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

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Items 101–200 of 313

1. Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation
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2. Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation
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3. Continual learning framework for a multicenter study with an application to electrocardiogram
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4. Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12‐Lead ECGs Based on Left Atrial Remodeling
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5. Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study
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6. Association between Anemia Severity and Ischemic Stroke Incidence: A Retrospective Cohort Study
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7. Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
   Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Douglas J. Weber, Ding Xuan Zhao · 2023 · Findings of the Association for Computational Linguistics: EACL
8. The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment
   Hanyang Liang, Han Zhang, Juan Wang, Xing‐hui Shao, Shuang Wu, Si‐qi Lyu, Wei Xu, Lulu Wang, Jiang‐Shan Tan, Jingyang Wang et al. · 2024 · Reviews in Cardiovascular Medicine
9. Classification of ECG Anomaly with Dynamically-biased LSTM for Continuous Cardiac Monitoring
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10. Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset
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11. Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates
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13. Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing
    Sheng‐Feng Sung, Kuan‐Lin Sung, Ru‐Chiou Pan, Pei-Ju Lee, Ya‐Han Hu · 2022 · Frontiers in Cardiovascular Medicine
14. Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs
    Hui‐Wen Yang, Cheng‐Yi Hsiao, Yuqi Peng, Tse-Yu Lin, Lung-Wen Tsai, Chen Lin, Men‐Tzung Lo, Chun-Ming Shih · 2022 · Journal of Personalized Medicine
15. Clinical perspectives on the adoption of the artificial intelligence-enabled electrocardiogram
    Shaan S. Khurshid · 2023 · Journal of Electrocardiology
16. Another piece in the puzzle of atrial fibrillation risk: clinical, genetic, and electrocardiogram-based artificial intelligence
    Shinwan Kany, Patrick T. Ellinor, Shaan S. Khurshid · 2024 · European Heart Journal
17. Evaluating the Risk of Paroxysmal Atrial Fibrillation in Noncardioembolic Ischemic Stroke Using Artificial Intelligence-Enabled ECG Algorithm
    Chang-Ho Han, Oyeon Kwon, Mineok Chang, S. Joo, Yeha Lee, J. Lee, Ji M. Hong, Seong-Joon Lee, D. Yoon · 2022 · Frontiers in Cardiovascular Medicine
18. An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk
    Sushravya M. Raghunath, John M. Pfeifer, Chris R. Kelsey, Arun Nemani, Jeffrey A. Ruhl, Dustin N. Hartzel, Alvaro Ulloa, Linyuan Jing, David P. vanMaanen, Joseph B. Leader et al. · 2022 · Journal of Electrocardiology
19. Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study
    Maximilian Schoels, Laura Krumm, Alexander Nelde, Manuel C. Olma, Christian Hans Nolte, Jan Friedrich Scheitz, Markus G. Klammer, Christoph Leithner, Andreas Meisel, Franziska Scheibe et al. · 2025 · EBioMedicine
20. Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study
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21. Novel interpretable Feature set extraction and classification for accurate atrial fibrillation detection from ECGs
    Ruhi Sharmin, Melissa C. Brindise, Jibin Joy Kolliyil, Brett Meyers, Jiacheng Zhang, Pavlos P. Vlachos · 2024 · Computers in Biology and Medicine
22. Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions
    Iannone, Stacy, Kaur, Amarpreet, Johnson, Kevin B. · 2026 · Journal of General Internal Medicine
23. An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer
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24. Artificial intelligence for precision medicine
    Marie-Elise Martel, Adán José-García, Celine Vens, Maarten De Vos, Vincent Sobanski · 2025 · Therapies
25. Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis
    Mingfen Wu, Kefu Yu, Zhigang Zhao, Bin Zhu · 2024 · Heliyon
26. The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke
    Bill Goh, Sonu M. M. Bhaskar · 2024 · Annals of the New York Academy of Sciences
27. The art of selecting the ECG input in neural networks to classify heart diseases: a dual focus on maximizing information and reducing redundancy
    Elisa Ramírez, Samuel Ruipérez-Campillo, Rubén Casado-Arroyo, José Luís Merino, Julia Elisabeth Vogt, Francisco Castells, José Millet · 2024 · Frontiers in Physiology
28. Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review
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30. Artificial intelligence analysis of the single-lead ECG predicts long-term clinical outcomes
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31. AI ‐ ECG for early detection of atrial fibrillation: First‐year results from a stroke prevention study in Shimizu, Japan
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32. Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study
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33. A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs
    Mohammed T. Al-Bairmani, Mohammadreza Yazdchi, Fahimeh Nasimi · 2026 · Scientific Reports
34. The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation
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35. Many Models, Little Adoption—What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?
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36. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications
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37. ECG-based machine learning model for AF identification in patients with first ischemic stroke
    Chih‐Chieh Yu, Yuqi Peng, Chen Lin, Chia‐Hsin Chiang, Chih‐Min Liu, Yenn‐Jiang Lin, Lian‐Yu Lin, Men‐Tzung Lo · 2024 · International Journal of Stroke
38. Intelligent algorithms powered smart devices for atrial fibrillation discrimination
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39. Role of Artificial Intelligence in Congenital Heart Disease and Interventions
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40. Is machine learning the future for atrial fibrillation screening?
    Pavidra Sivanandarajah, Huiyi Wu, Nikesh Bajaj, Sadia N Khan, Fu Siong Ng · 2022 · Cardiovascular Digital Health Journal
41. Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
    Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael Rosenberg, Douglas J. Weber, Emerson H. Liu, Ding Xuan Zhao · 2023 · IEEE International Conference on Acoustics Speech and Signal Processing
42. Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation
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43. Implications of Artificial Intelligence in Stroke Intervention and Care
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44. Atrial fibrillation detection method combining multi-scale bands and spatio-temporal features of ECG
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45. Short-term atrial fibrillation onset prediction using machine learning
    Jean-Marie Grégoire, Cédric Gilon, François Marelli, Hugues Bersini, Laurent Groben, Thomas Nguyen, Bernard Deruyter, Pascal Godart, STEPHANE G. CARLIER · 2025 · European Heart Journal - Digital Health
46. Artificial Intelligence in the Diagnosis and Management of Atrial Fibrillation
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47. Artificial intelligence capabilities in identifying atrial fibrillation using baseline sinus rhythm ECG : a systematic review
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48. Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial
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49. Precision Medicine for Electrocardiogram Interpretation: Clinical Relevance, Challenges, and Advances
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50. AI-enabled cardiovascular devices: a lifecycle playbook for evidence, change control, and post-market assurance
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51. Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges
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52. Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography
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53. Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset
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54. Predicting Atrial Fibrillation after Ischemic Stroke: Clinical, Genetics, and Electrocardiogram Modelling
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55. High-throughput methods for cardiac cellular electrophysiology studies: the road to personalized medicine
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56. Higher-Order Spectral Analysis Combined with a Convolution Neural Network for Atrial Fibrillation Detection-Preliminary Study
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57. Development and Validation of a Deep-Learning Model to Detect CRP Level from the Electrocardiogram
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58. Predicting incident atrial fibrillation in sinus rhythm: more than just trusting the ‘black box’
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67. Artificial intelligence-estimated electrocardiographic sex as a recurrence predictor after atrial fibrillation catheter ablation
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