Research on real-time detection and staging technology for pressure injuries in critically ill patients based on YOLOv8

Background To develop and validate a real-time detection and staging system for PIs using the YOLOv8 deep learning model. Methods A total of 507 PI images from intensive care unit patients (Jan 2023-Jun 2025) were randomly divided into training (414) and test (93) sets. Images were classified into six stages per NPUAP guidelines ^[1] . Five YOLOv8 versions were developed using transfer learning, with AdamW optimizer and dynamic learning rate adjustment. The best model was evaluated on precision, accuracy, and inference speed. Results This model effectively enhanced the objectivity and accuracy of pressure injury (PI) staging identification. In testing with 93 PI images, YOLOv8l achieved the optimal balance with 0.854 precision and 0.35 fps/img inference speed, outperforming other versions. Additionally, the model demonstrated high prediction accuracy across all six PI stages: all Stage 2, Stage 4, and Unstageable images were correctly predicted; one image each in Stage 1, Stage 3, and Deep Tissue Injury was misclassified. Conclusions For PI staging identification, the PI assessment system built on the YOLOv8l deep learning model demonstrates high accuracy and efficiency, providing reliable support for clinical decision-making, thereby delivering more personalized care to critically ill patients and significantly reducing pressure injury-related healthcare costs.