biostudies-literatureUnknown6(5)Wadforth B<h4>Aims</h4>Spontaneous cardioversion (SCV) is commonly observed in patients presenting to emergency departments (EDs) with primary atrial fibrillation (AF). Predicting SCV could facilitate timely discharge and avoid costly admissions. We sought to evaluate whether SCV could be predicted using artificial intelligence-enabled electrocardiograms (AI-ECGs) and whether this could produce cost savings.<h4>Methods and results</h4>We recruited patients presenting to EDs with primary AF throughout 2022-23. Patients were excluded if the outcome of their AF episode was unclear, or the ECG was not accessible. Spontaneous cardioversion prediction was attempted using ResNet50, EfficientNet, and DenseNet convolutional neural network (CNN) architectures and subsequently an ensemble learning model. We thEuropean heart journal. Digital health969-978https://www.ebi.ac.uk/biostudies/studies/S-EPMC12450503biostudies-literaturePredicting the spontaneous cardioversion of atrial fibrillation using artificial intelligence-enabled electrocardiography.PMC12450503Strube TKarnon JJenkins EChapman DO'Loughlin LPMitchell LTonchev ILorensini SSalari Shahrbabaki SStrong CGanesan ANGoh JSWadforth BfalsePredicting the spontaneous cardioversion of atrial fibrillation using artificial intelligence-enabled electrocardiography.<h4>Aims</h4>Spontaneous cardioversion (SCV) is commonly observed in patients presenting to emergency departments (EDs) with primary atrial fibrillation (AF). Predicting SCV could facilitate timely discharge and avoid costly admissions. We sought to evaluate whether SCV could be predicted using artificial intelligence-enabled electrocardiograms (AI-ECGs) and whether this could produce cost savings.<h4>Methods and results</h4>We recruited patients presenting to EDs with primary AF throughout 2022-23. Patients were excluded if the outcome of their AF episode was unclear, or the ECG was not accessible. Spontaneous cardioversion prediction was attempted using ResNet50, EfficientNet, and DenseNet convolutional neural network (CNN) architectures and subsequently an ensemble learning model. We th2025-01-01T00:00:00Z2025 Sep2026-06-03T17:29:19.469Z2026-04-29T03:13:27.915ZS-EPMC124505034098500310.1093/ehjdh/ztaf081