Assessment of the individual fracture risk of the proximal femur by using statistical appearance models.
OBJECTIVE
Standard diagnostic techniques to quantify bone mineral density (BMD) include dual-energy x-ray absorptiometry (DXA) and quantitative computed tomography. However, BMD alone is not sufficient to predict the fracture risk for an individual patient. Therefore, the development of tools, which can assess the bone quality in order to predict individual biomechanics of a bone, would mean a significant improvement for the prevention of fragility fractures. In this study, a new approach to predict the fracture risk of proximal femora using a statistical appearance model will be presented.


METHODS
100 CT data sets of human femur cadaver specimens are used to create statistical appearance models for the prediction of the individual fracture load (FL). Calculating these models offers the possibility to use information about the inner structure of the proximal femur, as well as geometric properties of the femoral bone for FL prediction. By applying principal component analysis, statistical models have been calculated in different regions of interest. For each of these models, the individual model parameters for each single data set were calculated and used as predictor variables in a multilinear regression model. By this means, the best working region of interest for the prediction of FL was identified. The accuracy of the FL prediction was evaluated by using a leave-one-out cross validation scheme. Performance of DXA in predicting FL was used as a standard of comparison.


RESULTS
The results of the evaluative tests demonstrate that significantly better results for FL prediction can be achieved by using the proposed model-based approach (R = 0.91) than using DXA-BMD (R = 0.81) for the prediction of fracture load.


CONCLUSIONS
The results of the evaluation show that the presented model-based approach is very promising and also comparable to studies that partly used higher image resolutions for bone quality assessment and fracture risk prediction.
Schuler
Benedikt
B
Institute for Biomedical Image Analysis, University of Medical Informatics, Health Science and Technology (UMIT), 6060 Hall in Tirol, Austria. benedikt.schuler@umit.at
Fritscher
Karl D
KD
Kuhn
Volker
V
Eckstein
Felix
F
Link
Thomas M
TM
Schubert
Rainer
R
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D005264:Femoral Fractures / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*