In Journal of advanced nursing ; h5-index 55.0
AIMS : To develop a deep learning model for pressure injury stages classification based on real-world photographs and compare its performance with that of clinical nurses to seek the opportunity of its application in clinical settings.
DESIGN : This was a retrospective observational study using a deep learning model.
REVIEW METHODS : A plastic surgeon and two wound care nurses labelled a set of pressure injury images. We applied several modern Convolutional Neural Networks architectures and compared the performances with those of clinical nurses.
DATA SOURCES : We retrospectively analysed the electronic medical records of hospitalized patients between January 2019 and April 2021.
RESULTS : A set of 2464 pressure injury images were compiled and analysed. Using EfficientNet, in classifying pressure injury images, the macro F1-score was calculated to be 0.8941, and the average performance of two experienced nurses was reported as 0.8781.
CONCLUSION : A deep learning model for classifying pressure injury images by stages was successfully developed, and the performance of the model was compared with that of experienced nurses. The classification model developed in this study is expected to help less-experienced nurses or those working in under-resourced healthcare settings determine the stages of pressure injury.
IMPACT : Our deep learning model can minimize discrepancies in nurses' assessment of classifying pressure injury stages. Follow-up studies on improving the performance of deep learning models using modern techniques and clinical usability will lead to improved quality of care among patients with pressure injury.
NO PATIENT OR PUBLIC CONTRIBUTION : Patients or the public were not involved in our research's design, conduct, reporting or dissemination plans because this was a retrospective study that used electronic medical records.
Seo Suryang, Kang Jaeyeon, Eom In Hyang, Song Hyeji, Park Jun Ho, Lee Youngsoo, Lee Haeyoung
2023-Feb-08
artificial intelligence, classification, deep learning, neural networks, nurses, nursing, pressure ulcer
