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Journal of Electronic Imaging

Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression
Author(s): Chien-Chun Yang; Mahesh B. Nagarajan; Markus B. Huber; Julio Carballido-Gamio; Jan S. Bauer; Thomas H. Baum; Felix Eckstein; Eva-Maria Lochmüller; Sharmila Majumdar; Thomas M. Link; Axel Wismüller
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Paper 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.

Paper Details

Date Published: 4 February 2014
PDF: 11 pages
J. Electron. Imaging. 23(1) 013013 doi: 10.1117/1.JEI.23.1.013013
Published in: Journal of Electronic Imaging Volume 23, Issue 1
Show Author Affiliations
Chien-Chun Yang, Univ. of Rochester Medical Ctr. (United States)
Mahesh B. Nagarajan, Univ. of Rochester Medical Ctr. (United States)
Markus B. Huber, Univ. of Rochester Medical Ctr. (United States)
Julio Carballido-Gamio, Univ. of California, San Francisco (United States)
Jan S. Bauer, Technische Univ. München (Germany)
Thomas H. Baum, Technische Univ. München (Germany)
Felix Eckstein, Paracelsus Medizinische Privatuniversität (Austria)
Eva-Maria Lochmüller, Paracelsus Medizinische Privatuniversität (Austria)
Sharmila Majumdar, Univ. of California, San Francisco (United States)
Thomas M. Link, Univ. of California, San Francisco (United States)
Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)


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