Background and Objective: Diabetes mellitus is a common disorder amounting to 400 million patients worldwide. It is often accompanied by a number of complications, including neuropathy, nephropathy, and cardiovascular diseases. For example, peripheral neuropathy is present among 20–30% of diabetics before the diagnosis is substantiated. For this reason, a reliable detection method for diabetic complications is crucial and attracts a lot of research attention. Methods: In this paper, we introduce a non-invasive detection framework for patients with diabetic complications that only requires short video recordings of faces from a standard commercial camera. We employed multiple image processing and pattern recognition techniques to process video frames, extract relevant information, and predict the health status. To evaluate our framework, we collected a dataset of 114 video files from diabetic patients, who were diagnosed with diabetes for years and 60 video files from the control group. Extracted features from videos were tested using two conceptually different classifiers. Results: We found that our proposed framework correctly identifies patients with diabetic complications with 92.86% accuracy, 100% sensitivity, and 80% specificity. Conclusions: Our study brings a novel perspective on diagnosis procedures in this field. We used multiple techniques from image processing, pattern recognition, and machine learning to robustly process video frames and predict the health status of our subjects with high efficiency.

背景与目的：糖尿病是一种常见疾病，全世界有4亿患者。它通常伴有许多并发症，包括神经病，肾病和心血管疾病。例如，在确诊之前，20-30％的糖尿病患者存在周围神经病变。因此，一种可靠的糖尿病并发症检测方法至关重要，并且引起了很多研究关注。方法：在本文中，我们介绍了一种针对糖尿病并发症患者的非侵入性检测框架，该框架仅需要使用标准商用相机对面部进行简短的视频记录即可。我们采用了多种图像处理和模式识别技术来处理视频帧，提取相关信息并预测健康状况。为了评估我们的框架，我们收集了来自糖尿病患者的114个视频文件的数据集，这些患者被诊断患有糖尿病多年，而对照组则有60个视频文件。使用两个概念上不同的分类器测试了从视频中提取的功能。结果：我们发现，我们提出的框架可正确识别具有22.86％准确性，100％敏感性和80％特异性的糖尿病并发症患者。结论：我们的研究为该领域的诊断程序带来了新的视角。我们使用了图像处理，模式识别和机器学习等多种技术来稳健地处理视频帧，并高效地预测受试者的健康状况。使用两个概念上不同的分类器测试了从视频中提取的功能。结果：我们发现，我们提出的框架可正确识别具有22.86％准确性，100％敏感性和80％特异性的糖尿病并发症患者。结论：我们的研究为该领域的诊断程序带来了新的视角。我们使用了图像处理，模式识别和机器学习等多种技术来稳健地处理视频帧，并高效地预测受试者的健康状况。使用两个概念上不同的分类器测试了从视频中提取的功能。结果：我们发现，我们提出的框架可正确识别具有22.86％准确性，100％敏感性和80％特异性的糖尿病并发症患者。结论：我们的研究为该领域的诊断程序带来了新的视角。我们使用了图像处理，模式识别和机器学习等多种技术来稳健地处理视频帧，并高效地预测受试者的健康状况。