## 1 Introduction  
Dual-energy X-ray absorptiometry (DXA) is the most widely used clinical tool to diagnose osteoporosis. It permits a precise in vivo quantification of mineralization in bones surrounded by soft tissue. DXA utilizes two different X-ray energies in order to separate bone from soft tissue. The lower-energy beam is preferentially attenuated by bone; however, the contributions of soft tissue to beam attenuation can be mathematically derived. Radiation exposure to the patient is very small, usually of a magnitude similar to daily background radiation. Older generation DXA scanners provided low-resolution images that were solely used for patient positioning verification or regions of interest (ROI) placement. The obtaining of images of much better quality from modern DXA scanners has led to the development of many new diagnostic applications, such as the trabecular bone score or vertebral morphometry [1]. Special DXA protocols permit the quantification of bone mineralization, fat mass, and fat distribution. Improving the image quality and shortening the scanning time allows a full-body scan to be performed and to quantify its components. Contemporary DXA images of the full-body, containing information about the quantity and spatial distribution of body components, opens the field for research on the new use of this old method. This work is part of a larger project that addresses the following research question: how deep learning methods could improve the diagnostic knowledge extraction from whole-body, low resolution, dual-energy X-ray absorptiometry data, potentially related to a lower radiation dose for patients. Related diagnostic knowledge could be represented in many forms. For example, it could be organized as a set of parameters that include bone density (e.g., bone mineral density (BMD), bone mineral content (BMC)), fat density, muscle density, osteoporosis scores, bone age, etc. The scanners used for testing allow a digital version of the image to be obtained, but it is only used to check correct positioning of the patient setting and correct placement of the regions of interest (ROI). The bone density results are computed by specialized software based on the X-ray attenuation data that the detector receives. For BMD measurement, it is important to eliminate soft tissue input, a measurement based on bone alone. This is possible by scanning at two different X-ray photon energies, followed by mathematical manipulation of the recorded signal; different attenuations for bones and soft tissues at different photon energies are used. Furthermore, the radiation dose for DXA is considerably lower than the radiation dose for conventional radiography. Many other information and dependencies can be extracted from such images that are not used in determining BMD. Many researchers focus on extracting information from DXA and X-ray images. In [2] the authors presented a study that described the comparison of the use of DXA images together with X-ray images to assess bone age in children using the Bland and Altman statistical methods. A high agreement was obtained between the bone age assessments performed by DXA and the X-ray. Other studies also considered the problem of using statistical methods, this time involving only BMD measurements. [PERSON] et al. in [3] investigated whether there were correlations between age and gender and BMD, and whether you could use this solution in forensic anthropology. The research was carried out on 70 subjects and showed bone mineral density to be a useful technique for gender and age data in forensic anthropology. Neural networks can also be used to analyze X-ray images. [PERSON] et al. in [4] used neural networks to create a method to estimate age at death. The data sample consisted of 100 femora of female individuals. The authors used a modified general regression neural network as a model for age prediction.