Key result
AI applied to sinus rhythm ECGs predicts 1-year AF onset with an AUC of ~0.85.
Why the study?
AF increases the risk of stroke, heart failure, and death, requiring early diagnosis and anticoagulation, but is often asymptomatic and difficult to detect early.
Does an artificial intelligence model applied to 12-lead ECG during sinus rhythm detect paroxysmal atrial fibrillation in Japanese individuals?
Cohort (n=30,467)
No
Does an artificial intelligence model applied to 12-lead ECG during sinus rhythm detect paroxysmal atrial fibrillation in Japanese individuals?
Effect estimate: AUC 0.846
This study aims to develop and validate an AI model for detecting paroxysmal atrial fibrillation from 12-lead ECGs during sinus rhythm in a Japanese population.
【目的】心房細動（atrial fibrillation，AF）は脳梗塞、心不全や死亡のリスクを増加させることから、早期の診断と抗凝固療法の導入が必要となるが、AFは無症状のことも多く、早期発見が難しい。そこで、本研究は日本人を対象とし、AFの早期発見のため非発作時の心電図から発作性AFをスクリーニングするための人工知能（artificial intelligence，AI）開発を行い、本AIの臨床の場における有効性について検証することを目的とした。
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Takeuchi et al. (2026) conducted a cohort in Atrial fibrillation (n=30,467). Artificial Intelligence (AI) model for 12-lead ECG was evaluated on Prediction of atrial fibrillation onset within 1 year from sinus rhythm ECG (Pre-AF) (AUC 0.846). An AI model predicting atrial fibrillation onset within 1 year from 12-lead ECGs during sinus rhythm achieved an AUC of 0.846 with 44.0% sensitivity and 95.7% specificity in a Japanese cohort.
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