Improving reconstructive surgery design using Gaussian process surrogates to capture material behavior uncertainty.

ABSTRACT: To produce functional, aesthetically natural results, reconstructive surgeries must be planned to minimize stress as excessive loads near wounds have been shown to produce pathological scarring and other complications (Gurtner et al., 2011). Presently, stress cannot easily be measured in the operating room. Consequently, surgeons rely on intuition and experience (Paul et al., 2016; Buchanan et al., 2016). Predictive computational tools are ideal candidates for surgery planning. Finite element (FE) simulations have shown promise in predicting stress fields on large skin patches and in complex cases, helping to identify potential regions of complication. Unfortunately, these simulations are computationally expensive and deterministic (Lee et al., 2018a). However, running a few, well selected

SUBMITTER: Stowers C 

PROVIDER: S-EPMC8087634 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature