  An osteoporosis-related fracture occurs every three seconds worldwide,
affecting one in three women and one in five men aged over 50. The early
detection of at-risk patients facilitates effective and well-evidenced
preventative interventions, reducing the incidence of major osteoporotic
fractures. In this study, we present an automatic system for identification of
vertebral compression fractures on Computed Tomography images, which are often
an undiagnosed precursor to major osteoporosis-related fractures. The system
integrates a compact 3D representation of the spine, utilizing a Convolutional
Neural Network (CNN) for spinal cord detection and a novel end-to-end sequence
to sequence 3D architecture. We evaluate several model variants that exploit
different representation and classification approaches and present a framework
combining an ensemble of models that achieves state of the art results,
validated on a large data set, with a patient-level fracture identification of
0.955 Area Under the Curve (AUC). The system proposed has the potential to
support osteoporosis clinical management, improve treatment pathways, and to
change the course of one of the most burdensome diseases of our generation.
