TY - GEN
T1 - Prediction of burn healing time using artificial neural networks and reflectance spectrometer
AU - Yeong, Eng Kean
AU - Hsiao, Tzu Chien
AU - Chiang, Huihua Kenny
AU - Lin, Chii Wann
PY - 2004/12/1
Y1 - 2004/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=20844432061&partnerID=8YFLogxK
U2 - 10.1109/APBP.2004.1412321
DO - 10.1109/APBP.2004.1412321
M3 - Conference contribution
AN - SCOPUS:20844432061
SN - 0780386760
T3 - Second Asian and Pacific Rim Symposium on Biophotonics - Proceedings, APBP 2004
SP - 143
BT - Second Asian and Pacific Rim Symposium on Biophotonics - Proceedings, APBP 2004
T2 - Second Asian and Pacific Rim Symposium on Biophotonics, APBP 2004
Y2 - 15 December 2004 through 17 December 2004
ER -