CT-Based Deep Learning Assessment of Aortic Valve Fibrosis and Calcification

BACKGROUND: Computed tomography angiography (CTA) enables assessment of aortic valve tissue composition, providing insights into disease progression and helping predict outcomes in patients undergoing transcatheter aortic valve replacement for severe aortic stenosis (AS). The authors aimed to develop a deep learning system for CTA-based quantification of aortic fibrotic and calcific tissue, validate its agreement with expert manual analysis and histopathology, and evaluate its value for outcome prediction. METHODS: This international, multicenter study included 1,674 patients who underwent CTA at 9 sites. A deep learning model based on the nnU-Net architecture was trained in 468 patients and applied to an external test set of 1,206 patients, including 29 with valve histology and 12 with serial CTA. In 1,165 patients with clinical outcomes, prognostic value was assessed adjusting for clinical, echocardiographic, and procedural covariates. RESULTS: ) had a 2-fold higher risk of death (aHR: 2.0; P < 0.001), whereas patients with dense calcific tissue attenuation (>885 HU) had a 1.5-fold higher risk of death. CONCLUSIONS: Deep learning enables rapid quantification of aortic valve fibrotic and calcific tissue from CTA, demonstrating excellent agreement with both expert CTA and histological assessments. Quantitative analysis of aortic tissue composition provides additional prognostic value for all-cause mortality after transcatheter aortic valve replacement in multicenter external evaluation.