Investigation of the factors contributing to fracture risk in clinical populations using novel image processing techniques of standard DXA images from longitudinal datasets.

Osteoporosis is a disease where an individual's bone density is reduced, which decreases its strength and increases the risk of a fragility fracture. This disease is common in older adults and is the main reason for a broken bone among this population. Hips are a common site for these fractures, with devastating long-term consequences, with >50% of patients requiring assisted living facilities post-fracture. To identify those who are at greatest risk, osteoporosis is diagnosed based using a Dual Energy X-ray Absorptiometry (DXA) scan. However, studies suggest that the current approach of evaluating bone density at one location is not able to accurately predict who will experience a fracture. In order to identify who should receive interventions (such as hip protectors or medications), new methods are necessary. Previous work in our lab has developed a method to extract more relevant information from a DXA scan. This now considers both the shape and quality of the bone, as well as including the patient's age and sex. We will apply this method to more than 50,000 patient scans from existing databases and use their history of falls and fractures to identify those at greatest risk. Using long-term follow-up data, we will investigate whether medications can slow the progress of osteoporosis, as well as how other factors such as smoking, activity, or diabetes alter risk. Through this, we will identify what influences a person's risk of breaking a bone from a fall. The outcome of this work will be recommendations for slowing age-associated increases in fracture risk. This will allow clinicians to better identify who would benefit from protective measures, ultimately reducing the incidence of these devastating and life-altering injuries for older adults.