The deep learning system achieved AUCs of 0.88, 0.84, and 0.89 for mild, moderate, and severe tricuspid regurgitation respectively in internal validation.
Observational (n=12,473)
Yes
Does an end-to-end deep learning system accurately assess tricuspid regurgitation severity using continuous wave Doppler spectra in adult patients?
A novel deep learning system using continuous wave Doppler spectra can accurately and automatically classify tricuspid regurgitation severity, potentially improving diagnostic efficiency and reducing operator variability.
Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human errors leading to measurement variability, with outcomes critically dependent on the operators' diagnostic expertise. In this study, we present an innovative assessment methodology for evaluating TR severity utilizing an end-to-end deep learning system. This deep learning system comprises a segmentation model of single cardiac cycle TR continuous wave (CW) Doppler spectra and a classification model of the spectra, trained on the TR CW Doppler spectra from a cohort of 11,654 patients. The efficacy of this intelligent assessment methodology was validated on 1500 internal cases and 573 external cases. The receiver operating characteristic (ROC) curves of the internal validation results indicate that the deep learning system achieved the areas under curve (AUCs) of 0.88, 0.84, and 0.89 for mild, moderate, and severe TR, respectively. The ROC curves of the external validation results demonstrate that the system attained the AUCs of 0.86, 0.79, and 0.87 for mild, moderate, and severe TR, respectively. Our study results confirm the feasibility and efficacy of this novel intelligent assessment method for TR severity.
Xie et al. (Mon,) conducted a observational in Tricuspid regurgitation (n=12,473). Deep learning system for TR severity assessment vs. Conventional echocardiographic assessment methods was evaluated on AUC of the deep learning system for predicting TR severity. The deep learning system achieved AUCs of 0.88, 0.84, and 0.89 for mild, moderate, and severe tricuspid regurgitation respectively in internal validation.