Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
Casey Stowers, Taeksang Lee, Ilias Bilionis, Arun Gosain, Adrian, Buganza Tepole

TL;DR
This paper develops Gaussian process surrogate models to efficiently predict stress in skin flaps during reconstructive surgery, accounting for material uncertainty, and optimizes flap orientation to minimize complications.
Contribution
It introduces a novel application of GP surrogates for predicting and optimizing skin flap behavior under uncertain material properties in reconstructive surgery.
Findings
GP surrogates accurately predict stress and strain in skin flaps.
Fiber orientation significantly influences strain field variations.
Optimized flap orientations can reduce mechanical stress and potential complications.
Abstract
Excessive loads near wounds produce pathological scarring and other complications. Presently, stress cannot easily be measured by surgeons in the operating room. Instead, surgeons rely on intuition and experience. 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 complex cases, helping to identify potential regions of complication. Unfortunately, these simulations are computationally expensive and deterministic. However, running a few, well-selected FE simulations allows us to create Gaussian process (GP) surrogate models of local cutaneous flaps that are computationally efficient and able to predict stress and strain for arbitrary material parameters. Here, we create GP surrogates for the advancement, rotation, and transposition flaps. We then use the…
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Taxonomy
MethodsGaussian Process
