Jiang Xiaoqiong, Wang Yu, Wang Yuxin, Zhou Min, Huang Pan, Yang Yufan, Peng Fang, Wang Haishuang, Li Xiaomei, Zhang Liping, Cai Fuman
College of Nursing, Wenzhou Medical University, Wenzhou, China.
Medical Engineering Office, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Br J Dermatol. 2022 Oct;187(4):571-579. doi: 10.1111/bjd.21665. Epub 2022 Jun 13.
It is challenging to detect pressure injuries at an early stage of their development.
To assess the ability of an infrared thermography (IRT)-based model, constructed using a convolution neural network, to reliably detect pressure injuries.
A prospective cohort study compared validity in patients with pressure injury (n = 58) and without pressure injury (n = 205) using different methods. Each patient was followed up for 10 days.
The optimal cut-off values of the IRT-based model were 0·53 for identifying tissue damage 1 day before visual detection of pressure injury and 0·88 for pressure injury detection on the day visual detection is possible. Kaplan-Meier curves and Cox proportional hazard regression model analysis showed that the risk of pressure injury increased 13-fold 1 day before visual detection with a cut-off value higher than 0·53 [hazard ratio (HR) 13·04, 95% confidence interval (CI) 6·32-26·91; P < 0·001]. The ability of the IRT-based model to detect pressure injuries [area under the receiver operating characteristic curve (AUC) , 0·98, 95% CI 0·95-1·00] was better than that of other methods.
The IRT-based model is a useful and reliable method for clinical dermatologists and nurses to detect pressure injuries. It can objectively and accurately detect pressure injuries 1 day before visual detection and is therefore able to guide prevention earlier than would otherwise be possible. What is already known about this topic? Detection of pressure injuries at an early stage is challenging. Infrared thermography can be used for the physiological and anatomical evaluation of subcutaneous tissue abnormalities. A convolutional neural network is increasingly used in medical imaging analysis. What does this study add? The optimal cut-off values of the IRT-based model were 0·53 for identifying tissue damage 1 day before visual detection of pressure injury and 0·88 for pressure injury detection on the day visual detection is possible. Infrared thermography-based models can be used by clinical dermatologists and nurses to detect pressure injuries at an early stage objectively and accurately.
在压力性损伤发展的早期阶段进行检测具有挑战性。
评估基于卷积神经网络构建的红外热成像（IRT）模型可靠检测压力性损伤的能力。
一项前瞻性队列研究使用不同方法比较了有压力性损伤患者（n = 58）和无压力性损伤患者（n = 205）的有效性。每位患者随访10天。
基于IRT的模型用于在肉眼检测到压力性损伤前1天识别组织损伤的最佳截断值为0.53，在肉眼可检测到压力性损伤当天进行检测的最佳截断值为0.88。Kaplan-Meier曲线和Cox比例风险回归模型分析表明，在肉眼检测前1天，当截断值高于0.53时，压力性损伤风险增加了13倍[风险比（HR）13.04，95%置信区间（CI）6.32 - 26.91；P < 0.001]。基于IRT的模型检测压力性损伤的能力[受试者操作特征曲线下面积（AUC），0.98，95% CI 0.95 - 1.00]优于其他方法。
基于IRT的模型是临床皮肤科医生和护士检测压力性损伤的一种有用且可靠的方法。它可以在肉眼检测前1天客观准确地检测压力性损伤，因此能够比其他方法更早地指导预防工作。关于该主题已知的信息有哪些？在早期阶段检测压力性损伤具有挑战性。红外热成像可用于皮下组织异常情况的生理和解剖学评估。卷积神经网络在医学影像分析中越来越常用。本研究增加了什么内容？基于IRT的模型用于在肉眼检测到压力性损伤前1天识别组织损伤的最佳截断值为0.53，在肉眼可检测到压力性损伤当天进行检测的最佳截断值为0.88。基于红外热成像的模型可供临床皮肤科医生和护士用于早期客观准确地检测压力性损伤。
