Longitudinal Validation of a Deep Learning Index for Aortic Stenosis Progression.

Journal: Journal of the American Heart Association
Published Date: Jan 14, 2026

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<0.001) and showed strong correlations with aortic valve maximal velocity (Pearson correlation coefficient, 0.69; P<0.001) and mean pressure gradient (Pearson correlation coefficient, 0.66; P<0.001). Higher baseline DLi-ASc was associated with a faster AS progression rate (P for trend<0.001). Additionally, the annualized change in DLi-ASc, estimated using linear mixed-effect models, correlated strongly with the annualized progression of aortic valve maximal velocity (Pearson correlation coefficient, 0.71, P<0.001) and mean pressure gradient (Pearson correlation coefficient, =0.68; P<0.001). In Fine-Gray competing risk models, baseline DLi-ASc was independently associated with progression to severe AS, even after adjustment for aortic valve maximal velocity or mean pressure gradient (hazard ratios per 10-point increase, 2.38 and 2.80, respectively). CONCLUSIONS: DLi-ASc increased in parallel with AS progression and was independently associated with severe AS progression. These findings support its role as a noninvasive imaging-based digital marker for longitudinal AS monitoring and risk stratification.

Authors

  • Jiesuck Park
    Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea.
  • Jiyeon Kim
    Department of Statistics, Keimyung University, Daegu, Republic of Korea.
  • Yeonyee E Yoon
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).
  • Jaeik Jeon
    CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03 721, South Korea.
  • Seung-Ah Lee
    CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea.
  • Hong-Mi Choi
    Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam.
  • In-Chang Hwang
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).
  • Goo-Yeong Cho
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).
  • Hyuk-Jae Chang
    Department of Cardiology, Yonsei University College of Medicine, Seoul, Republic Of Korea.
  • Jae-Hyeong Park
    College of Medicine, Chungnam National University, Daejun, South Korea.

Keywords

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