Machine-learning versus traditional scoring methods for prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

BACKGROUND: Atrial fibrillation (AF) recurrence after catheter ablation remains common, with recurrence rates up to 45%. Traditional risk scores demonstrate only modest predictive performance. Machine learning (ML) approaches have shown promise in improving risk stratification by leveraging high-dimensional clinical and imaging data, but direct comparisons with traditional scores remain limited. OBJECTIVE: To compare the discriminative performance of ML models versus traditional risk scores in predicting AF recurrence following catheter ablation. METHODS: MEDLINE, EMBASE, Web of Science, and Cochrane databases were searched to 11 June 2026 for studies reporting head-to-head comparisons of ML and traditional risk models predicting AF recurrence after ablation. The primary outcome was model discrimination, measured by the C-statistic. Random-effects meta-analysis was used to pool discrimination estimates, and meta-regression was performed to compare ML and traditional approaches. Subgroup analysis was conducted to explore the effect of ML model architecture. Methodological quality was assessed using the PROBAST tool. RESULTS: Eleven studies (n = 17,150) were included. ML models demonstrated higher pooled discrimination compared with traditional risk scores (AUC 0.80, 95% CI 0.72-0.86 vs. 0.58, 95% CI 0.53-0.62; QM 18.96, p < 0.001). Subgroup analysis suggested that multimodal fusion models achieved the highest pooled discrimination, followed by neural-network models. Tree-based models did not significantly outperform traditional risk scores in the primary analysis. CONCLUSIONS: ML models showed higher discrimination compared to traditional risk scores in predicting AF recurrence after ablation. Methodological limitations, lack of calibration reporting, and absence of external validation limit immediate clinical adoption.