Longitudinal Validation of a Deep Learning Index for Aortic Stenosis Progression

BackgroundAortic stenosis (AS) is a progressive disease requiring timely monitoring and intervention. While transthoracic echocardiography (TTE) remains the diagnostic standard, deep learning (DL)-based approaches offer potential for improved disease tracking. This study examined the longitudinal changes in a previously developed DL-derived index for AS continuum (DLi-ASc) and assessed its value in predicting progression to severe AS. MethodsWe retrospectively analysed 2,373 patients (7,371 TTEs) from two tertiary hospitals. DLi-ASc (scaled 0-100), derived from parasternal long- and/or short-axis views, was tracked longitudinally. The median follow-up duration was 42.8 months (IQR 22.2-75.7 months). ResultsDLi-ASc increased in parallel with worsening AS stages (p for trend <0.001) and showed strong correlations with AV maximal velocity (Vmax) (Pearson correlation coefficients [PCC] = 0.69, p<0.001) and mean pressure gradient (mPG) (PCC = 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 AV Vmax (PCC = 0.71, p<0.001) and mPG (PCC = 0.68, p<0.001). In Fine-Gray competing risk models, baseline DLi-ASc independently predicted progression to severe AS, even after adjustment for AV Vmax or mPG (hazard ratio per 10-point increase = 2.38 and 2.80, respectively) ConclusionDLi-ASc increased in parallel with AS progression and independently predicted severe AS progression. These findings support its role as a non-invasive imaging-based digital marker for longitudinal AS monitoring and risk stratification. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABSO_LIThis is the first study to validate longitudinal changes in a deep learning-derived index (DLi-ASc) for tracking aortic stenosis (AS) progression. C_LIO_LIDLi-ASc increases consistently over time in parallel with worsening AS stages and conventional AS hemodynamic parameters. C_LIO_LIBaseline DLi-ASc independently predicts future severe AS progression, even after adjusting for conventional hemodynamic parameters. C_LI What Are the Clinical Implications?O_LIDLi-ASc provides a quantitative, noninvasive digital marker for monitoring AS progression in routine clinical practice. C_LIO_LIDLi-ASc enables individualized risk stratification and may inform tailored follow-up strategies for patients with AS. C_LIO_LIDLi-ASc may serve as a surrogate marker for future studies evaluating therapeutic interventions to slow AS progression. C_LI