Costa Tatiana, Coelho Luis, Silva Manuel F
Instituto Superior de Engenharia do Porto, 1161257 Porto, Portugal.
Bioengineering (Basel). 2022 Feb 22;9(3):86. doi: 10.3390/bioengineering9030086.
Diabetic peripheral neuropathy is a major complication of diabetes mellitus, and it is the leading cause of foot ulceration and amputations. The Semmes-Weinstein monofilament examination (SWME) is a widely used, low-cost, evidence-based tool for predicting the prognosis of diabetic foot patients. The examination can be quick, but due to the high prevalence of the disease, many healthcare professionals can be assigned to this task several days per month. In an ongoing project, it is our objective to minimize the intervention of humans in the SWME by using an automated testing system relying on computer vision. In this paper we present the project's first part, constituting a system for automatically identifying the SWME testing sites from digital images. For this, we have created a database of plantar images and developed a segmentation system, based on image processing and deep learning-both of which are novelties. From the 9 testing sites, the system was able to correctly identify most 8 in more than 80% of the images, and 3 of the testing sites were correctly identified in more than 97.8% of the images.
糖尿病周围神经病变是糖尿病的主要并发症，也是足部溃疡和截肢的主要原因。Semmes-Weinstein单丝检查（SWME）是一种广泛使用、低成本、基于证据的工具，用于预测糖尿病足患者的预后。该检查可以很快完成，但由于该疾病的高发病率，许多医疗保健专业人员可能每月有好几天都被分配到这项任务。在一个正在进行的项目中，我们的目标是通过使用依赖计算机视觉的自动测试系统，尽量减少人类在SWME中的干预。在本文中，我们展示了该项目的第一部分，即一个从数字图像中自动识别SWME测试部位的系统。为此，我们创建了一个足底图像数据库，并开发了一个基于图像处理和深度学习的分割系统——这两者都是创新之处。在9个测试部位中，该系统能够在超过80%的图像中正确识别出最多8个部位，并且在超过97.8%的图像中正确识别出3个测试部位。
