用视觉变压器从心电图识别左室收缩功能不全，准确率高且可解释。
A Vision Transformer for ECG-Based Detection of Left Ventricular Systolic Dysfunction Across Multiple Clinical Sites
- 训练视觉变压器逐搏分析12导联心电图，自动识别射血分数≤40%的患者。
- 在外部验证集上达到AUROC 0.88、敏感性81.2%、阴性预测值97.8%。
- 模型对不同人群表现稳定，且注意力聚焦于QRS波，具备临床可解释性。
左室射血分数降低常无症状，多在心力衰竭进展后才被发现。心电图虽常规记录却未被充分利用，因射血分数降低无单一诊断波形。本研究从头训练一个视觉变压器集成模型，基于12导联心电图逐搏分析，检测射血分数≤40%的情况，数据来自三个美国医疗系统的七个临床中心，共10,142名患者。在三个独立地理区域的外部验证队列（4,092名患者，真实患病率8.72%）中，模型取得AUROC 0.88（95% CI 0.86–0.89）、敏感性81.2%、特异性81.0%、阴性预测值97.8%。敏感性在性别、种族、民族及合并症亚组中保持稳定，而老年人、房颤或心肌病患者特异性较低。逐搏注意力图显示模型持续关注QRS波而非P波，具备可解释性。结果表明，常规心电图有望作为大规模筛查工具，帮助识别需进一步做超声心动图评估射血分数的患者。
原文摘要 · Abstract (English)
Reduced left ventricular ejection fraction (LVEF) is frequently asymptomatic and often detected only after advanced heart failure develops. Electrocardiograms are recorded routinely yet underused for this condition, because reduced LVEF has no single diagnostic waveform. We trained an ensemble of vision transformers from scratch to detect reduced LVEF ($\leq$40%) from 12-lead ECGs, analyzing each heartbeat individually, using 10,142 patients across seven sites in three US health systems. In a held-out external cohort of 4,092 patients from three geographically independent US clinical sites at a real-world reduced-LVEF prevalence of 8.72%, the model achieved an AUROC of 0.88 (95% CI 0.86-0.89), sensitivity 81.2%, specificity 81.0%, and negative predictive value 97.8%. Sensitivity remained high across sex, race, ethnicity, and comorbidity subgroups, while specificity was lower in older patients and those with atrial fibrillation or cardiomyopathy. Beat-level attention maps provided interpretability into the model's predictions, showing consistent focus on the QRS complex rather than the P wave. These findings support the potential of routine ECGs as a scalable first-pass triage step to identify patients who should undergo echocardiography for reduced ejection fraction across diverse patient populations.
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