Bayesian calibration of a computational model of tissue expansion based on a porcine animal model.

ABSTRACT: Tissue expansion is a technique used clinically to grow skin in situ to correct large defects. Despite its enormous potential, lack of fundamental knowledge of skin adaptation to mechanical cues, and lack of predictive computational models limit the broader adoption and efficacy of tissue expansion. In our previous work, we introduced a finite element model of tissue expansion that predicted key patterns of strain and growth which were then confirmed by our porcine animal model. Here we use the data from a new set of experiments to calibrate the computational model within a Bayesian framework. Four 10×10cm² patches were tattooed in the dorsal skin of four 12 weeks-old minipigs and a total of six patches underwent successful tissue expander placement and inflation to 60cc for exp

SUBMITTER: Han T 

PROVIDER: S-EPMC8678288 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature