Artificial intelligence in the management and treatment of burns: a systematic review

Francisco Serra E Moura; Kavit Amin; Chidi Ekwobi

doi:10.1093/burnst/tkab022

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Artificial intelligence in the management and treatment of burns: a systematic review

更新时间：2022-06-14

- Artificial intelligence in the management and treatment of burns: a systematic review
- Burns & Trauma Vol. 9, Issue 1, (2021)
- 作者机构：
  
  1. Department of Plastic Surgery
  2. Norfolk and Norwich University Hospital
  3. Manchester University NHS Foundation Trust
  4. Lancashire Teaching Hospitals NHS Foundation Trust, Royal Preston Hospital
- 作者简介：
- 基金信息：
- DOI：10.1093/burnst/tkab022
  CLC：
- Published：2021，
  
  Published Online：19 August 2021，
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Francisco Serra E Moura, Kavit Amin, Chidi Ekwobi, Artificial intelligence in the management and treatment of burns: a systematic review, Burns & Trauma, Volume 9, 2021, tkab022, https://doi.org/10.1093/burnst/tkab022
DOI：

Francisco Serra E Moura, Kavit Amin, Chidi Ekwobi, Artificial intelligence in the management and treatment of burns: a systematic review, Burns & Trauma, Volume 9, 2021, tkab022, https://doi.org/10.1093/burnst/tkab022 DOI：

摘要

Abstract

BackgroundArtificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies.MethodsA systematic search was carried out on MEDLINE

Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications

key features

algorithms

outcomes and validation methods.ResultsA total of 46 observational studies were included for review. Assessment of burn depth (n = 26)

support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application

algorithm and validation tool used

respectively.ConclusionAI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.

关键词

Keywords

Artificial intelligenceMachine learningComputer visionBurnNeural networks

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