• Yuan Wang, Zuo Ke, Zhiyou He, Xiang Chen, Yu Zhang, Peizhen Xie, Tao Li, Jiao Zhou, Fangfang Li, Canqun Yang, Pihong Zhang, Chun Huang, and Lu Kai.
• College of Computer Science and Technology, National Defense University of Science and Technology, Changsha, Hunan, China.
• Burns. 2020 Dec 1; 46 (8): 1829-1838.

IntroductionEarly judgment of the depth of burns is very important for the accurate formulation of treatment plans. In medical imaging the application of Artificial Intelligence has the potential for serving as a very experienced assistant to improve early clinical diagnosis. Due to lack of large volume of a particular feature, there has been almost no progress in burn field.Methods484 early wound images are collected on patients who discharged home after a burn injury in 48 h, from five different levels of hospitals in Hunan Province China. According to actual healing time, all images are manually annotated by five professional burn surgeons and divided into three sets which are shallow(0-10 days), moderate(11-20 days) and deep(more than 21 days or skin graft healing). These ROIs were further divided into 5637 patches sizes 224 × 224 pixels, of which 1733 shallow, 1804 moderate, and 2100 deep. We used transfer learning suing a Pre-trained ResNet50 model and the ratio of all images is 7:1.5:1.5 for training:validation:test.ResultsA novel artificial burn depth recognition model based on convolutional neural network was established and the diagnostic accuracy of the three types of burns is about 80%.DiscussionThe actual healing time can be used to deduce the depth of burn involvement. The artificial burn depth recognition model can accurately infer healing time and burn depth of the patient, which is expected to be used for auxiliary diagnosis improvement.Copyright © 2020 Elsevier Ltd and ISBI. All rights reserved.

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