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A Bayesian finite-element trained machine learning approach for predicting post-burn contraction.

Ginger Egberts1,2, Marianne Schaaphok1, Fred Vermolen2

  • 1Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.

Neural Computing & Applications
|February 7, 2022
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Summary
This summary is machine-generated.

This study introduces a neural network model to predict skin contraction after burn injuries, offering a faster and accurate alternative to traditional methods. The AI tool can simulate healing for over a year, improving patient quality of life.

Keywords:
Feed-forward neural networkMachine learningMedical applicationMonte Carlo simulationsMorphoelasticityPost-burn scar contraction

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Artificial Intelligence in Medicine

Background:

  • Burn injuries significantly impair quality of life due to aesthetic concerns and joint mobility limitations caused by skin contractures.
  • Predicting post-wounding skin evolution is crucial for managing burn injuries, with current mathematical models relying on complex partial differential equations.
  • These computational models are resource-intensive, necessitating more efficient simulation methods.

Purpose of the Study:

  • To investigate the applicability of neural networks (NNs) as a computationally efficient alternative to traditional finite element methods for simulating skin contraction.
  • To develop and validate an NN model capable of predicting long-term skin evolution post-burn injury.
  • To demonstrate the practical utility of the developed NN model through an accessible online medical application.

Main Methods:

  • Development of a neural network model trained on 25 patient and injury-specific input parameters, including skin stiffness.
  • Simulation of skin contraction evolution over a one-year period using the trained neural network.
  • Validation of the NN model's accuracy against established finite element method results.

Main Results:

  • The neural network achieved a high average goodness of fit (R²) of 0.9928 (± 0.0013), indicating excellent agreement with existing methods.
  • A significant computational speed-up factor of 19354X was achieved compared to traditional finite element approaches.
  • The model's applicability was demonstrated via an online medical app incorporating patient age and burn length.

Conclusions:

  • Neural networks offer a highly accurate and dramatically accelerated method for simulating skin contraction following burn injuries.
  • The developed NN model provides a valuable tool for predicting long-term wound healing and can be integrated into clinical decision-making.
  • The online medical application showcases the potential for AI-driven tools to enhance patient care and outcomes in burn management.