Never Miss Another Fracture
Zebra’s solution provides powerful new technology that can identify vertebral compression fractures in an effort to help stem the widespread impact of osteoporosis. As noted by the World Congress of Osteoporosis, 75% of compression fractures are missed or not reported. Zebra’s automated compression fracture detection can help an institution identify most compression fractures on the commonly performed chest and abdomen CT scan.
Osteoporotic vertebral compression fractures are common, affecting up to one in four of post – menopausal women, and nearly one in seven men over the age of 65. Vertebral compression fractures (VCF) are a direct cause of morbidity, decreasing mobility and functional status particularly among the elderly. Detection of VCF’s is paramount in the effort to decrease secondary osteoporotic fractures – the most morbid of which are hip fractures. Diagnosing VCF’s is therefore of critical importance for the implementation of both primary therapeutic and secondary preventative interventions.
Zebra’s bone health algorithm detects vertebral compression fractures and can be applied to any CT of the chest or abdomen. This is an ideal solution for any institution that holds bone health as a priority. The University of Oxford has successfully tested and rolled out Zebra’s bone health solution sitewide, and are enjoying ROI positive results through the identification of more patients with compression fractures.
Customers with busy reporting environments where incidental findings are often not reported use Zebra to increase the number of vertebral compression fractures that are being reported thus the number of patients that are eligible for treatment. Sites that run fracture prevention programs/ population management programs use Zebra to systematically source people into these programs and initiate further investigation and treatment.
How it works
Zebra’s bone health solution integration occurs in the background as scans are being forwarded to the Zebra server for analysis. Zebra’s insights are then gathered to a structured report which includes all positive cases identified by the algorithm for the medical staff to review. A sagittal image is provided per each positive case where the algorithm detected a fracture.
Dr. Kassim JavaidUniversity of Oxford
We successfully ran a pilot with Zebra Medical Vision’s vertebral compression fracture algorithm with the purpose of evaluating how AI based technology can help increase patient flow into Oxford’s Fracture Liaison Service. The pilot was successful, as accuracy was above 90%. Dr. Javaid states, “We were quite pleased with the Zebra pilot and results, and as such are expanding our use of the technology to increase the patient flow into our FLS program in 2018 and 2019.”
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