Comprehensive aortic stenosis characterization using multi-view deep learning.

ABSTRACT:

Background and aims

Accurate assessment of aortic stenosis (AS) requires integration of both structural and functional information characterized by visual traits as well as quantitation of gradients. Existing artificial intelligence (AI) models utilize solely either structural or functional information.

Methods

We developed EchoNet-AS, an open-source end-to-end integrated approach combining video based convolutional neural networks to assess valve motion as well as segmentation models to automate the measurement of aortic valve peak velocity to classify AS severity.

Results

EchoNet-AS was trained on 210,193 images from 16,076 studies from Kaiser Permanente Northern California (KPNC) and validated on 1,589 held-out test studies and a temporally distinct cohort of 19,20

SUBMITTER: Ieki H 

PROVIDER: S-EPMC12622131 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature