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<p>Tricuspid regurgitation (TR) remains underdiagnosed due to the lack of effective screening tools. We developed a self-distilled and pre-trained transformer model for detecting TR (SPEED-TR) from electrocardiography. The model was trained using 466,149 electrocardiogram-echocardiogram pairs from 291,673 patients and validated in one internal (63,925 patients) and two external cohorts (44,951 and 21,300 patients). SPEED-TR accurately detected moderate-to-severe TR in the hold-out set (AUROC 0.945, NPV 0.983; specificity 0.973) and maintained stable performance in multi-center testing sets (AUROCs 0.939–0.943; NPVs 0.978–0.988). Three thresholds enabled SPEED-TR severity grading: none (0–0.008), mild (0.008–0.255), moderate (0.255–0.755), and severe (0.755–1), achieving accuracies of 0.749 (hold-out), 0.730 (internal), 0.775 and 0.726 (external), with overall accuracy of 0.744. SPEED-TR remained robust in patients with 1 to ≥3 risk factors and 1 to ≥2 valvular diseases. SPEED-TR demonstrated potential as a screening tool and may provide reference for TR severity assessment.</p><p></p>

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SPEED-TR: a self-distilled and pre-trained transformer model for enhanced ECG detection of tricuspid regurgitation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Xiaolin Diao,
  • Wei Xu,
  • Huaibing Cheng,
  • Ya Zhou,
  • Yang Liu,
  • Yanni Huo,
  • Jianli Lu,
  • Jinghan Huang,
  • Jia He,
  • Fang Liu,
  • Zhihui Cao,
  • Xue Zhang,
  • Wei Zhao,
  • Xiaohan Fan

摘要

Tricuspid regurgitation (TR) remains underdiagnosed due to the lack of effective screening tools. We developed a self-distilled and pre-trained transformer model for detecting TR (SPEED-TR) from electrocardiography. The model was trained using 466,149 electrocardiogram-echocardiogram pairs from 291,673 patients and validated in one internal (63,925 patients) and two external cohorts (44,951 and 21,300 patients). SPEED-TR accurately detected moderate-to-severe TR in the hold-out set (AUROC 0.945, NPV 0.983; specificity 0.973) and maintained stable performance in multi-center testing sets (AUROCs 0.939–0.943; NPVs 0.978–0.988). Three thresholds enabled SPEED-TR severity grading: none (0–0.008), mild (0.008–0.255), moderate (0.255–0.755), and severe (0.755–1), achieving accuracies of 0.749 (hold-out), 0.730 (internal), 0.775 and 0.726 (external), with overall accuracy of 0.744. SPEED-TR remained robust in patients with 1 to ≥3 risk factors and 1 to ≥2 valvular diseases. SPEED-TR demonstrated potential as a screening tool and may provide reference for TR severity assessment.