Department of Nursing, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Plastic and Reconstructive Surgery, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
J Adv Nurs. 2023 Aug;79(8):3047-3056. doi: 10.1111/jan.15584. Epub 2023 Feb 8.
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.
This was a retrospective observational study using a deep learning model.
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.
We retrospectively analysed the electronic medical records of hospitalized patients between January 2019 and April 2021.
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.
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.
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.
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.
开发一种基于真实照片的压力性损伤分期深度学习模型，并将其与临床护士的表现进行比较，以寻求在临床环境中应用的机会。
这是一项回顾性观察性研究，使用深度学习模型。
整形外科医生和两名伤口护理护士对一组压力性损伤图像进行了标记。我们应用了几种现代卷积神经网络架构，并将其性能与临床护士的表现进行了比较。
我们回顾性分析了 2019 年 1 月至 2021 年 4 月期间住院患者的电子病历。
我们编译和分析了一组 2464 张压力性损伤图像。使用 EfficientNet，在分类压力性损伤图像时，宏 F1 得分为 0.8941，两名经验丰富护士的平均表现为 0.8781。
成功开发了一种用于分期分类压力性损伤图像的深度学习模型，并将模型的性能与经验丰富的护士进行了比较。本研究开发的分类模型有望帮助经验较少的护士或资源匮乏的医疗环境中的护士确定压力性损伤的分期。
我们的深度学习模型可以最大限度地减少护士在评估压力性损伤分期方面的差异。使用现代技术和临床可用性来提高深度学习模型的性能的后续研究将提高压力性损伤患者的护理质量。
患者或公众没有参与我们的研究设计、进行、报告或传播计划，因为这是一项使用电子病历的回顾性研究。
