Deformation Derived Parameters for Automatic Classification of Aortic Stenosis

Abstract

Abstract The timing of valvular manipulation in aortic stenosis (AS) is challenging for asymptomatic patients and is based on reduced ejection fraction (EF). The routinely echocardiographic EF measurement is insensitive to subtle myocardial changes and is also dependent on left ventricular (LV) geometry. Various speckle-tracking echocardiography (STE) derived parameters were found valuable for detecting early LV dysfunction in AS, but only the global longitudinal strain (GLS) is guided due to a lack of robustness. We propose a novel machine-learning-based model, trained over global layer-specific STE parameters for automatic classification of AS. The dataset includes 82 AS patients with severe stenosis, 96 chest pain subjects, and 319 healthy volunteers. The proposed model outperformed with an area under the curve (AUC) of 0.97 for separating between AS patients and healthy volunteers, compared to 0.88 and 0.82 for EF and conventional GLS, respectively. For separating between AS patients and chest pain subjects, the model’s AUC was 0.95, compared to 0.9 and 0.55 for EF and conventional GLS, respectively.