Diagnosis of osteoporosis by quantification of trabecular microarchitectures from hip radiographs using artificial neural networks

Ju Hwan Lee, Yoo Na Hwang, Sung Yun Park, Sung Min Kim

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

The purpose of this study was to assess the diagnostic efficacy of an artificial neural network (ANN) in identifying postmenopausal women with low bone mineral density (BMD) by quantifying trabecular bone microarchitectures. The study included 53 post-menopausal women, who were classified as normal (n=17) and osteoporotic (n=36) according to T-scores. BMD was measured on the femoral neck by dual-energy X-ray absorptiometry. Morphological features were extracted to find optimum input variables by quantifying microarchitectures of trabecular bone. Principal component analysis was used to reduce the dimen-sionalities and improve classification accuracy. For the classification, a two-layered feed forward ANNs was designed using the Levenberg-Marquardt train-ing algorithm. The experimental results indicated the superior performance of the proposed approach for discriminating osteoporotic cases from normal.

Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
EditorsLinqiang Pan, Tao Song, Gheorghe Păun, Mario J. Pérez-Jiménez
PublisherSpringer Verlag
Pages247-250
Number of pages4
ISBN (Electronic)9783662450482
DOIs
StatePublished - 2014

Publication series

NameCommunications in Computer and Information Science
Volume472
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Keywords

  • Artificial neural network
  • Bone mineral density
  • Dual-energy X-ray absorptiometry
  • Microarchitecture
  • Osteoporosis
  • Trabecular bone

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