"" Using Artificial Neural Networks for the Forecasting of the Correct Skeletal System Development in Children

Review Article

Austin J Anat. 2014;1(3): 1012.

Using Artificial Neural Networks for the Forecasting of the Correct Skeletal System Development in Children

Gworys B1*, Kordecki H1,3 and Brukiewa R2

1Department of Anatomy, Wroclaw Medical University, Poland

2NZOZ „Nasz Lekarz” in Torun, Poland

3Institute of Computer Technology, Automatics and Robotics, Wroclaw University of Technology, Poland

*Corresponding author: Gworys B, Department of Anatomy, Wroclaw Medical University, 6a Chalubinskiego St, 50-368 Wroclaw, Poland.

Received:May 20, 2014; Accepted: July 09, 2014; Published: July 14, 2014

Abstract

Background: The aim of the work was to evaluate changes in bone density taking place in the femoral bone of healthy children depending on the amount of fat tissue and advancement in puberty.

Methods: A sample of 370 children chosen at random from the public school, of both sexes’ (190 boys aged from 7.2 to 156 years and 180 girls aged from 5.6 to 14 years) was examined in Torun, at the “Nasz Lekarz” outpatient clinic. Using DXA method an individual evaluation of femoral bone density in particular measurement fields was performed. Taking BMI parameter into account children were divided (separately boys and girls) into groups in pre-pubertal phase, pubertal phase and post-pubertal phase. The evaluation of statistic differences between groups was performed using variance analysis. To estimate the variability of femoral bone density artificial neural networks (RBF) were chosen. The use of this method allowed individual prediction of bone density based on current age and BMI value.

Results: Girls have 0.1 g/cm2 less dense femoral bone than boys. Femoral bone density rises with the pubertal spurt with girls by 0.22 g/cm2, with boys by 0.16 g/cm2.

Conclusion: It was ascertained that there is a statistically significant increase in femoral bone density dependent on the age of the child. For boys it is larger in the later phase of puberty. Differences of bone density changes depending on sex were confirmed. The bone density prediction based on current age and BMI for small group of children was performed and its results appeared to be very promising.

Keywords: Bone density; Puberty; Artificial neural networks

Introduction

Osteoporosis and osteopenia are medical conditions that cause ill-health for patients and costs for society in terms of treatment. A prophylactic intervention that would prevent osteoporosis by providing a peak bone mineralization could result in a considerable decrease in the number of osteoporotic fractures in later life. Therefore a definition of the onset of pathological change and an ability to predict these changes would be of benefit to administrative and health care authorities and, not least to potential future sufferers. The aim of the work is the evaluation of changes in bone density taking place in femoral bone in healthy children depending on the amount of fat tissue and advancement in puberty. At the same time methods of individual prediction of bone density basing on age and BMI value was described.

Seeman [1] noticed that research on twins and families proves that the peak bone mass is determined by predominantly genetic factors and is to a high degree hereditary. Gilsanz et al. [2] Shoved that puberty has an essential effect on the final bone mass formation and bone density. Results of analysis performed by team Clark [3] suggests that there is a connection between low bone density and fractures in children but he also mentioned that well conducted and extensive research is needed to confirm this fact.

Methods

Material for the work contains examination results of 370 children, of both sexes’ (190 boys aged from 7.2 up to 16 years and 180 girls aged from 5.6 to 14 years) examined in Torun, at the “Nasz Lekarz” outpatient clinic chosen at random from public school No 24. The individual evaluation of morphological age based on anamnesis and the estimation of secondary sex characteristics age was performed before the examination of every child. Basing on this criterion, children were divided into 6 groups: boys in pre-pubertal phase – 63 individuals; pubertal phase – 64 individuals; in late pubertal phase – 63 individuals; girls in pre-pubertal phase – 60 individuals; in pubertal phase – 60 individuals in late pubertal phase – 60 individuals.

Using DXA method the evaluation of femoral bone density in particular measurement fields was performed. The BMD-FQ (femoral neck), BMD-FT (greater trochanter), BMD-FU (femoral shaft), CMD-FV (total femoral bone density) were taken into account. For both sexes and every measured parameter, statistically significant differences between mean values in separate age categories were examined using variance analysis (ANOVA).

To estimate the bone density in particular regions of femur depending on simultaneously age and BMI value artificial neural networks were used. Expecting non-linear dependences two layers networks type RBF (Radial Based Function) were chosen. The usefulness of RBF networks in case of nonlinearity, among the others, was tested with positive results by Blake [4].

Results

In tables 1 and 2 the basic statistical parameters of the investigated material are shown.