A Thank You To Our Partners in Healthcare

By: Elad Benjamin

As we get ready to head out to the annual Radiology Society of North America (RSNA) conference, we’re excited to be profiled in their new Machine Learning Showcase along with our two partners, Google and Carestream. Even more so, the team here at Zebra Medical Vision is feeling very fortunate that so many partners have already come forward to integrate our technology into their PACS viewers.

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How to Approach AI in Healthcare

By: Eyal Gura

Before the weekend, we announced the launch of AI1, a new way of making our artificial intelligence (AI) algorithms – current and future – available to healthcare providers for $1 per scan. We’re so happy to have seen this social-impact business model covered in by Forbes , Engadget, Venturebeat, and other notable publications. among others. 

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Introducing AI in Healthcare That’s Just $1 Per Use

By: Elad Benjamin
Over the last few years, we’ve been hard at work at Zebra to develop and introduce AI into radiology. We’ve written before about why this is important to us, based on the challenges this field is facing, and the impact we believe we can make. Healthcare is challenging – with long cycles, regulatory barriers and slow adoption of new technology, but our vision of affordable, accessible imaging technology for everyone keeps us continually thinking of ways to accelerate the realization of those goals.

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The story behind Zebra’s recent Mammography Publication

phil-e1469945648791Just over three years ago, Zebra medical vision launched into the world with two fundamental convictions and one mission. We knew that the potential for every novel technology is defined by its most profound application- which can be discovered only by people with ample doses of creativity, persistence and good first aid skills. 

We felt the same about human potential:  defined by a person’s decisions rather than her ability- a view captured by Shimon Peres in a quote on our wall: “you are as great as the cause you serve.” 

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Zebra receives CE Approval for its Analytics Engine

I happy and proud to share our latest news with all of you – the regulatory approval of our Imaging Analytics Engine in Europe, Australia and New Zealand. Receiving CE approval marks another milestone for Zebra, as we continue to provide physicians and hospitals with a growing number of analytics tools to help them improve care. Continue reading “Zebra receives CE Approval for its Analytics Engine”

Zebra’s CMO interviews Oxford Rheumatologist Dr. Kassim Javaid

eldad-photo Dr. Elnekave: What were the defining experiences which compelled you to dedicate your professional life as a consultant Rheumatologist to the issue of bone health & osteoporosis?


 Dr. Javaid: Rheumatology covers a wide range of diseases across the lifecourse. My interest in bone health was sparked by working with an inspirational professor that then led to me to complete a PhD in the field. Additionally osteoporosis allows interplay between epidemiology, biological processes, therapies and heatlh services research as well as interactions with multple other medical and surgical specialties. 

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Zebras & Streetlights part 2

In our last blog we discussed the need to search beyond the proverbial streetlight in medical imaging and presented two of Zebra’s first automatic imaging algorithms: coronary calcium quantification and liver density measurement. In that blog post, we pointed out that CT-chest lung cancer screening gained approval by demonstrating a 20% reduction of lung cancer deaths in the National Lung Cancer Screening Trial (NLCST). We argued that the merits of CT chest screening for long—term smokers might be markedly underestimated: more people in the NLCST died of cardiovascular disease than lung cancer, and the most powerful predictors of cardiovascular disease and mortality are coronary calcium and visceral fat, both of which can be screened for using the same Chest CT (hence our rationale for developing these fully automatic algorithms). Now we look even farther beyond the streetlight and mortality statistics to examine the factors most predictive of how well people live.

Smokers are at 13x increased risk of developing chronic obstructive lung disease (COPD); fully half of smokers develop some degree of COPD. For a person with COPD, life is defined mainly by how well they can breathe – “just” breathe. For healthcare providers, COPD is a mighty foe – prevalent, powerful and unpredictable. COPD exacerbations account for more than 12% of all ER admissions and 10% of inpatient hospital beds but those numbers can change precipitously: seasonal and even sporadic fluctuations  of COPD  exacerbations can overwhelm hospital wards [1]. The hospitalization course of any individual with COPD is just as precarious: according to the Royal College of Physicians [2], nearly 1 in 12 hospital admissions for COPD exacerbation resulted in death during that admission and 1 in 4 were associated with death within that year. The same study found that fully 30% of patients admitted with acute COPD required repeated hospitalization within 3 months.

After cardiovascular disease (25%) and lung cancer (24%), the next distinct cause of death among the NLCST population was lung disease (11%). Could the same screening CT data be used to change not only who dies of lung disease but, perhaps more important, how those with chronic lung disease live? Indeed, distinct CT- identifiable patterns of lung architecture have been correlated to different risks of clinical deterioration and can even be used to predict which of various therapy options would be most effective [3], [4] [5] [6] [7].

