Opportunistic automated aortic valve calcification assessment in low-cost, screening CT calcium score exams

Abstract

Aortic valve stenosis (AS) is the most common valvular disease, with a growing impact in the aging population. AS can culminate in heart failure if left untreated. Early treatment with minimally-invasive transcatheter aortic valve replacement (TAVR) is being evaluated in clinical trials. We addressed an unmet clinical need for early detection, referral to echocardiography for AS evaluation, and possible treatments (e.g., lifestyle changes, drugs, or TAVR). As aortic valve calcification (AVC) is typically present in AS, we created a method to detect AVC in low-cost/no-cost non-contrast CT calcium score (CTCS) screening exam images. We developed a multi-task deep network to identify a cylindrical aortic valve region of interest (ROI) and applied Agatston criteria within the ROI to obtain calcifications. Predicted ROIs had good agreement with cardiologists’ labels and sometimes were better, and predicted Agatston scores agreed with cardiologists (r = 1.00, paired t-test p = 0.573, t = 0.57). On a retrospective screening cohort of 2000 + patients, we found that 20.6% of men and 22.2% of women had some degree of AVC. According to guidelines, 3.53% and 9.04%, respectively, would have severe AS. These promising results indicate that further evaluation of this approach is warranted, with the potential for significant public health impact.