Hip fractures, a major cause of mortality in older adults, often result from osteoporosis, which weakens bones with age. Current methods for diagnosing osteoporosis and hip fracture risk, such as the T-score from dual-energy X-ray absorptiometry (DXA) scans and the Fracture Risk Assessment Tool (FRAX), have limitations in predicting actual fracture risk. New technology, leveraging machine learning and image processing, shows promise for improved fracture risk prediction. This work aims to accelerate the clinical implementation of an image processing fracture risk tool.

Four studies were undertaken to accomplish this goal. To ensure low-effort implementation clinically, automation of the tool was needed. This was achieved by automating hip contour detection using a U-Net Convolutional Neural Network and applying this model to DXA scans from different databases, achieving high accuracies. To assess the tool's sensitivity to postural changes, the next study examined how minor adjustments, often due to limited range of motion or patient discomfort, affected fracture risk predictions and DXA clinical metrics. While postural changes did not influence fracture risk output, flexion angles exceeding 12° significantly impacted DXA metrics. Next, the tool's performance was evaluated using two file types and two DXA scanning protocols, which did not impact performance, highlighting its potential for seamless clinical integration. Finally, to ensure broad applicability to a heterogeneous cohort, the tool's effectiveness on medicated and unmedicated clinical patients and ability to predict changes in risk over time was evaluated. The results showed a superior improvement over FRAX, demonstrating the tool's positive potential with a more heterogeneous population. Better performance is expected with a larger and even more diverse training population, which represents the next critical advancement required for clinical integration. This work has great significance for the better identification of osteoporotic patients and will have positive outcomes for long-term health in aging.