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Updated: Sep 23, 2026

Biomechanical Characterization of Human Soft Tissues Using Indentation and Tensile Testing
Published on: December 13, 2016
Toward in vivo assessment of skin mechanical properties for flap design: challenges, methods, and technical pathways
Hui Shen1, Amelia Palacios2, Sofia Deek3
1Dr. Carl D. Clay and H. Jane Clay Department of Mechanical Engineering, The T.J. Smull College of Engineering, Ohio Northern University, Ada, OH, United States.
Abstract:
There is a strong need to quantify skin mechanical properties for flap design in plastic surgery, such as syndactyly reconstruction. Quantitative measurement of skin mechanical properties enables more accurate surgical planning, thereby improving surgical outcomes and reducing complications. Although a large number of methods and devices have been proposed, nearly none of them have been applied in surgical practice. We therefore conducted a comprehensive narrative review of the literature published from 1969 to the present on in vivo skin mechanical property measurement. To identify the factors limiting clinical application, studies using suction, indentation, torsion, stretching, non-contact, and computational approaches were screened and categorized based on their underlying mechanical assumptions and boundary conditions. Three fundamental paradoxes limiting clinical translation were identified: (1) conventional engineering characterization requires destructive testing, which is incompatible with preserving intact skin for surgery; (2) in vivo measurements inevitably capture surrounding tissue boundary constraints rather than intrinsic skin mechanics; and (3) engineering analyses depend on stress-based parameters and skin thickness, whereas thickness is rarely considered in clinical flap planning. Furthermore, inverse numerical models, such as finite element analysis, rely on idealized assumptions that may not fully reflect complex, patient-specific surgical conditions. Rather than attempting to identify a universally accurate conventional measurement device, this review proposes a novel paradigm shift: combining non-invasive microstructural imaging with computer vision and machine learning to predict mechanical transition thresholds (e.g., collagen alignment and tangent modulus) directly from unstretched tissue, with the goal of enabling patient-specific, computational surgical flap planning.

