Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study.

ABSTRACT:

Background and aims

Valvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR).

Methods

The AI-ECG models were developed in a data set of 988 618 ECG and transthoracic echocardiogram pairs from 400 882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care data set from

SUBMITTER: Liang Y 

PROVIDER: S-EPMC12634117 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature