Research Appraisals

Journal of global health

Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals

BACKGROUND: Accurate assessment of pressure injury staging is essential for guiding appropriate care, reducing patient suffering, alleviating the healthcare burden, and improving quality of life. We aimed to develop a deep learning-based model for pressure injury recognition and to translate the best-performing model into a preliminary smartphone application. METHODS: We conducted a multicentre retrospective study using pressure injury images collected from 59 hospitals. We applied to the data set three artificial intelligence-based image analysis models, Mask R-convolutional neural network (CNN) with ResNet-18, Mask R-CNN with Swin Transformer, and Segmenting Objects by Locations, version 2. We assessed model performance using standard evaluation metrics, including precision-recall curves, average recall (AR), average precision (AP), and mean average precision (mAP). The model achieving the best overall performance was selected for subsequent translation into a smartphone-based tool. RESULTS: We included a total of 1903 pressure injury images, with 1713 used for model training and 190 for validation. Among the models evaluated, Mask R-CNN with Swin Transformer demonstrated the highest overall performance (mAP = 0.894, AP50 = 0.900, AP = 0.757, AR100 = 0.792), surpassing the corresponding metrics of the other two models. The model, based on Mask R-CNN with a Swin Transformer, was integrated into a smartphone for preliminary translation. CONCLUSIONS: The proposed deep learning-based system showed promising performance in pressure injury staging and may provide meaningful support for clinical decision-making. Future studies should focus on further validation and refinement using larger and more diverse data sets to enhance their applicability in routine clinical practice.