Beijing University of Chinese Medicine, 100000, China.
EVision Technology (Beijing) Co. LTD, 100000, China.
Biomed Res Int. 2021 Apr 20;2021:5556057. doi: 10.1155/2021/5556057. eCollection 2021.
In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.
在这项研究中，我们提出了一种使用易于携带的设备快速、无创地诊断 1 型和 2 型糖尿病的技术。糖尿病是一种影响全球公共健康的慢性疾病。虽然使用传统方法可以准确诊断糖尿病，但这些方法需要在临床环境中收集数据，在医疗资源较少的地区不太可行。该技术结合了对患者的生理和生理特征（即中医的舌诊和脉诊）的眼底摄影分析。随机森林算法用于分析数据，糖尿病的正确分类的准确率、精密度、召回率和 F1 分数分别为 0.85、0.89、0.67 和 0.76。该糖尿病诊断技术为糖尿病的诊断提供了一种新方法，因为在缺乏医疗资源的地区，这种方法可能更为方便，而在这些地区，早期发现糖尿病较为困难。
