Group of Machine Learning, Data Science and Artificial Intelligence, Embodied Systems for Robotics and Learning (ESRL), The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Denmark.
Center for Innovative Medical Technology, Odense University Hospital, Odense, Denmark.
J Diabetes Res. 2019 Sep 2;2019:4583895. doi: 10.1155/2019/4583895. eCollection 2019.
(1) To quantify the invisible variations of facial erythema that occur as the blood flows in and out of the face of diabetic patients, during the blood pulse wave using an innovative image processing method, on videos recorded with a conventional digital camera and (2) to determine whether this "unveiled" facial red coloration and its periodic variations present specific characteristics in diabetic patients different from those in control subjects.
We video recorded the faces of 20 diabetic patients with peripheral neuropathy, retinopathy, and/or nephropathy and 10 nondiabetic control subjects, using a Canon EOS camera, for 240 s. Only one participant presented visible facial erythema. We applied novel image processing methods to make the facial redness and its variations visible and automatically detected and extracted the redness intensity of eight facial patches, from each frame. We compared average and standard deviations of redness in the two groups using -tests.
Facial redness varies, imperceptibly and periodically, between redder and paler, following the heart pulsation. This variation is consistently and significantly larger in diabetic patients compared to controls ( value < 0.001).
Our study and its results (i.e., larger variations of facial redness with the heartbeats in diabetic patients) are unprecedented. One limitation is the sample size. Confirmation in a larger study would ground the development of a noninvasive cost-effective automatic tool for early detection of diabetic complications, based on measuring invisible redness variations, by image processing of facial videos captured at home with the patient's smartphone.
(1) 使用创新的图像处理方法,在常规数码相机拍摄的视频上,量化糖尿病患者面部因血液进出而产生的、肉眼不可见的红斑变化,即在血液脉搏波期间;(2) 确定这种“揭示”的面部红色和其周期性变化在糖尿病患者中是否具有不同于对照组的特定特征。
我们使用佳能 EOS 相机对 20 名患有周围神经病变、视网膜病变和/或肾病的糖尿病患者和 10 名非糖尿病对照者的面部进行了 240 秒的视频记录。只有一名参与者出现可见的面部红斑。我们应用新颖的图像处理方法,使面部的红色及其变化可见,并自动检测和提取每个帧中 8 个面部斑块的红色强度。我们使用 t 检验比较了两组的平均和标准差。
面部红斑随心跳而周期性地、难以察觉地从更红变为更苍白。与对照组相比,这种变化在糖尿病患者中始终且显著更大(<0.001)。
我们的研究及其结果(即糖尿病患者的面部红色随心跳变化的幅度更大)是前所未有的。一个局限性是样本量。在更大的研究中得到证实,将为基于处理家庭中使用患者智能手机拍摄的面部视频的图像处理,测量不可见的红斑变化,开发一种非侵入性、具有成本效益的早期检测糖尿病并发症的自动工具奠定基础。