A hybrid physics-based and machine learning approach combining structural point coherence and wall shear stress accurately predicted prosthetic valve calcification risk (Spearman correlation 0.98).
Does a hybrid physics-based and machine learning approach accurately predict calcification risk in prosthetic aortic valves?
A novel hybrid physics-based and machine learning approach accurately predicts calcification risk in prosthetic aortic valves, providing a transferable design tool for evaluating next-generation candidates.
Effect estimate: Spearman rank correlation 0.98, Cohen's weighted kappa 0.91
Calcific degeneration remains the primary failure mode of bioprosthetic aortic valves and a growing concern for emerging polymeric alternatives. In this study, six prosthetic valve configurations combining two leaflet geometries (V-shaped scallop D1V and U-shaped scallop D2U) with three constitutive material models (synthetic elastomer, bovine pericardium and porcine tissue) are simulated throughout systole within a patient-specific curved aorta reconstructed from clinical CT data using a high-fidelity fluid-structure interaction framework. An incremental formulation of Finite-Time Lyapunov Exponents (FTLE) applied to the leaflet deformation gradients is introduced as a novel descriptor of structural point coherence on the leaflet surface. Together with wall shear stress (WSS) derived features, the FTLE field is fed into an unsupervised k-means clustering algorithm that partitions each leaflet into four calcification risk classes without any fitting to experimental data. Validation against an experimentally calibrated calcification intensity map from micro-CT of explanted bovine pericardial valves yields a Spearman rank correlation of 0.98 and a Cohen's weighted kappa of 0.91 when the FTLE- and WSS-based classifications are combined, with an approximately equal optimal weighting. The analysis reveals that porcine tissue concentrates strain into localised high-gradient zones whereas the isotropic elastomer distributes strain uniformly yet generates elevated shear fluctuations through sustained leaflet flutter. The D2U leaflet geometry paired with bovine pericardium emerges as the least susceptible configuration across both risk dimensions. By coupling leaflet structural point coherence with haemodynamic shear in a model-free framework, the proposed methodology provides a transferable design tool for ranking and evaluating next-generation prosthetic valve candidates with respect to calcification susceptibility.
Corso et al. (Fri,) conducted a other in Prosthetic aortic valve calcification. Hybrid physics-based and machine learning approach vs. Experimentally calibrated calcification intensity map was evaluated on Validation of calcification risk classification (Spearman rank correlation 0.98, Cohen's weighted kappa 0.91). A hybrid physics-based and machine learning approach combining structural point coherence and wall shear stress accurately predicted prosthetic valve calcification risk (Spearman correlation 0.98).