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Intelligent Prediction of Pressure Injury by Image-Based Feature Variable With Machine Learning.

Xuehua Liu, Chengbin Tang, Lingxiang Guo

Nursing Research

|July 1, 2025

Summary

This summary is machine-generated.

An intelligent system using image analysis and machine learning accurately determines pressure injuries (PIs) without human evaluation. This approach achieves high classification rates, aiding in early detection and preventing wound deterioration.

Keywords:

intelligent prediction machine learning pressure injury

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Area of Science:

Medical Imaging
Machine Learning in Healthcare
Computational Pathology

Background:

Objective pressure injury (PI) assessment is vital for preventing wound deterioration.
Current methods often rely on subjective human sensory evaluation.
Developing automated systems can improve diagnostic accuracy and efficiency.

Purpose of the Study:

To create an image-based intelligent system for pressure injury (PI) determination.
To eliminate reliance on subjective human sensory evaluation for PI diagnosis.
To enhance the objectivity and speed of PI assessment.

Main Methods:

Developed an image-based PI determination system using feature variable extraction and machine learning.
Utilized color and texture features, correlating them with human sensory evaluation methods.
Employed Shapley Additive Explanations and Spearman algorithms to analyze feature contributions and model robustness.
Investigated the impact of sample size and K values on model performance.

Main Results:

A k-nearest neighbor algorithm was used to build PI prediction models.
The optimal model achieved a classification rate of 97.22% on the training set.
The same model demonstrated a 97.08% classification rate on the test set.

Conclusions:

Image-based feature variables combined with machine learning are effective for PI determination.
This approach shows potential for other medical diagnoses requiring visual recognition.
Automated image analysis offers a robust and efficient method for clinical diagnostics.