Development and application of an intelligent pressure injury assessment system using AI image recognition

Abstract

Background Pressure injuries are a significant concern in clinical settings, requiring accurate assessment to prevent complications. Traditional assessment methods are often subjective and time-consuming. Objective This study aimed to develop and evaluate an AI-based intelligent system for assessing pressure injuries, focusing on improving accuracy and efficiency compared to traditional methods. Methods The study involved 108 ICU patients, divided into control and experimental groups. The control group used traditional assessment methods, while the experimental group used an AI-based system with deep learning algorithms which is built upon a convolutional neural network (CNN). The accuracy, efficiency, and integration of the AI system with electronic medical records were analyzed. Results The AI system achieved an accuracy of 90%, outperforming traditional methods which had an accuracy of 81.2%. The system also significantly reduced the assessment time, improving the overall efficiency of pressure injury evaluation. Conclusion The AI-based system demonstrated superior accuracy and efficiency in pressure injury assessment, offering a valuable tool for clinical use. Further research is needed to expand the system's application to other types of wounds.