A fully convolutional neural network for the quantification of mitral regurgitation in echocardiography.

ABSTRACT:

Background

Mitral regurgitation (MR) is the most common form of valvular heart disease (VHD), and the accurate assessment of MR severity is critical for clinical management. However, the quantitative assessment of MR is intricate and time-consuming, posing challenges for physicians in ensuring the precision of the results. Thus, our objective was to create an automated and reproducible artificial intelligence (AI) system. This study aimed to assist physicians in grading MR severity using color Doppler echocardiograms through the implementation of a fully convolutional neural network (FCN).

Methods

A retrospective cohort was established comprising 433 patients diagnosed with MR based on clinical criteria. Following screening, 269 patients met the inclusion criteria for the st

SUBMITTER: Zhong L 

PROVIDER: S-EPMC11652043 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature