Longitudinal Validation of a Deep Learning Index for Aortic Stenosis Progression

Journal of the American Heart Association 2026 Open access

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Authors: Jiesuck Park, Jiesuck Park, Jiyeon Kim, Yeonyee E. Yoon, Jaeik Jeon, Seung‐Ah Lee, Hong‐Mi Choi, In‐Chang Hwang, Goo‐Yeong Cho, Hyuk‐Jae Chang, Jae‐Hyeong Park, Jae‐Hyeong Park
DOI: 10.1161/jaha.125.045179
Cited by: 4
References: 20

Abstract

Background Aortic stenosis (AS) is a progressive disease requiring timely monitoring and intervention. While transthoracic echocardiography remains the diagnostic standard, deep learning–based approaches offer the potential for improved disease tracking. This study examined the longitudinal changes in a previously developed deep learning–derived index for AS continuum (DLi‐ASc) and assessed its prognostic association with progression to severe AS. Methods We retrospectively analyzed 2373 patients (7371 transthoracic echocardiographies) from 2 tertiary hospitals. DLi‐ASc (scaled 0–100), derived from parasternal long‐axis and short‐axis views, was tracked longitudinally. The median follow‐up duration was 42.8 (interquartile range, 22.2–75.7) months. Results DLi‐ASc increased in parallel with worsening AS stages ( P for trend

Fields of Study & Subjects

Cardiac Valve Diseases and Treatments Cardiovascular Function and Risk Factors Coronary Interventions and Diagnostics

Publication details

Journal / source
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Volume
15
Issue
2
Pages
e045179–e045179
Publication date
14 January 2026
Language
EN