Development of a pressure ulcer stage determination system for community healthcare providers using a vision transformer deep learning model.

This study reports the first steps toward establishing a computer vision system to help caregivers of bedridden patients detect pressure ulcers (PUs) early. While many previous studies have focused on using convolutional neural networks (CNNs) to elevate stages, hardware constraints have presented challenges related to model training and overreliance on medical opinions. This study aimed to develop a tool to classify PU stages using a Vision Transformer model to process actual PU photos. To do so, we used a retrospective observational design involving the analysis of 395 images of different PU stages that were accurately labeled by nursing specialists and doctors from 3 hospitals. In the pressure ulcer cluster vision transformer (PUC-ViT) model classifies the PU stage with a mean ROC curve value of 0.936, indicating a model accuracy of 97.76% and F1 score of 95.46%. We found that a PUC-ViT model showed higher accuracy than conventional models incorporating CNNs, and both effectively reduced computational complexity and achieved low floating point operations per second. Furthermore, we used internet of things technologies to propose a model that allows anyone to analyze input images even at low computing power. Based on the high accuracy of our proposed model, we confirm that it enables community caregivers to detect PUs early, facilitating medical referral.