Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence

BACKGROUND Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. Howev-er, AF burden is not routinely measured in clinical practice. An arti-ficial intelligence (AI)-based tool could facilitate the assessment of AF burden. OBJECTIVE We aimed to compare the assessment of AF burden per-formed manually by physicians with that measured by an AI-based tool. METHODS We analyzed 7-day Holter electrocardiogram (ECG) re-cordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agree-ment between both techniques by means of Pearson correlation coefficient, linear regression model, and Bland-Altman plot. RESULTS We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81.53%, Pearson correlation coefficient was 0.998. The calibration intercept was-0.001 (95% CI-0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R2 0.995, residual standard error 0.017). Bland-Altman analysis resulted in a bias of-0.006 (95% limits of agreement-0.042 to 0.030). CONCLUSION The assessment of AF burden with an AI-based tool provided very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.