{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T18:17:41Z","timestamp":1777918661694,"version":"3.51.4"},"reference-count":33,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cardiovasc electrophysiol"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>We aimed to construct an artificial intelligence\u2010enabled electrocardiogram (ECG) algorithm that can accurately predict the presence of left atrial low\u2010voltage areas (LVAs) in patients with persistent atrial fibrillation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study included 587 patients with persistent atrial fibrillation who underwent catheter ablation procedures between March 2012 and December 2023 and 942 scanned images of 12\u2010lead ECGs obtained before the ablation procedures were performed. Artificial intelligence\u2010based algorithms were used to construct models for predicting the presence of LVAs. The DR\u2010FLASH and APPLE clinical scores for LVA prediction were calculated. We used a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis to evaluate model performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The data obtained from the participants were split into training (<jats:italic>n<\/jats:italic>\u2009=\u2009469), validation (<jats:italic>n<\/jats:italic>\u2009=\u200958), and test sets (<jats:italic>n<\/jats:italic>\u2009=\u200960). LVAs were detected in 53.7% of all participants. Using ECG alone, the deep learning algorithm achieved an area under the ROC curve (AUROC) of 0.752, outperforming both the DR\u2010FLASH score (AUROC\u2009=\u20090.610) and the APPLE score (AUROC\u2009=\u20090.510). The random forest classification model, which integrated a probabilistic deep learning model and clinical features, showed a maximum AUROC of 0.759. Moreover, the ECG\u2010based deep learning algorithm for predicting extensive LVAs achieved an AUROC of 0.775, with a sensitivity of 0.816 and a specificity of 0.896. The random forest classification model for predicting extensive LVAs achieved an AUROC of 0.897, with a sensitivity of 0.862, and a specificity of 0.935.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The deep learning model based exclusively on ECG data and the machine learning model that combined a probabilistic deep learning model and clinical features both predicted the presence of LVAs with a higher degree of accuracy than the DR\u2010FLASH and the APPLE risk scores.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jce.16373","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T05:06:42Z","timestamp":1721970402000},"page":"1849-1858","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An artificial intelligence\u2010enabled electrocardiogram algorithm for the prediction of left atrial low\u2010voltage areas in persistent atrial fibrillation"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2920-3384","authenticated-orcid":false,"given":"Yirao","family":"Tao","sequence":"first","affiliation":[{"name":"Department of Cardiology, Beijing Chaoyang Hospital Capital Medical University Beijing China"},{"name":"Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital Capital Medical University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"HeartVoice Medical Technology Hefei China"},{"name":"HeartRhythm\u2010HeartVoice Joint Laboratory Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Cardiology Hebei Yanda Hospital Hebei Hebei Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0552-8125","authenticated-orcid":false,"given":"Yanjiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Chaoyang Hospital Capital Medical University Beijing China"},{"name":"Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital Capital Medical University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Chaoyang Hospital Capital Medical University Beijing China"},{"name":"Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital Capital Medical University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Chi","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Beijing Chaoyang Hospital Capital Medical University Beijing China"},{"name":"Heart Center and Beijing Key Laboratory of Hypertension, Beijing Chaoyang Hospital Capital Medical University Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijia","family":"Geng","sequence":"additional","affiliation":[{"name":"HeartVoice Medical Technology Hefei China"},{"name":"HeartRhythm\u2010HeartVoice Joint Laboratory Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhimin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Cardiology Heart Rhythm Cardiovascular Hospital Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenda","family":"Hong","sequence":"additional","affiliation":[{"name":"National Institute of Health Data Science Peking University Beijing China"},{"name":"Health Science Center of Peking University Institute of Medical Technology Beijing 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