Advanced 2D image processing technique to predict hip fracture risk in an older population based on single DXA scans.

Abstract

Diagnosis of osteoporosis is based on the measurement of bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA) scans. However, studies have shown this to be insufficient to accurately predict hip fractures. Therefore, complementary methods are needed to enhance hip fracture risk prediction to identify vulnerable patients. Hip DXA scans were obtained for 192 subjects from the Canadian Multicenter Osteoporosis Study (CaMos), 50 of whom had experienced a hip fracture within 5years of the scan. 2D statistical shape and appearance modeling was performed to account for the effect of the femur's geometry and BMD distribution on hip fracture risk. Statistical shape modeling (SSM), and statistical appearance modeling (SAM) were also used separately to predict the fracture risk based solely on the femur's geometry and BMD distribution, respectively. Combined with BMD, age, and body mass index (BMI), logistic regression was performed to estimate the fracture risk over the 5-year period. Using the new technique, hip fractures were correctly predicted in 78% of cases compared with 36% when using the T-score. The accuracy of the prediction was not greatly reduced when using SSM and SAM (78% and 74% correct, respectively). Various geometric and BMD distribution traits were identified in the fractured and non-fractured groups. 2D SSAM can dramatically improve hip fracture prediction at high specificity levels and estimate the year of the impending fracture using standard clinical images. This has the potential to be implemented in clinical practice to estimate hip fracture risk.