Abstract
Osteoporotic fractures present a significant social and economic burden, which is set to rise commensurately with the aging population. Greater understanding of the physicochemical differences between osteoporotic and normal conditions will facilitate the development of diagnostic technologies with increased performance and treatments with increased efficacy. Using coherent X-ray scattering we have evaluated a population of 108 ex vivo human bone samples comprised of non-fracture and fracture groups. Principal component fed linear discriminant analysis was used to develop a classification model to discern each condition resulting in a sensitivity and specificity of 93% and 91%, respectively. Evaluating the coherent X-ray scatter differences from each condition supports the hypothesis that a causal physicochemical change has occurred in the fracture group. This work is a critical step along the path towards developing an in vivo diagnostic tool for fracture risk prediction.
Highlights
Provides estimates of the average areal density of all bone components
The performance of DEXA is at least as good at diagnosing osteoporosis as blood pressure is at predicting a stroke[6], though consistent performance statistics are not available in the literature
Enhancements have already been introduced in the form of patient risk factors but we seek to include additional information from bone quality that cannot be measured by bone mineral density (BMD)
Summary
Provides estimates of the average areal density of all bone components. We have collected X-ray scatter patterns from 108 bone samples (54 from individuals suffering from hip fractures and 54 from individuals with no fractures) and used this information in two different ways. To build a classification model that predicts each condition to produce a fracture and non-fracture group; and second, to evaluate which characteristics within the scatter patterns may be condition related. This approach was designed to further our understanding of the physicochemical changes that occur in osteoporotic tissue. Our aim is to support and inform the ongoing development of an in vivo diagnostic technique to enhance fracture risk prediction
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