Construction of an Artificial Intelligence-assisted System for Automatic Detection of Pressure Injury Based on the YOLO Neural Network

Background With the aging population, the incidence of pressure injury (PI) is gradually increasing. This not only severely impacts the quality of life for patients but also increases healthcare expenditures. However, the early detection and accurate staging of PI heavily depend on specialized training. Objective To construct and validate an artificial intelligence model for the automatic detection and staging of PI aimed at enhancing the real-time nature, accuracy, and objectivity of PI diagnostics. Methods A total of 693 PI images from the electronic management system of pressure ulcers at Changshu No.1 People's Hospital were selected from January 2021 to February 2024, the images were randomly divided into a training set (551 images) and a test set (142 images), and categorized into six stages according to National Pressure Ulcer Advisory Panel (NPUAP) guidelines: StageⅠ (154 images), StageⅡ (188 images), StageⅢ (160 images), StageⅣ (82 images), deep tissue injury (57 images), and unstageable (52 images).