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• W2059664105 abstract "We investigate the use of different trabecular bone descriptors and advanced machine learning techniques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R 2 . The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869±0.121 , R 2 : 0.68±0.079 ), which was significantly better than DXA BMD alone (RMSE: 0.948±0.119 , R 2 : 0.61±0.101 ) (p<10 −4 ). For multivariate feature sets, SVR outperformed multiregression (p<0.05 ). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens." @default.
• W2059664105 created "2016-06-24" @default.
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• W2059664105 date "2014-02-04" @default.
• W2059664105 modified "2023-10-16" @default.
• W2059664105 title "Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression" @default.
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• W2059664105 doi "https://doi.org/10.1117/1.jei.23.1.013013" @default.
• W2059664105 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4030629" @default.
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