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Songqun Huang

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Background: Asymptomatic recurrences of atrial fibrillation (AF) are common after ablation of AF. Objective: We aimed to analyze the performance of the mobile ECG device using artificial intelligence (AI) algorithm in detection of AF after ablation. Method: A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). Monitoring data was collected and analyzed. The ECGs collected by BigThumb were compared using the automated AF detection algorithm, AI algorithm and cardiologists’ manual review. Subsequent changes in adherence on oral anticoagulation of patients were also recorded. Result: We studied 218 patients (109 in BT Group, 109 in TF Group). After a follow-up of 345.4±60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P=0.0163), with more adherence on oral anticoagulation in BT Group (P=0.0052). The participants in the BT Group recorded 26133 ECGs during the follow-up, among which 3299 (12.6%) were diagnosed as AF by cardiologists’ manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%). Conclusion: We found that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence on oral anticoagulation. Artificial intelligence algorithm improves the accuracy of ECG diagnosis.