC state-of-the-art review","volume":"84","author":"Khera R","year":"2024","unstructured":"Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, Topol EJ. Transforming cardiovascular care with\u00a0artificial intelligence: from\u00a0discovery to practice: JACC state-of-the-art review. J Am Coll Cardiol. 2024, 84:97-114. 10.1016\/j.jacc.2024.05.003","journal-title":"J Am Coll Cardiol"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp2404691","article-title":"Compared with what? Measuring AI against the health care we have","volume":"391","author":"Kohane IS","year":"2024","unstructured":"Kohane IS. Compared with what? Measuring AI against the health care we have. N Engl J Med. 2024, 391:1564-6. 10.1056\/NEJMp2404691","journal-title":"N Engl J Med"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/413673-artificial-intelligence-in-electrocardiography-from-automated-arrhythmia-detection-to-predicting-hidden-cardiovascular-disease","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:22:08Z","timestamp":1759882928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/413673-artificial-intelligence-in-electrocardiography-from-automated-arrhythmia-detection-to-predicting-hidden-cardiovascular-disease"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":32,"URL":"https:\/\/doi.org\/10.7759\/cureus.94065","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,7]]}}}