Medicine

Satomi Hamada, Miki Amemiya, Mie Ochida, S. Tao, I. Kawamura, T. Sasano

2026.2.1Journal of Arrhythmia

DOI: 10.1002/joa3.70280

tlooto Summary

This work evaluated the reproducibility and effect of recording condition on the estimation of AF risk using an electrocardiography (ECG) machine equipped with an AI‐based program.

Abstract

Although artificial intelligence (AI) has been developed to identify patients with paroxysmal atrial fibrillation (PAF) during sinus rhythm, information on its variability remains limited. We evaluated the reproducibility and effect of recording condition on the estimation of AF risk using an electrocardiography (ECG) machine equipped with an AI‐based program.

Citation format

HAMADA, Satomi, et al. Temporal variability and influence of measurement conditions of ai‐based atrial fibrillation risk estimation. Journal of Arrhythmia, 2026, 42(1): e70280.