AI-Powered Image-Based Assessment of Pressure Injuries Using You Only Look once (YOLO) Version 8 Models.

Abstract

Objective: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for precise image analysis. This study explores the application of the You Only Look Once version 8 (YOLOv8) deep learning model for pressure injury staging. Approach: We prepared a high-quality, publicly available dataset to evaluate different variants of YOLOv8 (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and five optimizers (Adam, AdamW, NAdam, RAdam, and stochastic gradient descent) to determine the most effective configuration. We followed a simulation-based research approach, which is an extension of the Consolidated Standards of Reporting Trials (CONSORT) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for dataset preparation and algorithm evaluation. Results: YOLOv8s, with the AdamW optimizer and hyperparameter tuning, achieved the best performance metrics, including a mean average precision at intersection over union ≥0.5 of 84.16% and a recall of 82.31%, surpassing previous YOLO-based models in accuracy. The ensemble model incorporating all YOLOv8 variants showed strong performance when applied to unseen images. Innovation: Notably, the YOLOv8s model significantly improved detection for challenging stages such as Stage 2 and achieved accuracy rates of 0.90 for deep tissue injury, 0.91 for Unstageable, and 0.74, 0.76, 0.70, and 0.77 for Stages 1, 2, 3, and 4, respectively. Conclusion: These results demonstrate the effectiveness of YOLOv8s and ensemble models in improving the accuracy and robustness of pressure injury staging, offering a reliable tool for clinical decision-making.