Tracking early myocardial decline in aortic stenosis with AI-based strain

Abstract Background Aortic stenosis (AS) imposes chronic pressure overload on the left ventricle (LV), leading to progressive myocardial remodeling and, eventually, systolic dysfunction. Identifying early markers of functional deterioration is essential to optimize monitoring and timing of intervention. Purpose To characterize the longitudinal evolution of LV systolic function in patients with AS, identify early predictors of LVEF decline, and assess their prognostic value — with a focus on deep learning-based strain (DLS) metrics. Methods We retrospectively studied 602 patients with severe AS (mean age 77 years, 54% female), all of whom had at least two prior echocardiograms (median 3 studies per patient over 4 years). At the time of severe AS diagnosis, patients were categorized as having low fraction (LF, LVEF 58%). LVEF and DLS values were strong early predictors of future LVEF deterioration, detectable up to 4 years prior to the onset of severe AS. The decision tree model defined three echocardiographic profiles with 89.5% internal validation accuracy: high risk (LVEF