The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.

人工智能 (AI) 算法在 12 导联心电图 (ECG) 中的应用提供了有前景的年龄预测模型。我们探讨了术前 AI-ECG 年龄与实际年龄之间的差距是否可以预测导管消融后心房颤动 (AF) 复发。我们在四个跨国数据集上验证了基于预训练残差网络的年龄预测模型。然后，我们使用来自两个独立 AF 消融队列的接受抗心律失常药物治疗的个体的术前窦性心律心电图来估计 AI-ECG 年龄，这些个体接受了从头 AF 导管消融术。我们根据从四个模型验证数据集中获得的 AI-ECG 年龄差距的平均绝对误差对 AI-ECG 年龄差距进行分类；老年心电图（≥10 岁）和正常心电图年龄（<10 岁）组。在两个房颤消融队列中，与正常心电图年龄组相比，老年心电图与房颤复发风险显着增加相关。这些关联与实际年龄或左心房直径无关。总之，术前 AI-ECG 年龄对于导管消融后 AF 复发具有预后价值。