<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed, E I</style></author><author><style face="normal" font="default" size="100%">Maiolo, C</style></author><author><style face="normal" font="default" size="100%">Linder, Roland</style></author><author><style face="normal" font="default" size="100%">Siegfried J. Pöppl</style></author><author><style face="normal" font="default" size="100%">De Lorenzo, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial Neural Network Analysis: A Novel Application for Predicting Site-Specific Bone Mineral Density</style></title><secondary-title><style face="normal" font="default" size="100%">Acta diabetologica</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Acta Diabetol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bone Density</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks (Computer)</style></keyword><keyword><style  face="normal" font="default" size="100%">Predictive Value of Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Reference Values</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003 Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">40 Suppl 1</style></volume><pages><style face="normal" font="default" size="100%">S19-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/14618427?dopt=Abstract</style></custom1></record></records></xml>