Monitoring and risk assessment of pressure ulcer and injuries, using 3D modelling and neural networks

Abstract

This paper summarizes the current results, open problems and research directions of the monitoring and risk assessment methods of pressure and shear wounds (PU, PI), which occur in significant numbers in patients treated in inpatient wards and can also be interpreted as nosocomial harm using IT solutions. The article examines the healthcare - particularly nursing-professional -, economic and considerations with an IT approach at the frontier of science, with a multi-aspect approach, and analyzes the relevance of further research based on a literature review.

The article reviews international practices focusing on the topic, the practice of the domestic healthcare system, and its further development opportunities. The research can be aligned with a previous research program, which prepared a situation assessment and formulated proposals by reviewing management aspects and policy decisions - covering the period 2019-2023. Several domestic and international publications have been published on the conceptual foundations of the research.

The aim of this publication is to summarize the currently available IT tools and methods suitable for risk assessment and monitoring of pressure ulcers in the area of ​​chronic wound assessment and care, with a solution focus, and to determine further research and development directions based on international practice, which aim at domestic implementation.

The method of this research: search-word literature review, summary of results, display of the field topology. In the study, based on methods, technologies and software solutions with a scientific literature background, recommendations are formulated that could be possible solutions in domestic care practice, and their introduction is recommended for consideration. We should focus primarily on mobile solutions that can be used at the patient's bedside, are easy for healthcare professionals to learn and use, and are capable of determining the condition of wounds with acceptable accuracy (>90%).