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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
Shaan Khurshid, Samuel Friedman, Christopher Reeder, et al.
Circulation (2021) Vol. 145, Iss. 2, pp. 122-133
Open Access | Times Cited: 198

Showing 1-25 of 198 citing articles:

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives
Xiaoyu Sun, Yuzhe Yin, Qiwei Yang, et al.
European journal of medical research (2023) Vol. 28, Iss. 1
Open Access | Times Cited: 62
Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association
Antonis A. Armoundas, Sanjiv M. Narayan, Donna K. Arnett, et al.
Circulation (2024) Vol. 149, Iss. 14
Open Access | Times Cited: 59
Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset
Jiewei Lai, Huixin Tan, Jinliang Wang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 50
Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference
Dominik Linz, Jason G. Andrade, Elena Arbelo, et al.
EP Europace (2024) Vol. 26, Iss. 4
Open Access | Times Cited: 38
Artificial Intelligence for Cardiovascular Care—Part 1: Advances
Pierre Elias, Sneha S. Jain, Timothy J. Poterucha, et al.
Journal of the American College of Cardiology (2024) Vol. 83, Iss. 24, pp. 2472-2486
Closed Access | Times Cited: 25
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
Georgios Petmezas, Leandros Stefanopoulos, Vassilis Kilintzis, et al.
JMIR Medical Informatics (2022) Vol. 10, Iss. 8, pp. e38454-e38454
Open Access | Times Cited: 57
Atrial fibrillation and stroke: State-of-the-art and future directions
Sandra Elsheikh, Andrew Hill, Greg Irving, et al.
Current Problems in Cardiology (2023) Vol. 49, Iss. 1, pp. 102181-102181
Open Access | Times Cited: 42
The digital journey: 25 years of digital development in electrophysiology from an Europace perspective
Emma Svennberg, Enrico G. Caiani, Nico Bruining, et al.
EP Europace (2023) Vol. 25, Iss. 8
Open Access | Times Cited: 37
Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias
Art Schuermans, Caitlyn Vlasschaert, Victor Nauffal, et al.
European Heart Journal (2023) Vol. 45, Iss. 10, pp. 791-805
Open Access | Times Cited: 30
Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
Joshua Mayourian, William La Cava, Akhil Vaid, et al.
Circulation (2024) Vol. 149, Iss. 12, pp. 917-931
Closed Access | Times Cited: 16
Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management
Evan D. Muse, Eric J. Topol
Cell Metabolism (2024) Vol. 36, Iss. 4, pp. 670-683
Closed Access | Times Cited: 14
Artificial-intelligence-based risk prediction and mechanism discovery for atrial fibrillation using heart beat-to-beat intervals
Fan Lin, Peng Zhang, Yuting Chen, et al.
Med (2024) Vol. 5, Iss. 5, pp. 414-431.e5
Open Access | Times Cited: 14
Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?
Adrian Petzl, Gilbert Jabbour, Julia Cadrin‐Tourigny, et al.
EP Europace (2024) Vol. 26, Iss. 8
Open Access | Times Cited: 12
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
Özgün Turgut, Philip Müller, Paul Hager, et al.
Medical Image Analysis (2025) Vol. 101, pp. 103451-103451
Closed Access | Times Cited: 1
Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model
Youfu He, Yu Zhou, Yu Qian, et al.
Frontiers in Cardiovascular Medicine (2025) Vol. 11
Open Access | Times Cited: 1
Clinician-artificial intelligence collaboration: A win-win solution for efficiency and reliability in atrial fibrillation diagnosis
Peng Zhang, Fan Lin, Fei Ma, et al.
Med (2025), pp. 100668-100668
Open Access | Times Cited: 1
Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy
Paschalis Karakasis, Panagiotis Theofilis, Μarios Sagris, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 8, pp. 2627-2627
Open Access | Times Cited: 1
Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation
David Harmon, Ojasav Sehrawat, Maren Maanja, et al.
Arrhythmia & Electrophysiology Review (2023) Vol. 12
Open Access | Times Cited: 22
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation
Neal Yuan, Grant Duffy, Sanket S. Dhruva, et al.
JAMA Cardiology (2023) Vol. 8, Iss. 12, pp. 1131-1131
Open Access | Times Cited: 21
Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
Matteo Gadaleta, Patrick Harrington, Eric Barnhill, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 21
Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry
Konrad Pieszko, Jarosław Hiczkiewicz, Katarzyna Łojewska, et al.
European Heart Journal (2023) Vol. 45, Iss. 1, pp. 32-41
Open Access | Times Cited: 19
Adopting artificial intelligence in cardiovascular medicine: a scoping review
Hisaki Makimoto, Takahide Kohro
Hypertension Research (2023) Vol. 47, Iss. 3, pp. 685-699
Closed Access | Times Cited: 18
Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging
Konrad Pieszko, Aakash Shanbhag, Ananya Singh, et al.
npj Digital Medicine (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 17
Perioperative Patients With Hemodynamic Instability: Consensus Recommendations of the Anesthesia Patient Safety Foundation
Michael J. Scott
Anesthesia & Analgesia (2023)
Open Access | Times Cited: 17
Pathophysiology and clinical relevance of atrial myopathy
Michiel Tubeeckx, Gilles W. De Keulenaer, Hein Heidbüchel, et al.
Basic Research in Cardiology (2024) Vol. 119, Iss. 2, pp. 215-242
Closed Access | Times Cited: 8
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