A pencil beam Hologic QDR 1000W scanner (1000), a fan beam QDR 4500A scanner (4500) and a fan beam Lunar Expert scanner (Expert) were compared for bone mineral and body composition measurement accuracy. Phantoms were scanned with each instrument to assess magnification effects and to compare calibrations for bone mineral and fat proportion. 41 volunteers were scanned with both the 1000 and the 4500, and 21 patients with both the 4500 and the Expert. The height of a bone within the body affected the measured bone mineral content (BMC) and, to a lesser extent, the bone mineral density (BMD). There were differences in calibration against recognized standards for fat proportion between the three instruments. The 1000 underestimated low fat proportions and the 4500 underestimated high fat proportions. Fat results for the Expert were closer to nominal values. Comparisons on volunteers showed that measured mean total body BMD was 4% higher and BMC was 7% higher with the 1000 compared with the 4500; some regional differences were greater. Mean values of per cent fat were equal, but the total and regional regression coefficients were well above unity. Mean BMD was 3% higher and mean BMC was 10% higher with the Expert compared with the 4500, but most regression coefficients for these comparisons were less than unity. Mean values of per cent fat were equal, but regression coefficients were above unity. Errors due to magnification are acceptable. Differences between the instruments are appreciable, but can be accommodated by cross-calibration.
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http://dx.doi.org/10.1259/bjr.74.878.740166 | DOI Listing |
Endocrine
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Department of Health Management, Chronic Health Management Laboratory, Henan Provincial People's Hospital, Zhengzhou, 450003, China.
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January 2025
Graduate Program, School of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
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Department of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
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Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
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The Third Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
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