Prediction of burn healing time using artificial neural networks and reflectance spectrometer

Eng Kean Yeong, Tzu-Chien Hsiao, Kenny Chiang Huihua, Chii Wann Lin*

*Corresponding author for this work

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

Background: Burn depth assessment is important as early excision and grafting is the treatment of choice for deep dermal burn. Inaccurate assessment causes prolonged hospital stay, increased medical expenses and morbidity. Based on reflected burn spectra, we have developed an artificial neural network to predict the burn healing time. Purpose: Our study is to develop a non-invasive objective method to predict burn-healing time. Methods and materials: Burns less than 20% TBSA was included. Burn spectra taken on the third postburn day using reflectance spectrometer were analyzed by an artificial neural network system. Results: Forty-one spectra were collected. With the newly developed method, the predictive accuracy of burns healed in less than 14 days was 96%, and that in more than 14 days was 75%. Conclusions: Using reflectance spectrometer, we have developed an artificial neural network to determine the burn healing time with 86% overall predictive accuracy.

Original languageEnglish
Pages (from-to)415-420
Number of pages6
JournalBurns
Volume31
Issue number4
DOIs
StatePublished - 1 Jun 2005

Keywords

  • Artificial neural network
  • Burn healing time
  • Reflectance spectrometer

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