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View the full release here: While less than one third of vertebral compression fractures (VCFs) are effectively diagnosed, VCFs are remarkably common, affecting up to one in four postmenopausal women and nearly one in seven men over the age of 65. More than two million osteoporotic fractures occur annually in the US alone, significantly impairing the lives of those affected and their families, and costing the healthcare system nearly $17 billion USD. Detection of VCFs is thus paramount in the effort to decrease osteoporotic fractures the most morbid of which are hip fractures. Diagnosing VCFs is of critical importance for implementation of both primary therapeutic and secondary preventative interventions. “Research has shown that radiologists miss up to 50% of vertebral fractures, since they are usually focused on looking for other features, says Dr. Kassim Javiad, Clinical Lead of the UK Fracture Liaison Service report from the Department of Rheumatology, Oxford University Hospitals at the University of Oxford. In the UK, with our proven coordinated care programs for effective fracture prevention (Fracture Liaison Services), we believe that early detection of such fractures can yield both Shared this better care and significant healthcare cost savings. VCF is estimated to cost the NHS 1.5 billion annually. The Zebra VCF algorithm automatically identifies and localizes compression fractures. The This Site algorithm uses deep learning to differentiate between compression fractures and more ubiquitous degenerative endplate changes and osteophytes. This knowledge assists healthcare providers in accurately identifying people at risk and placing them under supervision or fracture prevention programs to reduce the risks of subsequent osteoporotic fractures. Osteoporotic fractures have a deeply negative impact on the lives of patients and their caregivers.

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