Deep learning-assisted aortic stenosis detection and grading based on multiview versus single-view echocardiography.

ABSTRACT:

Background

Advances in deep learning (DL) have shown promise in automating echocardiogram interpretation, thereby enhancing accuracy and efficiency in clinical practice. However, a fully automated pipeline for aortic stenosis (AS) analysis remains largely unexplored. This study aimed to develop a DL framework to streamline clinical AS assessment.

Methods

A total of 499 AS studies (1,996 echocardiographic view) were selected from 17,436 cases of patients with valvular heart diseases (VHDs) obtained from three hospitals to form training (n=302), validation (n=76), and internal testing (n=121) datasets, while a prospectively collected set of 3,278 consecutive echocardiograms served as a real-world test data set. The DL framework automatically classified echocardiographic views,

SUBMITTER: Yang F 

PROVIDER: S-EPMC12591924 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature