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Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes
02:49

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes

Published on: February 23, 2024

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Iterative refinement of a histologic algorithm for burn depth categorization based on 798 consecutive burn wound

Herb A Phelan1, James H Holmes2, William L Hickerson1

  • 1LSUHSC, New Orleans, USA.

Burns : Journal of the International Society for Burn Injuries
|December 1, 2023
PubMed
Summary

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A new burn biopsy algorithm (BBA-V2) offers improved accuracy in classifying burn wound depth compared to its predecessor (BBA-V1). This simpler version enhances clinical assessment for deep partial and full-thickness burns.

Area of Science:

  • Dermatology
  • Surgical Pathology
  • Medical Imaging

Background:

  • Accurate burn wound depth assessment is critical for treatment decisions.
  • The previous Burn Biopsy Algorithm version 1 (BBA-V1) was developed for burn wound depth categorization.
  • A simpler, updated version, BBA-V2, was developed to improve upon BBA-V1.

Purpose of the Study:

  • To introduce and evaluate a newer, simplified Burn Biopsy Algorithm version 2 (BBA-V2).
  • To compare the classification accuracy of BBA-V2 against BBA-V1.
  • To assess the concordance of BBA-V2 with clinical visual assessment of burn wound depth.

Main Methods:

  • A total of 798 burn wound biopsies were classified using both BBA-V1 and BBA-V2.
  • For surgically treated burns, 4 mm biopsies were taken every 25 cm².
Keywords:
AlgorithmArtificial intelligenceBurn biopsyBurn depthHuman

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  • For non-operative wounds, serial imaging at 72 hours and 21 days post-injury was performed to assess healing.
  • Pixel analysis was used to quantify healing in non-operative wounds.

    • Main Results:

    • BBA-V2 reclassified 21% of biopsies from a non-operative to an operative pathway compared to BBA-V1.
    • BBA-V2 classified burns as 3% superficial partial-thickness (SPT), 67% deep partial-thickness (DPT), and 30% full-thickness (FT), significantly different from BBA-V1 (p < 0.0001).
    • Non-operative wounds initially classified as SPT using BBA-V2 showed 89.6% healing accuracy at 21 days.

    Conclusions:

    • The Burn Biopsy Algorithm version 2 (BBA-V2) demonstrates significantly higher agreement with clinical visual assessment for deep partial and full-thickness burn wounds.
    • BBA-V2 offers a more accurate and potentially simpler method for burn wound depth classification.
    • The improved accuracy of BBA-V2 may lead to more appropriate treatment decisions for burn patients.