Osteoporosis affects 1/3 women and 1/5 men over 50 and is responsible for 9M fractures globally every year. 80% of those at risk are not identified or treated, and patients who suffer from an Osteoporotic fracture experience significant degradation in their quality of life – 25% of hip fracture patients end up in a nursing home within 12 months of their fracture. The costs of Osteoporosis treatment are estimated to be $17 Billion in the US alone.
One of the parameters used to identify patients at risk of Osteoporosis is bone density. A DXA scan provides a T-Score, which along with other risk factors gives an indication of the likelihood of Osteoporosis. Unfortunately, few people actively seek to monitor their bone density, and DXA scans are only performed by a small percentage of the population. This perpetuates the low identification rate.
As part of its Imaging Analytics platform, Zebra has developed an automated algorithm that uses existing CT scans, performed for any reason, to output a result which is equivalent to the Bone Density T-Score generated by DEXA scans.
Providers can use their existing CT data to conduct prescreening for people with increased risk of fracture, with no need for additional tests or radiation. These can then be targeted for Bone Health or Fracture Prevention programs, reducing overall fracture rates and associated costs.
Vertebral Compression Fractures*
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. Timely surgical or minimally invasive treatment of VCF’s is effective but under-utilized, in part because less than one third of VCF’s are effectively diagnosed. Although VCF’s may be the result of infection, trauma or malignancy, the vast majority are a manifestation of osteoporosis. As such, vertebral fractures are diagnostic of osteoporosis in individuals over the age of 50. Detection of VCF’s is thus paramount in the effort to decrease additional osteoporotic fractures – (the most morbid of which are hip fractures) because the diagnosis may initiate effective preventative treatment. Diagnosing VCF’s is therefore of critical importance for implementation of both primary therapeutic and secondary preventative interventions.
The Zebra VCF detection algorithm was developed utilizing a combination of traditional machine vision segmentation and convolutional neural net (CNN) technology and can be applied to any CT of the chest, abdomen and/or pelvis. The algorithm automatically segments the vertebral column, identifies and localizes compression fractures. The algorithm is trained to differentiate between compression fractures and more ubiquitous degenerative endplate degenerative changes and bony osteophytes.