Development and calibration of digital twins for human skin growth in tissue expansion

Tissue expansion (TE), an essential technique in reconstructive surgery, leverages the growth of skin in response to stretch. However, human skin growth dynamics have not been evaluated in vivo. Previously, we quantified this process in a porcine model and developed a calibrated computational framework. Here, we create patient-specific finite element (FE) models of skin growth in TE using longitudinal 3D photos collected during TE treatment. These geometries enable Bayesian model calibration, accounting for uncertainties in boundary conditions, mechanical properties, and biological parameters. The framework incorporates prior knowledge from the porcine model as well as literature information on human skin mechanics. The likelihood function assesses alignment between predicted and observed geometries, and predicted and observed skin growth. To efficiently sample the posterior distribution, we use Markov Chain Monte Carlo (MCMC) with Gaussian process surrogates, reducing computational cost. This pipeline is demonstrated in five TE cases. Post-calibration, FE models closely match 3D photos, with errors below 2 mm on average. Notably, Bayesian calibration collapses the critical stretch parameter posterior distribution. This study presents the first in vivo measurement of human skin growth, confirming that FE models accurately capture TE in the clinical setting, and that porcine-derived parameters provide a strong prior for Bayesian calibration in the clinical case. These findings support the development of personalized digital twins for TE, enhancing surgical planning and outcomes.

Statement of significance

Tissue expansion (TE) is widely used in reconstructive surgery, particularly for breast reconstruction and pediatric defect repair. While skin growth has been quantified in animal models, this work provides the first clinical measurement of human skin growth during TE. We employ a Bayesian calibration framework to create personalized finite element (FE) simulations for five TE cases. The initial FE model is constructed from a patient’s 3D photo taken at the start of treatment. Then, uncertainties in mechanical and biological parameters as well as boundary conditions are sampled and the model run. We use Gaussian process surrogates to replace the FE model. Calibration of parameters is done with 3D photos taken longitudinally during TE. This pipeline for skin digital twins can enhance personalized TE procedures, optimizing outcomes and reducing complications.