Artificial Intelligence-Enabled Staging Classification of Pressure Injuries.

This study aimed to design an artificial intelligence (AI) tool that can more accurately and objectively identify different stages of pressure injuries (PIs). In this study, the authors proposed using AI and computer vision to classify PI images by stage. To this end, the authors implemented a classification network and trained it on a set of PIs images labeled with their stages. This dataset included images from 2 different sources, namely the publicly available Pressure Injury Image Dataset (1091 images), and a private dataset from Koç University Wound Research Laboratory (AY-Lab) (572 images). All images were resized to 224×224 and normalized according to the ImageNet-1K dataset before model input. Various deep learning architectures, including ResNet18, ResNet18-Transformer Encoder Hybrid Model, and DenseNet-121, were used for training and testing. Three-fold cross-validation was used to ensure more robust training and testing. Multiple configurations were tested for each model, and the best-performing configuration was identified. Grad-CAM was applied to visualize attention areas for further evaluation of the model results. After 3-fold cross-validation, ResNet18 outperformed all tested models, achieving an average accuracy of 76.92 ± 0.92% on the 4-class classification task. The model demonstrated the highest precision of 87.35 ± 5.54% for Stage 1 and the lowest precision of 64.72 ± 2.66% for Stage 3. The results of using the proposed computational approach for PI staging are promising. The AI model can automate PI stage classification, making it a valuable tool for clinic experts.