RESUMO
BACKGROUND: Skin ulcers are an important cause of morbidity and mortality everywhere in the world and occur due to several causes, including diabetes mellitus, peripheral neuropathy, immobility, pressure, arteriosclerosis, infections, and venous insufficiency. Ulcers are lesions that fail to undergo an orderly healing process and produce functional and anatomical integrity in the expected time. In most cases, the methods of analysis used nowadays are rudimentary, which leads to errors and the use of invasive and uncomfortable techniques on patients. There are many studies that use a convolutional neural network to classify the different tissues in a wound. To obtain good results, the network must be trained with a correctly labeled data set by an expert in wound assessment. Typically, it is difficult to label pixel by pixel using a professional photo editor software, as this requires extensive time and effort from a health professional. OBJECTIVE: The aim of this paper is to implement a new, fast, and accurate method of labeling wound samples for training a neural network to classify different tissues. METHODS: We developed a support tool and evaluated its accuracy and reliability. We also compared the support tool classification with a digital gold standard (labeling the data with an image editing software). RESULTS: The obtained comparison between the gold standard and the proposed method was 0.9789 for background, 0.9842 for intact skin, 0.8426 for granulation tissue, 0.9309 for slough, and 0.9871 for necrotic. The obtained speed on average was 2.6, compared to that of an advanced image editing user. CONCLUSIONS: This method increases tagging speed on average compared to an advanced image editing user. This increase is greater with untrained users. The samples obtained with the new system are indistinguishable from the samples made with the gold standard.
