Clinical Validation of Object Detection Models for AI-Based Pressure Injury Stage Classification

TL;DR

This study shows that AI models, particularly YOLOv7, can accurately classify pressure injuries and improve nursing workflow and education.

Contribution

The study validates the practical use of AI for pressure injury classification in real clinical settings with a mobile application.

Findings

YOLOv7 achieved 93% accuracy for Stage 2 pressure injury classification.

The AI application improved diagnostic accuracy to 87% and reduced assessment time to 1 minute.

Nurses reported 4.0/5 satisfaction and found the tool valuable for education and workflow.

Abstract

Background/Objectives: Pressure injury stage classification was performed using object detection models to address inconsistencies in clinical assessment due to variability in nurses’ experience and education levels. Methods: A dataset of 1282 pressure injury images from a medical institution was used to train and compare five representative architectures, YOLOv5x, YOLOv7, YOLOv8x, YOLOv8n, and YOLOv11x, and Faster R-CNN across Stages 1–4, excluding Deep Tissue Injury and unclassified cases. A mobile application incorporating YOLOv7 was deployed at Eulji University Daejeon Medical Center and tested by 10 nurses over 2 weeks, processing 46 cases. Results: YOLOv7 demonstrated superior performance with mAP@0.5 of 0.97 and mAP@0.5:0.95 of 0.68, achieving 93% accuracy for Stage 2 classification, the most challenging diagnostic category. Clinical validation demonstrated 87% diagnostic accuracy, 4.0/5 user satisfaction, and workflow improvement with assessment time reduced from 4–6 min to 1 min. The application proved valuable as both a diagnostic support tool and educational resource for novice nurses, with zero critical misclassifications recorded. Conclusions: This study establishes the practical utility of AI-based pressure injury classification systems in clinical practice and their potential for enhancing nursing competency and workflow efficiency.

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