This systematic review compared machine learning models with traditional risk scores for predicting atrial fibrillation recurrence after catheter ablation. Four databases were searched to June 2026; eleven studies covering 17,150 patients were pooled by random-effects meta-analysis of the C-statistic. Machine learning gave a pooled discrimination of 0.80 (95% CI 0.72-0.86) against 0.58 (95% CI 0.53-0.62) for traditional scores. Multimodal fusion models discriminated best, then neural networks; tree-based models did not significantly beat the traditional scores. Calibration was largely unreported and no model was externally validated. A PROBAST appraisal found the included studies methodologically weak.
Why it is interesting: Recurrence after ablation is common and the existing scores discriminate little better than chance. The pooled gap is large, but no model has been externally validated.
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