Yeong Eng-Kean, Hsiao Tzu-Chien, Chiang Huihua Kenny, Lin Chii-Wann
Department of Surgery, Division of Plastic Surgery, National Taiwan University Hospital, Taipei, Taiwan, ROC.
Burns. 2005 Jun;31(4):415-20. doi: 10.1016/j.burns.2004.12.003. Epub 2005 Feb 17.
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.
Our study is to develop a non-invasive objective method to predict burn-healing time.
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.
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%.
Using reflectance spectrometer, we have developed an artificial neural network to determine the burn healing time with 86% overall predictive accuracy.
烧伤深度评估很重要，因为早期切除和植皮是深度真皮烧伤的首选治疗方法。评估不准确会导致住院时间延长、医疗费用增加以及发病率上升。基于反射烧伤光谱，我们开发了一种人工神经网络来预测烧伤愈合时间。
我们的研究旨在开发一种非侵入性的客观方法来预测烧伤愈合时间。
纳入烧伤面积小于20%体表面积的患者。使用反射光谱仪在烧伤后第三天采集的烧伤光谱由人工神经网络系统进行分析。
收集了41个光谱。采用新开发的方法，愈合时间小于14天的烧伤预测准确率为96%，大于14天的为75%。
使用反射光谱仪，我们开发了一种人工神经网络来确定烧伤愈合时间，总体预测准确率为86%。
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