Interestingly, a single measurement of the size of the pulmonary artery on CT is the most powerful predictor of hospitalization and mortality in COPD patients[8], [9]. In fact, finding a pulmonary artery diameter which is larger than that of the adjacent aorta is associated with a greater than 3x risk of requiring hospitalization for acute respiratory failure within twelve months[9]. Although this finding is rare among the general population, it is seen in 10% – 30% of individuals with longstanding lung disease. Combining these two assessments of lung texture and pulmonary artery diameter could identify those in greatest risk of disease progression in order to more effectively deliver preventative and maintenance care [10].

COPD is a mighty foe – its prevalence will only increase in the next decades while resources to treat it remain relatively static. But COPD is not one disease – not a single diagnosis that follows a single predictable clinical trajectory. Its precariousness may be its greatest threat – and hence our target: to define the key features of COPD most predictive of disease course so that we can prevent and treat those who need it first. Two more automatic insights which every Chest CT should include: emphysema quantification and pulmonary artery diameter. To be continued…



[1] J. Kidney, T. McManus, and P. V Coyle, “Exacerbations of chronic obstructive pulmonary disease.,” Thorax, vol. 57, no. 9, pp. 753–4, Sep. 2002.

[2] B. T. S. and B. L. F. Royal College of Physicians, “Report of the National Chronic Obstructive Pulmonary Disease Audit 2008: Clinical audit of COPD exacerbations admitted to acute NHS trusts across the UK.,” 2008.

[3] O. M. Mets, M. Schmidt, C. F. Buckens, M. J. Gondrie, I. Isgum, M. Oudkerk, R. Vliegenthart, H. J. de Koning, C. M. van der Aalst, M. Prokop, J.-W. J. Lammers, P. Zanen, F. A. Mohamed Hoesein, W. P. Mali, B. van Ginneken, E. M. van Rikxoort, and P. A. de Jong, “Diagnosis of chronic obstructive pulmonary disease in lung cancer screening Computed Tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening.,” Respir. Res., vol. 14, p. 59, Jan. 2013.

[4] O. M. Mets, P. A. de Jong, and M. Prokop, “Computed tomographic screening for lung cancer: an opportunity to evaluate other diseases.,” JAMA, vol. 308, no. 14, pp. 1433–4, Oct. 2012.

[5] M. K. Han, B. Bartholmai, L. X. Liu, S. Murray, J. L. Curtis, F. C. Sciurba, E. A. Kazerooni, B. Thompson, M. Frederick, D. Li, M. Schwarz, A. Limper, C. Freeman, R. J. Landreneau, R. Wise, and F. J. Martinez, “Clinical significance of radiologic characterizations in COPD.,” COPD, vol. 6, no. 6, pp. 459–67, Dec. 2009.

[6] D. A. Lynch, J. H. M. Austin, J. C. Hogg, P. A. Grenier, H.-U. Kauczor, A. A. Bankier, R. G. Barr, T. V Colby, J. R. Galvin, P. A. Gevenois, H. O. Coxson, E. A. Hoffman, J. D. Newell, M. Pistolesi, E. K. Silverman, and J. D. Crapo, “CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society.,” Radiology, p. 141579, May 2015.

[7] F. A. A. Mohamed Hoesein, M. Schmidt, O. M. Mets, H. A. Gietema, J.-W. J. Lammers, P. Zanen, H. J. de Koning, C. van der Aalst, M. Oudkerk, R. Vliegenthart, I. Isgum, M. Prokop, B. van Ginneken, E. M. van Rikxoort, and P. A. de Jong, “Discriminating dominant computed tomography phenotypes in smokers without or with mild COPD.,” Respir. Med., vol. 108, no. 1, pp. 136–43, Jan. 2014.

[8] A. Chaouat, R. Naeije, and E. Weitzenblum, “Pulmonary hypertension in COPD.,” Eur. Respir. J., vol. 32, no. 5, pp. 1371–85, Nov. 2008.

[9] J. M. Wells, G. R. Washko, M. K. Han, N. Abbas, H. Nath, A. J. Mamary, E. Regan, W. C. Bailey, F. J. Martinez, E. Westfall, T. H. Beaty, D. Curran-Everett, J. L. Curtis, J. E. Hokanson, D. A. Lynch, B. J. Make, J. D. Crapo, E. K. Silverman, R. P. Bowler, and M. T. Dransfield, “Pulmonary arterial enlargement and acute exacerbations of COPD.,” N. Engl. J. Med., vol. 367, no. 10, pp. 913–21, Sep. 2012.

[10] J. Garcia-Aymerich, E. Barreiro, E. Farrero, R. M. Marrades, J. Morera, and J. M. Antó, “Patients hospitalized for COPD have a high prevalence of modifiable risk factors for exacerbation (EFRAM study).,” Eur. Respir. J., vol. 16, no. 6, pp. 1037–42, Dec. 2000.