Estimation of bone mineral density data using MoG neural networks

In this paper we propose a low cost prevention strategy for osteoporosis. Osteoporosis is a disease consisting in the structural deterioration of bones. This disease has a very high cost for the public health expense all over the world. Its main diagnostic tool is a radiographic analysis called Computerized Bone Mineralometry, by which it is possible to measure the Bone Mineral Density (BMD). Starting from the BMD value it is possible to estimate the risk of contracting osteoporosis. Although the cost of this clinical analysis is not high, a wide screening of the population can be not affordable. The proposed prevention strategy is based on the assumption that BMD can be estimated by a neural model, on the basis of some objective individual characteristics to be determined by the patient itself. We propose the use of MoG (Mixture of Gaussian) neural model, trained by an automatic procedure based on maximum likelihood approach.