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Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

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Published on: December 8, 2010

AI-Powered Image-Based Assessment of Pressure Injuries Using You Only Look Once Version 8 (YOLOv8) Models.

Mehedi Hasan Tusar¹, Fateme Fayyazbakhsh^1,2,3, Niloofar Zendehdel¹

¹Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA.

Advances in Wound Care

|March 13, 2025

Summary

This study introduces the YOLOv8s deep learning model for accurate pressure injury staging, achieving high precision and recall. The findings offer a robust tool for clinical decision-making in pressure injury assessment.

Keywords:

YOLO artificial intelligence deep learning ensemble model medical image analysis pressure injury simulation-based research

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Deep learning for diagnostics

Background:

Pressure injuries pose a significant clinical challenge.
Accurate staging is crucial for effective treatment and patient outcomes.
Current methods for pressure injury detection and staging can be subjective.

Purpose of the Study:

To enhance pressure injury detection and staging using machine learning.
To evaluate the You Only Look Once version 8 (YOLOv8) deep learning model for this application.
To identify the optimal YOLOv8 variant and optimizer for pressure injury staging.

Main Methods:

A high-quality, publicly available dataset was utilized.
Multiple YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) were evaluated.
Five optimizers (Adam, AdamW, NAdam, RAdam, SGD) were tested with hyperparameter tuning.

Main Results:

YOLOv8s with the AdamW optimizer achieved the highest performance.
Key metrics included 84.16% mean average precision (IoU ≥0.5) and 82.31% recall.
The ensemble model demonstrated strong performance on unseen images.

Conclusions:

YOLOv8s and ensemble models significantly improve pressure injury staging accuracy.
The model demonstrated notable accuracy for challenging stages like Stage 2.
This approach provides a reliable tool for clinical decision-making in pressure injury management.