Key result
Combined FTLE and WSS modeling strongly correlates with leaflet calcification on micro-CT.
Why the study?
Calcific degeneration is the primary failure mode of bioprosthetic aortic valves and a growing concern for polymeric substitutes, necessitating improved prediction methods for calcification risk.
Does a hybrid FTLE and WSS machine learning model accurately predict calcification risk in prosthetic aortic valve designs?
Does a hybrid FTLE and WSS machine learning model accurately predict calcification risk in prosthetic aortic valve designs?
Effect estimate: Spearman correlation ≥0.9
A novel hybrid physics and machine learning model combining structural point coherence and wall shear stress accurately predicts bioprosthetic valve calcification risk, identifying the U-shaped bovine pericardium design as least susceptible.
May guide prosthetic valve geometry and material selection in simulations; leaves open whether findings reduce clinical calcification risk.
Calcific degeneration remains the foremost failure mode of bioprosthetic aortic valves and a mounting concern for polymeric substitutes. Six prosthetic valve configurations, pairing two leaflet scallop geometries (V-shaped D1 and U-shaped D2) with three constitutive materials (elastomer, bovine pericardium and porcine tissue), are simulated throughout systole within a patient-specific curved aorta reconstructed from clinical CT, using a high-fidelity fluid-structure interaction framework. An incremental formulation of Finite-Time Lyapunov Exponents (FTLEs), applied to the leaflet deformation gradients, is introduced as a descriptor of leaflet structural point coherence. With wall shear stress (WSS) derived features, the FTLE field feeds an unsupervised k -means clustering that partitions each leaflet into four calcification risk classes with no fitting to experimental data. Validated against an experimentally calibrated micro-CT calcification intensity map of explanted bovine pericardial leaflets, the combined FTLE and WSS classification attains a Spearman correlation ≥0.9 under near-equal weighting (γ ≈ 0.5). Porcine tissue concentrates strain into localised high-gradient zones whereas the isotropic elastomer spreads strain evenly yet sustains pronounced shear fluctuations through leaflet flutter. The D2 U geometry with bovine pericardium proves least susceptible across both risk dimensions, furnishing a transferable, model-free tool for ranking next-generation valve designs.
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Corso et al. (2026) studied Calcific degeneration of prosthetic aortic valves. Combined FTLE and WSS classification vs. Experimental micro-CT calcification intensity map was evaluated on Correlation with experimental micro-CT calcification intensity map (Spearman correlation ≥0.9). A combined FTLE and WSS machine learning classification of leaflet calcification risk strongly correlated with experimental micro-CT maps (Spearman correlation ≥0.9).
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