Artificial Intelligence in the Management and Treatment of Burns: A Systematic Review and Meta-analyses

  • Bilal Gani Taib
    Corresponding author: Bilal G Taib. Queen Elizabeth Hospital: Queen Elizabeth Hospital Birmingham, Department of Burns & Plastic Surgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, United Kingdom, +441216272000
    Burns and Plastic Surgery Department, Queen Elizabeth Hospital, Birmingham, UK
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  • A Karwath
    Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK

    Health Data Research UK Midlands Site, Birmingham, UK

    University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK
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  • K Wensley
    Burns and Plastic Surgery Department, Queen Elizabeth Hospital, Birmingham, UK
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  • L Minku
    School of Computer Science, University of Birmingham, Birmingham, UK
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  • G.V. Gkoutos
    Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK

    Health Data Research UK Midlands Site, Birmingham, UK

    University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK

    NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK
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  • N Moiemen
    College of Medical and Dental Sciences, University of Birmingham, UK

    Centre for Conflict Wound Research, Scar Free Foundation, Birmingham, UK

    NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, UK
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Published:November 22, 2022DOI:


      Introduction and Aim

      Artificial Intelligence (AI) is already being successfully employed to aid the interpretation of multiple facets of burns care. In light of the growing influence of AI this systematic review and diagnostic test accuracy meta-analyses aims to appraise and summarise the current direction of research in this field.


      A systematic literature review was conducted of relevant studies published between 1990 to 2021 yielding 35 studies. 12 studies were suitable for a Diagnostic Test Meta-Analyses.


      The studies generally focussed on burn depth (Accuracy 68.9%-95.4%, Sensitivity 90.8% Specificity 84.4%), burn segmentation (Accuracy 76.0%-99.4%, Sensitivity 97.9% and specificity 97.6%) and burn related mortality (Accuracy >90%-97.5% Sensitivity 92.9% and specificity 93.4%). Neural networks were the most common machine learning algorithm utilised in 69% of the studies. The QUADAS-2 tool identified significant heterogeneity between studies.


      The potential application of AI in the management of burns patients is promising, especially given its propitious results across a spectrum of dimensions, including burn depth, size, mortality, related sepsis, and acute kidney injuries. The accuracy of the results analysed within this study are comparable to current practices in burns care.


      The application of AI in the treatment and management of burns patients, as a series of point of care diagnostic adjuncts, is promising. Whilst AI is a potentially valuable tool a full evaluation of its current utility and potential is limited by significant variations in research methodology and reporting.

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