الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Body composition is used to describe the percentages of fat, bone, water and muscle in human bodies. Precise and accurate measurements of body composition are useful in achieving a greater understanding of human energy metabolism in physiology and in different clinical conditions, and in evaluating interventions. Many disease processes affect bone and soft tissue at the same time. Therefore, the comprehensive view of body composition provided by whole body DXA makes it an attractive technique for a variety of clinical research and practice applications.
Measuring bone mineral density (BMD) is currently the best modality to diagnose osteoporosis and predict future fractures. The use of risk factors to predict BMD and fracture risk has been considered to be inadequate for precise diagnostic purpose, but it may be helpful as a screening tool to determine who actually needs BMD assessment.
Bone mineral density (BMD) is an indicator of bone health and the most accurate predictor of the risk of fracture, which increases exponentially as BMD decreases, and it is fundamental in clinically diagnosing osteopenia and osteoporosis. Monitoring BMD is particularly important when considering that pharmacological treatment for osteoporosis, although effective in maintaining bone mass, does not considerably increase BMD, so that prevention is of utmost importance.
Dual X-ray absorptiometry (DXA), which is the most commonly used method for the diagnosis and followup of human bone health, is known to produce accurate estimates\ of bone mineral density (BMD).
Recent advances in the use of artificial neural networks (ANN) for classifying clinical data and for predicting pathologies may be an alternative means of predicting BMD in various states of health.
ANNs represent a powerful tool to help physicians perform diagnosis and other enforcements. In this regard, ANNs have several advantages including: the ability to process large amount of data and reduction of diagnosis time
ANNs have proven suitable for satisfactory diagnosis of various diseases. In addition, their use makes the diagnosis more reliable and therefore increases patient satisfaction. However, despite their wide application in modern diagnosis, they must be considered only as a tool to facilitate the final decision of a clinician, who is ultimately responsible for critical evaluation of the ANN output. Methods of summarizing and elaborating on informative and intelligent data are continuously improving and can contribute greatly to effective, precise, and swift medical diagnosis.