A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.

Link: https://doi.org/10.1101/2023.09.28.23296234
Authors: Oikonomou, Evangelos K; Holste, Gregory; Yuan, Neal; Coppi, Andreas; McNamara, Robert L; Haynes, Norrisa; Vora, Amit N; Velazquez, Eric J; Li, Fan; Menon, Venu; Kapadia, Samir R; Gill, Thomas M; Nadkarni, Girish N; Krumholz, Harlan M; Wang, Zhangyang; Ouyang, David; Khera, Rohan

Abstract: 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. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression. We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024. DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos. In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.