ABSTRACT
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.
ABSTRACT
BACKGROUND: Several studies have been conducted to analyze the role social networks play in communication between patients and health professionals. However, there is a shortage of studies in relation to communication among primary health professionals, in a professional context, using the various mobile phone apps available. OBJECTIVE: The objective of our study was to explore mobile phone social networking app use among primary health care professionals for work-related purposes, by comparing the most widely used apps in the market. METHODS: We undertook a cross-sectional study using an anonymous Web survey among a convenience sample of 1635 primary health care professionals during August and September 2017. RESULTS: Of 483 participants in the survey, 474 (98.1%, 95% CI 97.1%-99.4%) were health professionals who commonly accessed social networking sites and 362 (74.9%, 95% CI 71.1%-78.8%) accessed the sites in a work-related context. Of those 362 respondents, 219 (96.7%, 95% CI 94.8%-98.5%) preferred WhatsApp for both personal and professional uses. Of the 362 respondents who used social networking sites in a work-related context, 276 (76.2%, 95% CI 71.9%-80.6%) rated social networking sites as useful or very useful to solve clinical problems, 261 (72.1%, 95% CI 67.5%-76.7%) to improve their professional knowledge, and 254 (70.2%, 95% CI 65.5%-74.9%) to speed up the transmission of clinical information. Most of them (338/362, 94.8%, 95% CI 92.5%-97.0%) used social networking sites for interprofessional communications, and 204 of 362 (56.4%, 95% CI 51.2%-61.5%) used them for pharmacological-related consultations. CONCLUSIONS: Health professionals frequently accessed social networking sites using their mobile phones and often for work-related issues. This trend suggests that social networking sites may be useful tools in primary care settings, but we need to ensure the security of the data transfer process to make sure that social networking sites are used appropriately. Health institutions need to increase information and training activities to ensure the correct use of these tools.
