Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis.

Research paper by K K KK Nishiyama, M M Ito, A A Harada, S K SK Boyd

Indexed on: 21 Aug '13Published on: 21 Aug '13Published in: Osteoporosis International


We used quantitative computed tomography and finite element analysis to classify women with and without hip fracture. Highly accurate classifications were achieved indicating the potential for these methods to be used for subject-specific assessment of fracture risk.Areal bone mineral density (aBMD) is the current clinical diagnostic standard for assessing fracture risk; however, many fractures occur in people not defined as osteoporotic by aBMD. Finite element (FE) analysis based on quantitative computed tomography (QCT) images takes into account both bone material and structural properties to provide subject-specific estimates of bone strength. Thus, our objective was to determine if FE estimates of bone strength could classify women with and without hip fracture.Twenty women with femoral neck fracture and 15 women with trochanteric fractures along with 35 age-matched controls were scanned with QCT at the hip. Since it is unknown how a specific subject will fall, FE analysis was used to estimate bone stiffness and bone failure load under loading configurations with femoral neck internal rotation angles ranging from -30° to 45° with 15° intervals. Support vector machine (SVM) models and a tenfold cross-validation scheme were used to classify the subjects with and without fracture.High accuracy was achieved when using only FE analysis for classifying the women with and without fracture both when the fracture types were pooled (82.9 %) and when analyzed separately by femoral neck fracture (87.5 %) and trochanteric fracture (80.0 %). The accuracy was further increased when FE analysis was combined with volumetric BMD (pooled fractures accuracy, 91.4 %)While larger prospective studies are needed, these results demonstrate that FE analysis using multiple loading configurations together with SVM models can accurately classify individuals with previous hip fracture.