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There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. The present study reports application of a neural network classifier to the processing of previously collected data on very low power radiofrequency propagation through the wrist with the goal to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low BMD (DXA T score below - 1) measured within one year.