Aplicativo para smartphone de gerenciamento do cuidado aos indivíduos com lesão por pressão

Abstract

Introduction: nurses play an essential role in assisting the person with pressure ulcer (PU), and one of the steps in care planning is to extract characteristics of the tissue composition of the wound bed. However, in practice, judgment based on individual ability to visually assess injuries has been shown to be subjective and imprecise. Objectives: to develop a smartphone app for segmentation and automatic classification of the tissues present in the PU bed through the processing of digital images. Methods: applied research whose final result is the technological production of a first version of Wound Manager (GeFe App) for smartphone. It followed the model of Systematic Instructional Design consisting of three stages: analysis, design and development. The acquisition of 20 sacral and trochanteric LPP images occurred from november 2018 to january 2019, and provided the color characteristics of the wound bed. Eight nurses working in the evaluation and treatment of wounds in SES / DF appreciated the images identifying the granulation tissue, epithelization, eschar and slough. The results contributed to evaluate the performance of the developed technique. Results: The overall accuracy of the GeFe App in the identification of granulation, liquefaction and coagulation was 75%. Intraobserver independence was verified in the endpoints of the measurements performed. Developed products: an app for smartphones was developed, running on Android device. GeFe App has three main interfaces: patient, wound and report. In Patient functionality there is a built-in data collection instrument. For the wound module, a pattern recognition algorithm was developed using artificial neural network Self Organizing Maps, in which the contours of the lesion are automatically delineated and the tissues present in the wound bed are identified. In the end, reports are generated archiving the qualitative and semi-quantitative information recorded. Conclusions: The GeFe App obtained promising results such the technique should support nurses to evaluate tissue composition precisely, an essential step in the diagnosis and treatment of PU.