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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.
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.
Fatty Liver is common, found incidentally on CT in 11.4% of the adult population in the US and in 22% among diabetics. Fatty liver is a risk factor for several key preventable diseases. Presence of fatty liver is associated with subclinical cardiovascular changes, elevated inflammatory markers of atherosclerosis and heart dysfunction. In diabetics (type II), fatty liver is associated with coronary artery disease.
It is also independently associated with increased coronary artery calcification and is a strong predictor of high-risk coronary artery plaque. The presence of fatty liver indicates 2.13x – 4.6x risk of having high risk coronary artery plaque. People with fatty liver are nearly 2x as likely to experience a cardiovascular event (heart attack or sudden death) over a mean follow up interval of 7.3 years.
Zebra developed an algorithm which analyses CT Chest / Abdomen data, automatically segments the liver, and calculates its average density. When detected in time, fatty liver can be reversible with lifestyle modifications involving diet, exercise and reduced alcohol intake. This algorithm can provide a ‘wake up’ call to pre-diabetics to spur lifestyle interventions.
Coronary artery calcium is a biomarker of coronary artery disease – and quantification of coronary calcification is a strong predictor for cardiovascular events such as heart attack or strokes.
Conventional coronary calcium scoring has required dedicated cardiac, ECG gated CT performed with and without contrast. Recently, the reliable derivation of coronary calcium score has been obtained algorithmically from low dose chest CT data (Isgum et al. 2012); the automatically derived score was predictive of cardiovascular events in a large cohort of individuals undergoing CT lung cancer screening.
Zebra has developed an algorithm that automatically calculates Coronary Calcium Scores based on standard, non-contrast Chest CTs. This tool can provide early detection of people at high risk of severe cardiovascular events.
An aneurysm is an abnormal enlargement of an artery due to weakness of the vessel wall. Aneurysms can be caused by abnormal flow within the vessel, disease of the arterial wall and most commonly a combination of the two. Abdominal aortic aneurysms (AAA’s) describe such enlargement in the abdominal portion of the Aorta, most commonly below the level of the renal (kidney) arteries. AAA’s are generally asymptomatic – the aorta being a structure which is surrounded by fat and able to grow in circumference without creating alarm. The greatest risk of AAA’s is of rupture, which results in death almost uniformly (over 90% of the time). The risk of rupture is directly related to the size of the aneurysm: generally the risk of rupture is given as %/yr and increases by 10% with every cm diameter growth. For example, a 3cm diameter Abdominal aorta is considered normal. An aneurysm which measures 6-7cm is associated with a 10-20% risk of rupture per year; a 7-8 cm diameter raises the risk of rupture.
Aortic aneurysms are commonly overlooked in CT imaging, especially when expert radiologists are seeking and diagnosing more urgent conditions. This is especially so for small (<4-5 cm) aneurysms – up to 89% of which go undetected. A recent study found that 9% of “missed” AAA’s, measured 5.5cm or greater – a diameter commonly accepted as the threshold for intervention.
The Zebra AAA detection tool automatically evaluates each contrast enhanced CT study which includes the abdomen. The algorithm measures the greatest dimension of the abdominal aorta and identifies studies in which the aorta measures greater 4cm with an accuracy of over 90%.
Emphysema is one of the diseases that comprises COPD (chronic obstructive pulmonary disease). It is a long-term, progressive obstructive lung disease in which the alveoli (small sacs) that promote oxygen-carbon dioxide exchange between the air and the bloodstream become damaged or destroyed. There are approximately 12 million individuals in the US who carry a diagnosis of COPD and the American Lung Association estimates that there were twice as many patients with impaired lung function (indicative of early stage COPD) than patients with diagnosed COPD. COPD is the third leading cause of death behind heart disease and cancer, and current estimates suggest that COPD costs the nation almost $50 billion annually in both direct and indirect health expenditures.
Zebra has developed an algorithm which analyzes CT Chest studies, detects emphysematous regions in the lungs, and quantifies the volume of emphysema in comparison to the overall lung volume.
A more accurate understanding of the prevalence of the disease within a given population can help patients manage the disease more effectively before it degrades to more severe, less treatable manifestations.
Brain bleeds refer to any condition which results in blood within the skull but outside of its normal location in an artery or vein. When bleeding is from an artery, it is commonly due to an abnormal weakening of the artery wall, such as seen in the case of a ruptured aneurysm. An estimated 6 million people in the United States have an unruptured brain aneurysm. The most common type of venous brain bleeds, on the other hand, is a subdural vein rupture. Brain bleeds can also arise from within the tiny vessels in the brain tissue itself: these are known as “parenchymal” brain bleeds and are most commonly due to stroke, trauma or cancer.
The presence of a bleed within the brain is a finding of major importance which should trigger immediate therapeutic intervention. Unfortunately there are several factors that contribute to the difficulty in making an accurate and timely diagnosis of bleeds on CT brains; firstly, the vast majority of Brain CT scans are completely normal (Callaghan, Kerber, Pace, Skolarus, & Burke, 2014). In the absence of traumatic head injury, only 13% of head CTs show any significant abnormality and less than 5% show evidence of intracranial blood (Wang & You, 2013). Secondly, many bleeds can be subtle on CT imaging (Yuh et al., 2013).
Zebra has developed an algorithm that automatically detects internal brain bleeds based on standard, non-contrast Brain CTs. This tool can provide early detection of people at high risk of severe brain bleeding events.