Work

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

Year: 2025

Type: article

Abstract: 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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-025-02011-4