Artificial Intelligence‐Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices

BACKGROUND: Artificial intelligence (AI)-enhanced electrocardiography (ECG) has been developed to detect paroxysmal atrial fibrillation (AF) from sinus rhythm ECGs (SR-ECGs). For broader applicability, model development across institutions and ECG systems is essential. METHODS: We developed an AI-enhanced ECG model using SR-ECGs from Tokai University (n = 172,613; Nihon Kohden [NK] system) and The Cardiovascular Institute (n = 19,170; GE MUSE system). AF-labeled SR-ECGs were defined as recordings within 31 days of an AF episode, while SR-labeled SR-ECGs were those with ≥ 1095 days of AF-free follow-up. Three datasets were constructed: Dataset 1 (Tokai University, all departments, NK), Dataset 2 (Tokai University, Cardiology Department, NK), and Dataset 3 (The Cardiovascular Institute, Cardiology Department, MUSE). We developed five models: scratch models (S1-S3) trained on Datasets 1-3, and fine-tuned models (F1, F2) trained on Datasets 1 and 2 after pretraining on Dataset 3. Models were evaluated using A1-A3 (same as Datasets 1-3) and B1-B3, which differed in ECG resolution and compression (B1: original MUSE, B2: MUSE-NK intermediate, B3: NK-converted). RESULTS: Model F2, fine-tuned on homogeneous datasets from cardiology departments, showed consistently high performance (AUC: A1 = 0.885, A2 = 0.829, A3 = 0.845). Model F1, fine-tuned on heterogeneous datasets, demonstrated lower performance (AUC: A1 = 0.837, A2 = 0.726, A3 = 0.660). Model performance was consistent across different ECG format variants (B1-B3). CONCLUSION: Fine-tuning on homogeneous data improved performance and generalizability, whereas heterogeneous data led to reduced performance. ECG system format differences had minimal impact on model accuracy.