ArticleInternational journal for numerical methods in biomedical engineering2025
Assessing the Impact of Morphological Parameters on the Mechanical Behavior of Synthetic Meshes. A Multivariate Regression Approach.
Article in International journal for numerical methods in biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative finite element modelling of surgical meshes at macro and mesoscale: assessing the role of morphology on mechanical behavior.Medical & biological engineering & computing · 2026Article
- Assessing the Impact of Morphological Parameters on the Mechanical Behavior of Synthetic Meshes. A Multivariate Regression Approach.International journal for numerical methods in biomedical engineering · 2025Article
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5 authors.
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Abstract
The impact of morphological and mechanical parameters of surgical meshes on the healing processes and patient comfort after abdominal repair surgery is widely accepted. However, how the structure of the knitted pattern of synthetic meshes affects the mechanical behavior remains primarily theoretical. The objective of this study was therefore to assess the correlation between these key factors, identifying the most crucial morphological parameters able to support the design of new meshes. In this perspective, morphological parameters related to pore size, shape, and orientation were computed based on high-resolution images using the poreScanner app and the Matlab Image Processing toolbox. Additional parameters such as weight and thickness were measured through high-precision instruments. Concurrently, 12 mechanical parameters were assessed by executing a comprehensive testing protocol. Multivariate regression models were implemented, each using one to five morphological parameters as independent variables and one of the 12 mechanical parameters as dependent variables. A leave-one-out (LOO) validation algorithm was then employed to estimate the models' performance, robustness, and accuracy for potential future predictions. Regression models showed high coefficients of determination (R
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