ReviewClinical research in cardiology : official journal of the German Cardiac Society2022
Machine learning in the detection and management of atrial fibrillation.
Review in Clinical research in cardiology : official journal of the German Cardiac Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.Current cardiology reviews · 2025Pooled it
- Review
- Roof-dependent atrial tachycardia after pulsed field ablation-mechanistic insights and predictive modeling for optimized ablation strategies: an ultra-high-density mapping study.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- [The EHRA/HRS/APHRS/LAHRS/AEPC consensus statement on the management of atrial tachycardias : Implications of the SMART-AT 10-point plan for clinical practice and healthcare structures].Herzschrittmachertherapie & Elektrophysiologie · 2026Article
- Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight.Clinical cardiology · 2026Review
- Rapid thrombogenesis prediction in COVID-19 patients using DNN with data labeling.Scientific reports · 2025Article
- Machine learning model for predicting in-hospital cardiac mortality among atrial fibrillation patients.Scientific reports · 2025Article
- Article
- Artificial intelligence-powered advancements in atrial fibrillation diagnostics: a systematic review.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2025Review
- Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model.BMC cardiovascular disorders · 2025Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.European heart journal. Digital health · 2025Article
- The Efficacy of Artificial Intelligence in the Detection and Management of Atrial Fibrillation.Cureus · 2025Review
- Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations.Frontiers in cardiovascular medicine · 2025Review
- [The 2024 ESC guidelines for management of atrial fibrillation : AF-CARE as new credo].Herzschrittmachertherapie & Elektrophysiologie · 2024Review
- [Everyone over the age of 65 years should wear a smartwatch : Pro and contra].Herzschrittmachertherapie & Elektrophysiologie · 2024Review
- Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism.Scientific reports · 2024Article
- Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation.BMC cardiovascular disorders · 2024Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- Research Concepts and Opportunities for Early-Career Investigators in Cardiac Surgery.The Annals of thoracic surgery · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls. Typical data flow in machine learning applications for atrial fibrillation detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.