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


OBJECTIVE
Our study is to develop a non-invasive objective method to predict burn-healing time.


METHODS
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.
Yeong
Eng-Kean
EK
Department of Surgery, Division of Plastic Surgery, National Taiwan University Hospital, Taipei, Taiwan, ROC.
Hsiao
Tzu-Chien
TC
Chiang
Huihua Kenny
HK
Lin
Chii-Wann
CW
D000293:Adolescent; D000328:Adult; D000368:Aged; D002056:Burns / Q000473:pathology* / Q000628:therapy; D002648:Child; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012542:Scattering, Radiation; D013053:Spectrophotometry / Q000379:methods; D014945:Wound Healing