Bayesian Inference Framework to Identify Skin Material Properties in vivo From Active Membranes.

Area of Science:

• Biomedical Engineering
• Dermatology
• Computational Mechanics

Background:

• Accurate in vivo characterization of skin mechanical properties is crucial for dermatology and surgery.
• Current noninvasive methods struggle to capture complex, nonlinear skin behaviors.

Purpose of the Study:

• To develop a Bayesian inference framework for noninvasive, in vivo skin biomechanics assessment.
• To infer patient-specific skin properties using active membranes and strain field measurements.

Main Methods:

• Utilized a finite element model of skin-membrane interaction with the Holzapfel-Gasser-Ogden model.
• Developed a data-driven surrogate model using PCA and Gaussian process regression to reduce computational cost.
• Employed Bayesian inference to estimate skin parameters from strain field data.

Main Results:

• Successfully inferred key skin biomechanical parameters, including shear modulus and fiber properties.
• Demonstrated accurate parameter recovery even with moderate noise.
• Showed that multi-frame or multi-membrane observations significantly improve parameter identifiability.

Conclusions:

• The proposed active membrane framework offers a viable, noninvasive method for in vivo skin biomechanics assessment.
• This approach bypasses the need for direct force measurements, relying instead on known membrane properties and strain data.
• The method holds significant potential for improving dermatological diagnostics and surgical planning.