Artificial Neural Network Analysis: A Novel Application for Predicting Site-Specific Bone Mineral Density

TitelArtificial Neural Network Analysis: A Novel Application for Predicting Site-Specific Bone Mineral Density
Publication TypeJournal Article
Year of Publication2003
AuthorsMohamed E.I., Maiolo C., Linder R., Pöppl S.J., De Lorenzo A.
JournalActa diabetologica
Volume40 Suppl 1
PagesS19-22
Date Published2003 Oct
Publication Languageeng
ISSN0940-5429
SchlüsselwörterBone Density, Humans, Models, Biological, Neural Networks (Computer), Predictive Value of Tests, Reference Values
Abstract

Dual X-ray absorptiometry (DXA), which is the most commonly used method for the diagnosis and followup of human bone health, is known to produce accurate estimates of bone mineral density (BMD). However, high costs and problems with availability may prevent its use for mass screening. The objective of the present study was to estimate BMD values for healthy persons and those with conditions known to be associated with BMD, using artificial neural networks (ANN). An ANN was used to quantitatively estimate site-specific BMD values in comparison with reference values obtained by DXA (i. e. BMD(spine), BMD(pelvis), and BMD(total)). Anthropometric measurements (i. e. sex, age, weight, height, body mass index, waist-to-hip ratio, and the sum of four skinfold thicknesses) were fed to the ANN as independent input variables. The estimates based on four input variables were generated as output and were generally identical to the reference values for all studied groups. We believe the ANN is a promising approach for estimating and predicting site-specific BMD values using simple anthropometric measurements.

DOI10.1007/s00592-003-0020-3
PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/14618427?dopt=Abstract

Alternate JournalActa Diabetol
Erstellt am 9. November 2012 - 15:28 von Kulbe.

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