The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study. Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

This article proposed to build a system founded on metamaterial sensor antennas, which can be used to evaluate impurities in aqueous substances according to the quality of transmission between the sensor antennas. In order to do this, a dedicated setup with tests in several frequencies was deployed so as to monitor the behavior of transmission variation between sensors. These sensors are microstrip antennas with a ground plane of resonant cleaved metallic rings; the substrate functions as a metamaterial for the irradiating element. In this study, an analysis was made of transmission between the sensors, looking for variation in angles of incidence of signal and of distance between the antennas. The sensor was tested at various operating frequencies, as such 1.8 GHz, 2.4 GHz, 3.4 GHz and 4.1 GHz, resulting in different values of sensitivity. The prototypes were constructed and tested so as to analyze the dielectric effects of the impurities on NaCl and C₂H₄O₂ substances. The research aims to use these control systems of impurities in industrial premises.

In this work, the electrical signals obtained by application of microwaves in chemical and bone tissues are analyzed and classified using techniques of signal processing and pattern recognition. For this, Wavelet Transform is applied as a method to extract relevant features of signal and KNN is used as a classification technique. The results showed that microwave signals can be analyzed using Wavelet Transform, which can be used to reconstruct the signals with minimal error rate and KNN showed satisfactory results.