Development of a decision-support system for biomechanical skin characterization using impact analysis

Pathologies induce variations of the mechanical properties of biological tissues. The principle of the Impact-Based Analysis Method (IBAM) is to analyze the force signal resulting from an instrumented hammer impacting a punch that is in contact with the soft tissue being characterized. Based on a simple dedicated signal processing technique, it is possible to assess the mechanical properties of soft tissues such as elasticity, viscosity, and nonlinearity. In vitro (using soft tissue phantoms) and in silico (numerical and analytical modeling) studies have demonstrated the effectiveness of IBAM in characterizing superficial soft tissues (around 1–10 mm). We have studied in details the volume of interest investigated by the IBAM, as well as its stiffness and spatial sensitivities. Artificial intelligence models have been considered to enhance our understanding of the signal and to predict the Young's modulus phantoms. Experiments with murine models allowed to evaluate stiffness variations in skin samples affected by cutaneous neurofibromas or during the wound-healing process. The IBAM is a simple, real-time, and low-cost way to characterize the skin mechanically. This research paves the way for developing a decision-support device for clinical diagnoses regarding various dermatological pathologies or to personalize skincare solutions for customers.