Track synthesis, materials and catalysis as it is published.
Search papers, labs, and topics across Lattice.
This paper introduces a Constitutive Artificial Neural Network (CANN) framework to discover a hyperelastic strain-energy function (SEF) for venous tissue, addressing limitations of existing models in capturing characteristic venous mechanical behavior across different vein types. By incorporating structurally motivated terms and employing rigorous pruning techniques, the CANN framework successfully identified a compact, eight-term SEF that significantly outperforms established models in fitting biaxial mechanical data from saphenous, jugular, and inferior vena cava veins. This work establishes a data-driven approach for constitutive model discovery in soft biological tissues, offering a more accurate representation of venous mechanics for clinical applications.
A data-driven CANN framework discovers an eight-term hyperelastic strain-energy function that accurately models diverse venous biomechanics, outperforming existing constitutive models across multiple vein types.
Veins are commonly subjected to altered mechanical loading in clinical settings such as vein grafting, arteriovenous fistula creation, and tissue-engineered vascular graft implantation, making accurate constitutive models of venous tissue clinically important. Venous mechanical behavior differs characteristically from that of arteries, and existing constitutive models struggle to simultaneously fit the circumferential and axial stress-stretch responses across vein types. Here, we present a constitutive artificial neural network (CANN) framework that discovers a compact, interpretable hyperelastic constitutive model from biaxial mechanical data across three types of veins (saphenous, jugular, and inferior vena cava). The framework incorporates an expanded candidate function library with structurally motivated terms representing progressive fiber recruitment and fiber-family interactions. Sobol sensitivity analysis, correlation-based pruning, and response-level family collapse were then used to reduce the discovered basis to a compact strain-energy density function (SEF). The resulting eight-term SEF achieved significantly higher fitting accuracy than established venous constitutive models across all three types of veins $(R^2>0.9, p<0.01)$. These findings establish a framework for constitutive model discovery in veins and propose a candidate constitutive form for further validation across larger, independent cohorts.