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采用智能鞋垫的先进动态压力中心诊断:糖尿病患者与健康人在诊断糖尿病周围神经病变方面的比较

Advanced Dynamic Centre of Pressure Diagnostics with Smart Insoles: Comparison of Diabetic and Healthy Persons for Diagnosing Diabetic Peripheral Neuropathy.

作者信息

Fuss Franz Konstantin, Tan Adin Ming, Weizman Yehuda

机构信息

Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany.

Division of Biomechatronics, Fraunhofer Institute for Manufacturing Engineering and Automation IPA, D-95447 Bayreuth, Germany.

出版信息

Bioengineering (Basel). 2024 Dec 8;11(12):1241. doi: 10.3390/bioengineering11121241.

DOI:10.3390/bioengineering11121241
PMID:39768059
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11673908/
Abstract

Although diabetic polyneuropathy (DPN) has a very high prevalence among people with diabetes, gait analysis using cyclograms is very limited, and cyclogram research, in general, is limited to standard measures available in software packages. In this study, cyclograms (movements of the centre of pressure, COP, on and between the plantar surfaces) of diabetics and healthy individuals recorded with a smart insole were compared in terms of geometry and balance index, BI. The latter was calculated as the summed product of standard deviations of cyclogram markers, i.e., start/end points, turning points, and intersection points of the COP. The geometry was assessed by the positions of, and distances between, these points, and the distance ratios (14 parameters in total). The BI of healthy and diabetic individuals differed significantly. Of the fifteen parameters (including the BI), three were suitable as classifiers to predict DPN, namely two distances and their ratio, with false negatives ranging from 1.8 to 12.5%, and false positives ranging from 2.9 to 7.1%. The standard metric of the cyclogram provided by the software packages failed as a classifier. While the BI captures both DPN-related balance and other balance disorders, the changing geometry of the cyclogram in diabetics appears to be DPN-specific.

摘要

尽管糖尿病性多发性神经病变(DPN)在糖尿病患者中具有很高的患病率,但使用运动轨迹图进行步态分析的情况非常有限,而且一般来说,运动轨迹图研究仅限于软件包中可用的标准测量方法。在本研究中,对使用智能鞋垫记录的糖尿病患者和健康个体的运动轨迹图(足底表面上及之间的压力中心,COP的运动)在几何形状和平衡指数(BI)方面进行了比较。后者计算为运动轨迹图标记(即COP的起点/终点、转折点和交点)的标准差的乘积之和。通过这些点的位置、点之间的距离以及距离比(总共14个参数)来评估几何形状。健康个体和糖尿病患者的BI存在显著差异。在这15个参数(包括BI)中,有3个参数适合作为预测DPN的分类器,即两个距离及其比值,假阴性率在1.8%至12.5%之间,假阳性率在2.9%至7.1%之间。软件包提供的运动轨迹图的标准度量作为分类器失败了。虽然BI既反映了与DPN相关的平衡,也反映了其他平衡障碍,但糖尿病患者运动轨迹图几何形状的变化似乎是DPN特有的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/3d922c602085/bioengineering-11-01241-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/5d0c4318e3b4/bioengineering-11-01241-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/350cc148d1ae/bioengineering-11-01241-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/2237d65c372f/bioengineering-11-01241-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/15a9f57883f4/bioengineering-11-01241-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/cb09a2e573cf/bioengineering-11-01241-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/3d922c602085/bioengineering-11-01241-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/5d0c4318e3b4/bioengineering-11-01241-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/350cc148d1ae/bioengineering-11-01241-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/2237d65c372f/bioengineering-11-01241-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/15a9f57883f4/bioengineering-11-01241-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/cb09a2e573cf/bioengineering-11-01241-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6fb/11673908/3d922c602085/bioengineering-11-01241-g006.jpg

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本文引用的文献

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Is the 10 g monofilament fit for purpose for diagnosing DPN?10克单丝用于诊断糖尿病性周围神经病是否适用?
BMJ Open Diabetes Res Care. 2023 Nov;11(6). doi: 10.1136/bmjdrc-2023-003773.
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Diagnostic accuracy of the 5.07 monofilament test for diabetes polyneuropathy: influence of age, sex, neuropathic pain and neuropathy severity.
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