Neural Network-Based Prediction of Residual Paravalvular Leak in Bicuspid Aortic Valve TAVI Using CT-Derived...

Background/Objectives: Transcatheter aortic valve implantation (TAVI) in patients with bicuspid aortic valve (BAV) remains associated with higher rates of residual paravalvular leak (PVL), which confers a two-fold increase in mortality. Despite procedural optimization including balloon post-dilatation, a subset of patients exhibit residual ≥moderate PVL. Pre-procedural identification of these patients could guide procedural planning. Methods: We retrospectively analyzed 402 BAV patients who underwent TAVI with self-expanding valves and balloon post-dilatation between January 2016 and June 2024. A multi-modal deep learning model (Model B) was developed, integrating a 3D ResNet encoder for computed tomography (CT) imaging features with a multilayer perceptron (MLP) for clinical variables, fused via a cross-attention mechanism. Its performance was compared against a conventional model (Model A) combining clinical variables with manually derived CT measurements. Both models were evaluated on identical test folds using 5-fold stratified cross-validation. Results: Of 402 patients, 36 (9.0%) had residual ≥moderate PVL, associated with significantly larger aortic root dimensions at all anatomical levels and greater aortic valve calcification volume (median 887.6 vs. 559.2 mm³; p = 0.004). Model A achieved a mean AUC of 0.694 (95% CI: 0.596-0.792). Model B achieved a mean AUC of 0.822 (95% CI: 0.680-0.964), with a specificity of 0.971, accuracy of 0.881, and PPV of 0.860, while sensitivity was 0.429, reflecting the limited number of outcome events in this cohort. Conclusions: A multi-modal deep learning model integrating expert-segmented CT imaging with clinical variables demonstrated significantly improved discrimination over the conventional approach in this internal cohort for predicting residual PVL in BAV-TAVI, supporting the integration of segmentation-guided deep learning into pre-procedural TAVI planning. However, given the modest number of outcome events, external validation is required to confirm the generalizability of these findings.