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

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 (h

SUBMITTER: Diao X 

PROVIDER: S-EPMC12612177 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature