Testing the effectiveness of a polymeric membrane dressing in modulating the inflammation of intact, non-injured, mechanically irritated skin.

We investigated the inflammatory (IL-1 alpha) and thermal (infrared thermography) reactions of healthy sacral skin to sustained, irritating mechanical loading. We further acquired digital photographs of the irritated skin (at the visible light domain) to assess whether infrared imaging is advantageous. For clinical context, the skin status was monitored under a polymeric membrane dressing known to modulate the inflammatory skin response. The IL-1 alpha and infrared thermography measurements were consistent in representing the skin status after 40 min of continuous irritation. Infrared thermography overpowered conventional digital photography as a contactless optical method for image processing inputs, by revealing skin irritation trends that were undetectable through digital photography in the visual light, not even with the aid of advanced image processing. The polymeric membrane dressings were shown to offer prophylactic benefits over simple polyurethane foam in the aspects of inflammation reduction and microclimate management. We also concluded that infrared thermography is a feasible method for monitoring the skin health status and the risk for pressure ulcers, as it avoids the complexity of biological marker studies and empowers visual skin assessments or digital photography of skin, both of which were shown to be insufficient for detecting the inflammatory skin status.

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review.

Significance: As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at providing an in-depth overview of the relevant methodologies of artificial intelligence (AI) and their potential implementation to support these growing needs of wound care and management. Recent Advances: MEDLINE, Compendex, Scopus, Web of Science, and IEEE databases were all searched for new AI methods or novel uses of existing AI methods for the diagnosis or management of hard-to-heal wounds. We only included English peer-reviewed original articles, conference proceedings, published patent applications, or granted patents (not older than 2010) where the performance of the utilized AI algorithms was reported. Based on these criteria, a total of 75 studies were eligible for inclusion. These varied by the type of the utilized AI methodology, the wound type, the medical record/database configuration, and the research goal. Critical Issues: AI methodologies appear to have a strong positive impact and prospects in the wound care and management arena. Another important development that emerged from the findings is AI-based remote consultation systems utilizing smartphones and tablets for data collection and connectivity. Future Directions: The implementation of machine-learning algorithms in the diagnosis and managements of hard-to-heal wounds is a promising approach for improving the wound care delivered to hospitalized patients, while allowing health care professionals to manage their working time more efficiently.