Circulation:January 6, 2026:Circulation:January 6, 2026 Deep Learning–Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation

This research introduces 3DRECON-QT, a sophisticated artificial intelligence system designed to enhance the safety of patients taking specific heart medications by monitoring their cardiac rhythm through a single-lead device. By using deep learning to reconstruct comprehensive spatial data from a limited signal, the model can accurately identify dangerous QT prolongation, a condition that significantly increases the risk of life-threatening arrhythmias. The study demonstrates that this technology achieves guideline-level measurement accuracy, effectively performing as well as standard 12-lead hospital equipment even in outpatient settings. Ultimately, this innovation provides a continuous surveillance strategy that could prevent sudden cardiac events by allowing doctors to intervene the moment a patient’s heart rhythm becomes unstable.