Deep operator network models for predicting post-burn contraction

TL;DR

This paper demonstrates that deep operator networks can accurately and efficiently predict long-term post-burn wound contraction, offering a fast surrogate for traditional finite element simulations to aid treatment planning.

Contribution

The study introduces a DeepONet model enhanced with shape information and sine augmentation, achieving high accuracy and significant speedups for predicting post-burn contraction.

Findings

Achieved $R^2$ score of 0.99 indicating high accuracy.

Provided up to 128-fold CPU and 235-fold GPU speedups.

Successfully predicted wound evolution over one year.

Abstract

Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functional impairments and disfigurement. Understanding and predicting the evolution of post-burn wounds is essential for developing effective treatment strategies. Traditional mathematical models, while accurate, are often computationally expensive and time-consuming, limiting their practical application. Recent advancements in machine learning, particularly in deep learning, offer promising alternatives for accelerating these predictions. This study explores the use of a deep operator network (DeepONet), a type of neural operator, as a surrogate model for finite element simulations, aimed at predicting post-burn contraction across multiple wound shapes. A DeepONet was trained on three distinct initial wound shapes, with enhancement made to the architecture by incorporating initial wound shape information and applying sine augmentation to enforce boundary conditions. The performance of the trained DeepONet was evaluated on a test set including finite element simulations based on convex combinations of the three basic wound shapes. The model achieved an $R^2$ score of $0.99$, indicating strong predictive accuracy and generalization. Moreover, the model provided reliable predictions over an extended period of up to one year, with speedups of up to 128-fold on CPU and 235-fold on GPU, compared to the numerical model. These findings suggest that DeepONets can effectively serve as a surrogate for traditional finite element methods in simulating post-burn wound evolution, with potential applications in medical treatment planning.