alexa A Robotic Monofilament Test for Diabetic Neuropathy: From Bench to Clinic | OMICS International | Abstract
ISSN: 2155-6156

Journal of Diabetes & Metabolism
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Research Article

A Robotic Monofilament Test for Diabetic Neuropathy: From Bench to Clinic

Chumpon Wilasrusmee1*, Jackrit Suthakorn2, Yuttana Itsarachaiyot2, Napaphat Proprom1, Panuwat Lertsithichai1, Sopon Jirasisrithum1 and Dilip Kittur3

1Department of Surgery, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

2Center for Biomedical and Robotics Technology- BART LAB, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Bangkok, Thailand

3Department of Surgery, SUNY Upstate Medical University, Syracuse, New York, USA

*Corresponding Author:
Chumpon Wilasrusmee
Department of Surgery, Faculty of Medicine
Ramathibodi Hospital, Mahidol University
270 Rama VI Road, Bangkok 10400, Thailand
Tel: 662-201-1315
Fax: 662-201-1316
E-mail: [email protected]

Received date: December 19, 2011; Accepted date: February 21, 2012; Published date: February 26, 2012

Citation: Wilasrusmee C, Suthakorn J, Itsarachaiyot Y, Proprom N, Lertsithichai P et al. (2012) A Robotic Monofilament Test for Diabetic Neuropathy: From Bench to Clinic. J Diabetes Metab 3:179. doi:10.4172/2155-6156.1000179

Copyright: © 2012 Wilasrusmee C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: We have reported a novel robotic monofilament inspector (RMI) as a standard machine for screening of diabetic neuropathy. In this study, we aimed to evaluate the efficacy of RMI as compared to the manual Semmes- Weinstein monofilament test (SW), vibration perception test (VP), and Toronto Clinical Scoring (TC) in the screening of diabetic neuropathy.

Methods: 116 consecutive patients with Type II diabetes were included. The examiner conducted the RMI, VP, TC, and SW test without knowledge of patients’ lower-extremity symptoms and blinded from the patients’ perception. The performance of each test was analyzed by generating ROC curves for the detection diabetic neuropathy. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined by logistic regression analysis with adjustment for underlying disease.

Results: The prevalence of diabetic neuropathy detected (true positive) was highest in RMI, followed by SW, VP, and TC. The false positive rate for RMI, SW, VP, and TC were 26.42%, 24.53%, 33.96%, and 50.94%, respectively. The AUC of ROC curve for RMI was highest. It was slightly but not significantly higher than SW test. The AUCs of ROC curves of VP test and TC were significantly lower than RMI and SW test (Table 3, Figure 1). The sensitivity was highest in RMI, whereas the specificity was highest in SW test.

Conclusions: Difference screening tests result in different detection prevalence of diabetic neuropathy even in the same group of patients. The RMI could be used as a reliable tool in the screening of diabetic neuropathy.

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