{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:46:11Z","timestamp":1791909971044,"version":"4.3.4"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Wearable electrocardiogram (ECG) monitoring devices that utilize single-lead ECG technology have become valuable tools for identifying paroxysmal atrial fibrillation (AF). This study aimed to develop a machine learning (ML) algorithm to predict new-onset AF by training it on single-lead data extracted from 12-lead ECG recordings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Patients who underwent 12-lead ECG between January 2010 and December 2021 were classified into two groups based on a review of their medical records and diagnostic codes: the AF group and the normal group. An ML model was created using single-lead ECG data, excluding three augmented leads, and incorporating 60 calculated statistical variables for each of the remaining single leads. The model's performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and F1 score. We trained the ML model on 248,612 ECGs collected from 106,606 patients, of whom 11,810 had definite AF. Among the single-lead machine learning models developed from each of the nine individual leads, lead I demonstrated the best performance. The AUROC of the single-lead ECG ML model using lead I was 0.801, while the AUROC of the 12-lead ECG ML model was 0.816.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The single-lead ECG ML model has shown promise in predicting new-onset atrial fibrillation (AF), particularly with lead I. Its performance is comparable to that of the 12-lead model.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2025.1612750","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T06:09:38Z","timestamp":1759817378000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning algorithms for predicting atrial fibrillation using single-lead data derived from 12-lead ECGs"],"prefix":"10.3389","volume":"12","author":[{"given":"Ji-Hoon","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Hee","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongwoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JaeHu","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"KyungChang","family":"Woo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo Jin","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Jung","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Keun","family":"On","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju Youn","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyoung-Min","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.jacc.2020.11.010","article-title":"Global burden of cardiovascular diseases and risk factors, 1990\u20132019: update from the GBD 2019 study","volume":"76","author":"Roth","year":"2020","journal-title":"J Am Coll Cardiol"},{"key":"B2","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1161\/01.STR.22.8.983","article-title":"Atrial fibrillation as an independent risk factor for stroke: the Framingham study","volume":"22","author":"Wolf","year":"1991","journal-title":"Stroke"},{"key":"B3","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1161\/CIRCULATIONAHA.115.018614","article-title":"Atrial fibrillation begets heart failure and vice versa: temporal associations and differences in preserved versus reduced ejection fraction","volume":"133","author":"Santhanakrishnan","year":"2016","journal-title":"Circulation"},{"key":"B4","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1093\/ehjqcco\/qcaa093","article-title":"Atrial fibrillation in the UK: predicting costs of an emerging epidemic recognizing and forecasting the cost drivers of atrial fibrillation-related costs","volume":"8","author":"Burdett","year":"2022","journal-title":"Eur Heart J Qual Care Clin Outcomes"},{"key":"B5","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1093\/europace\/euac038","article-title":"How to use digital devices to detect and manage arrhythmias: an EHRA practical guide","volume":"24","author":"Svennberg","year":"2022","journal-title":"Europace"},{"key":"B6","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1056\/NEJMra2301903","article-title":"Wearable digital health technologies for monitoring in cardiovascular medicine","volume":"390","author":"Spatz","year":"2024","journal-title":"N Engl J Med"},{"key":"B7","doi-asserted-by":"publisher","first-page":"e024178","DOI":"10.1136\/bmjopen-2018-024178","article-title":"Atrial fibrillation detection using single lead portable electrocardiographic monitoring: a systematic review and meta-analysis","volume":"8","author":"Ramkumar","year":"2018","journal-title":"BMJ Open"},{"key":"B8","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1016\/j.jacc.2024.08.019","article-title":"Effect of screening for undiagnosed atrial fibrillation on stroke prevention","volume":"84","author":"Lopes","year":"2024","journal-title":"J Am Coll Cardiol"},{"key":"B9","year":""},{"key":"B10","doi-asserted-by":"publisher","first-page":"e1530","DOI":"10.1002\/widm.1530","article-title":"Artificial intelligence for atrial fibrillation detection, prediction, and treatment: a systematic review of the last decade (2013\u20132023)","volume":"14","author":"Salvi","year":"2024","journal-title":"WIRES Data Min Knowl Discov"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1596574","DOI":"10.3389\/fcvm.2025.1596574","article-title":"Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations","volume":"12","author":"Antoun","year":"2025","journal-title":"Front Cardiovasc Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41746-023-00966-w","article-title":"Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias","volume":"6","author":"Gadaleta","year":"2023","journal-title":"npj Digit Med"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e034154","DOI":"10.1161\/JAHA.123.034154","article-title":"Machine learning algorithm to predict atrial fibrillation using serial 12-lead ECGs based on left atrial remodeling","volume":"13","author":"Choi","year":"2024","journal-title":"J Am Heart Assoc"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1093\/europace\/euad036","article-title":"An artificial intelligence\u2013based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening","volume":"25","author":"Hygrell","year":"2023","journal-title":"EP Europace"},{"key":"B15","doi-asserted-by":"publisher","first-page":"euad354","DOI":"10.1093\/europace\/euad354","article-title":"Single-lead electrocardiogram artificial intelligence model with risk factors detects atrial fibrillation during sinus rhythm","volume":"26","author":"Dupulthys","year":"2024","journal-title":"Europace"},{"key":"B16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.ijcard.2021.10.021","article-title":"Screening for paroxysmal atrial fibrillation in primary care using holter monitoring and intermittent, ambulatory single-lead electrocardiography","volume":"345","author":"Karregat","year":"2021","journal-title":"Int J Cardiol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1161\/CIRCULATIONAHA.121.057014","article-title":"Screening for atrial fibrillation in older adults at primary care visits: VITAL-AF randomized controlled trial","volume":"145","author":"Lubitz","year":"2022","journal-title":"Circulation"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2261-4-12","article-title":"A randomised controlled trial and cost effectiveness study of systematic screening (targeted and total population screening) versus routine practice for the detection of atrial fibrillation in the over 65s: (SAFE)","volume":"4","author":"Swancutt","year":"2004","journal-title":"BMC Cardiovasc Disord"},{"key":"B19","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1161\/CIRCULATIONAHA.114.014343","article-title":"Mass screening for untreated atrial fibrillation","volume":"131","author":"Svennberg","year":"2015","journal-title":"Circulation"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-scale assessment of a smartwatch to identify atrial fibrillation","volume":"381","author":"Perez","year":"2019","journal-title":"N Engl J Med"},{"key":"B21","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1001\/jama.2018.8102","article-title":"Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial","volume":"320","author":"Steinhubl","year":"2018","journal-title":"JAMA"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1161\/CIRCULATIONAHA.117.030583","article-title":"Assessment of remote heart rhythm sampling using the AliveCor heart monitor to screen for atrial fibrillation","volume":"136","author":"Halcox","year":"2017","journal-title":"Circulation"},{"key":"B23","doi-asserted-by":"publisher","first-page":"e222419","DOI":"10.1001\/jamahealthforum.2022.2419","article-title":"Cost-effectiveness of screening for atrial fibrillation using wearable devices","volume":"3","author":"Chen","year":"2022","journal-title":"JAMA Health Forum"}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2025.1612750\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T06:09:38Z","timestamp":1759817378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2025.1612750\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":23,"alternative-id":["10.3389\/fcvm.2025.1612750"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2025.1612750","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,7]]},"article-number":"1612750"}}