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Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
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Refinement of an Artificial Intelligence Algorithm for Enhanced Burn Wound Depth Assessment Using Multispectral

Jeffrey E Carter1, Jeffrey W Shupp2, Herb A Phelan1

  • 1LSUHSC-New Orleans Department of Surgery, University Medical Center-New Orleans Burn Unit, New Orleans, LA, United States.

Journal of Burn Care & Research : Official Publication of the American Burn Association
|June 2, 2025
PubMed
Summary

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This study developed a deep learning algorithm using multispectral imaging to assess burn wounds, achieving 89.29% accuracy. Time-since-injury significantly impacts burn assessment accuracy.

Area of Science:

  • Medical imaging
  • Artificial intelligence in medicine
  • Burn wound assessment

Background:

  • Convolutional Neural Networks (CNNs) enable AI in visual fields.
  • Multispectral Imaging (MSI) sensors capture wavelengths beyond visible spectra.
  • Developing a deep learning (DL) algorithm for burn assessment using MSI data.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for burn wound assessment using multispectral imaging.
  • To evaluate the accuracy and effectiveness of CNNs in classifying burn wounds for operative or nonoperative healing.
  • To identify key covariates, such as time-since-injury, influencing burn assessment outcomes.

Main Methods:

  • Prospective study involving 100 adult and 24 pediatric subjects across three burn centers.
Keywords:
artificial intelligenceburn depthconvolutional neural networkdeep learning

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  • Multispectral imaging (MSI) of burn wounds, with data converted to pixel-level for analysis.
  • Training ten CNNs (8 unique DL algorithms and 2 ensemble DL algorithms) using expert-defined ground truth data.

    • Main Results:

    • The most effective CNN achieved an area under the curve of 0.95 (89.29% accuracy, 90.51% sensitivity, 87.22% specificity).
    • Time-since-injury was a significant covariate (P < .0001), with accuracy highest at 3-4 days post-injury (93.5%).
    • The CNN learning curve predicted 94.04% accuracy with future subject enrollment.

    Conclusions:

    • An optimal CNN architecture was identified for burn wound assessment.
    • "Time-since-injury" is a crucial covariate for improving DL algorithm performance.
    • Findings inform the design and power calculations for future algorithm training and validation studies.