Abstract 4369530: Automated Aortic Valve Calcification Scoring: Multicenter External Validation of a Deep Learning Algorithm

Abstract

Introduction/Background: Aortic valve calcification (AVC) quantification is recommended by guidelines as an imaging biomarker for aortic stenosis (AS) severity and progression yet remains underreported on routine chest computed tomography (CT). With nearly 20 million non-gated chest CTs performed annually in the U.S., automated AVC detection offers potential for opportunistic AS screening without additional radiation exposure or cost. Research Question/Hypothesis: We hypothesized that deep learning methods can accurately quantify AVC from non-gated, non-contrast chest CT scans with performance comparable to expert assessment. Methods/Approach: We developed a convolutional neural network to automatically detect and quantify AVC from non-gated chest CTs. The algorithm was trained and validated on 1,807 imaging studies across 8 large health systems in the U.S. and Brazil from 2021 to 2024. Model performance was evaluated on a holdout set of 239 CT studies from 33 sites across three U.S. geographic regions. The reference standard consisted of manual segmentations independently verified by at least two board-certified radiologists. Performance was evaluated by sensitivity, specificity, and Pearson correlation between algorithm-estimated and ground truth Agatston scores. Subgroup analyses across age categories, sex, geographic regions, CT manufacturers, and technical parameters were conducted. Results/Data: The deep learning algorithm demonstrated high correlation with expert reference standards (Pearson r = 0.99; 95% CI, 0.98-0.99; P <.001). Bland-Altman analysis showed minimal bias with mean difference of 5.2 AU (95% CI, -7.6 to 17.9 AU) and standard deviation of 99.9 AU. For detecting moderate-to-severe AS (>125 AU for females, >275 AU for males), sensitivity was 0.92 (95% CI, 0.80-0.97) and specificity was 0.98 (95% CI, 0.95-0.99). For severe AS (>600 AU for females, >1100 AU for males), sensitivity was 0.91 (95% CI, 0.62-0.98) and specificity was 1.00 (95% CI, 0.98-1.00). Performance remained consistent across demographic subgroups and CT technical parameters. Conclusion: This automated deep learning algorithm accurately quantifies AVC from routine chest CT scans with performance comparable to experts. Implementation into existing radiology workflows may enable opportunistic AS screening, potentially facilitating earlier identification and timely intervention. Prospective studies are needed to determine whether automated AVC screening improves clinical outcomes.