Hip fractures in older adults have severe effects on patients’ morbidity as well as mortality, so it is crucial to avoid this injury through the early identification of patients at high risk. Currently, the diagnosis of osteoporosis and consequently hip fracture risk is done through the measurement of bone mineral density by a dual-energy X-ray absorptiometry (DXA) scan. However, studies show that this method is not accurate enough, and a high percentage of patients who sustain a hip fracture had non-osteoporotic DXA scans less than a year before the incidence.

In this research, to enhance the hip fracture risk prediction, the effect of a femur’s geometry and bone mineral density distribution was considered in the hip fracture risk estimation. This was done through 2D and 3D statistical shape and appearance modeling of the proximal femur using standard clinical DXA scans. To assess the proposed techniques, destructive mechanical tests were performed on 16 isolated cadaveric femurs. Also, through collaboration with the Canadian Osteoporosis Study (CaMos), the proposed statistical techniques to predict the hip fracture risk were evaluated in a clinical population as well.

The results of this study showed that new techniques can enhance hip fracture risk estimation;​ in the clinical study, 2D and 3D statistical modeling were able to improve identifying patients at high risk by 40% and 44% over the clinical standard method. Also, the percentage of correct predictions using 2D statistical models did not differ significantly from the 3D predictions. Therefore, by applying these techniques in clinical practice it could be possible to identify patients at high risk of sustaining a hip fracture more accurately and eventually reduce the incidence of hip fractures and the pain and social and economic burden that comes with it.