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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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: Bum 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: 41 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
Title of host publicationSecond Asian and Pacific Rim Symposium on Biophotonics - Proceedings, APBP 2004
Pages143
Number of pages1
DOIs
StatePublished - 1 Dec 2004
EventSecond Asian and Pacific Rim Symposium on Biophotonics, APBP 2004 - Taipei, Taiwan
Duration: 15 Dec 200417 Dec 2004

Publication series

NameSecond Asian and Pacific Rim Symposium on Biophotonics - Proceedings, APBP 2004

Conference

ConferenceSecond Asian and Pacific Rim Symposium on Biophotonics, APBP 2004
CountryTaiwan
CityTaipei
Period15/12/0417/12/04

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