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Hierarchical models for epidermal nerve fiber data

Journal article
Authors Claes Andersson
T. Rajala
Aila Särkkä
Published in Statistics in Medicine
Volume 37
Issue 3
Pages 357-374
ISSN 0277-6715
Publication year 2018
Published at Department of Mathematical Sciences
Pages 357-374
Language en
Links doi.org/10.1002/sim.7516
Keywords adjusted Rand index, diabetic neuropathy, EM-algorithm, nerve tree, unsupervised classification, algorithm, Article, blister, classification, disease severity, epidermal nerve fiber, hierarchical model, human, nerve fiber, Rand index, skin biopsy, skin nerve, statistical model
Subject categories Mathematical statistics

Abstract

While epidermal nerve fiber (ENF) data have been used to study the effects of small fiber neuropathies through the density and the spatial patterns of the ENFs, little research has been focused on the effects on the individual nerve fibers. Studying the individual nerve fibers might give a better understanding of the effects of the neuropathy on the growth process of the individual ENFs. In this study, data from 32 healthy volunteers and 20 diabetic subjects, obtained from suction induced skin blister biopsies, are analyzed by comparing statistics for the nerve fibers as a whole and for the segments that a nerve fiber is composed of. Moreover, it is evaluated whether this type of data can be used to detect diabetic neuropathy, by using hierarchical models to perform unsupervised classification of the subjects. It is found that using the information about the individual nerve fibers in combination with the ENF counts yields a considerable improvement as compared to using the ENF counts only. Copyright © 2017 John Wiley & Sons, Ltd.

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