Introduction
Complete heart block (CHB), or third-degree atrioventricular block, is a critical cardiac conduction disorder characterized by the complete dissociation of atrial and ventricular activity. CHB can precipitate ventricular standstill, recurrent syncope, and sudden cardiac death, posing substantial morbidity and mortality risks if undetected or untreated. Electrocardiography (ECG) remains the cornerstone diagnostic modality for conduction system disease, yet current ECG-based risk stratification—primarily relying on the identification of bifascicular block—provides limited sensitivity and predictive accuracy for incident CHB. Recent advances in artificial intelligence (AI) within cardiology have demonstrated AI-enhanced ECG (AI-ECG) capabilities to detect various subclinical cardiac abnormalities, heralding transformative potential in prognostication and early diagnosis. Against this backdrop, the study by Sau et al. introduces AIRE-CHB, an AI-ECG-based risk estimator designed to predict incident CHB, aiming to address the limitations inherent in conventional ECG interpretation and improve clinical decision-making.
