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

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.