A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

ABSTRACT:

Importance

Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization.

Objective

To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression.

Design, setting, and participants

This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline

SUBMITTER: Oikonomou EK 

PROVIDER: S-EPMC10999005 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature