JMIR Publications

ABSTRACT

Background:

The global prevalence of pressure injuries is high and can cause severe infections, or death. Accurate staging is vital for effective intervention. Deep learning streamlines pressure injury assessment, enhances efficiency, and yields practical, accurate results. This scoping review summarized research on multi-modal deep learning for intelligent pressure ulcer recognition.

Objective:

It systematized models, training methods, and outcomes to identify the best systems for rapid detection and automated staging of pressure ulcers. Enhancing the timeliness, accuracy, and objectivity of diagnosis is the goal.

Methods:

We searched the following databases and sources: PubMed, the Cochrane Library, IEEE Xplore, and Web of Science. The scoping review was conducted in accordance with the JBI Scoping Review Methodology Group’s guidance and reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Extension for Scoping Reviews guidelines. The study protocol was registered with the International Prospective Registry of Systematic Reviews (PROSPERO) on 12 December 2025 (registration number: CRD420251251573).

Results:

15 articles were included: 26 models were involved, including AlexNet; VGG16; ResNet18; DenseNet121; SE-Swin Transformer; Cascade R-CNN; vision transformer (ViT); ConvNextV2; EfficientNetV2; Meta Former; TinyViT; CCM; BCM; ResNext + wFPN; SE-Inception; Mask-R-CNN; SE-ResNext101; Faster R-CNN; ResNet50; ResNet152; DenseNet201; EfficientNet-B4; YOLOv5; Inception-ResNet-v2; InceptionV3; MobilNetV2. The training methodology for intelligent pressure ulcer recognition models involves establishing an image database, processing images, and constructing the recognition model. Different models exhibit varying accuracy rates in staging pressure ulcers, with overall accuracy fluctuating between 54.84% and 93.71%. The DenseNet121 model achieved the highest recognition accuracy of 93.71%, while VGG16 was the most widely applied. The same model demonstrated significant variations in recognition accuracy across different studies.

Conclusions:

The multi-modal and deep learning-based intelligent recognition model for pressure injuries demonstrates high overall accuracy, enabling rapid automated staging of such injuries. Future research may explore optimized intelligent assistance systems to enhance the accuracy, objectivity, and efficiency of pressure injury diagnosis.