CT Analysis Technology Uncovers Disease Indicators

The algorithms are based on anonymous databases of medical images and clinical data, which helped train them to detect certain markers in medical images. The fatty liver algorithm, for example, segments and calculates the average density of the liver using CT scans of the chest and abdomen.

The platform helps discover chronic diseases earlier by automating CT and magnetic resonance imaging (MRI) scan analysis, which are overwhelming overstretched radiology departments. Earlier detection can give healthcare organizations the opportunity to establish preventative care programs, improving care while reducing the overall patient treatment costs. The Zebra algorithm engine can be deployed in both cloud and on-site configurations, and is designed to integrate into picture archiving and communication systems (PACS), radiological information systems (RIS), and electronic medical record (EMR) systems.

“Regulatory approvals allow us to continue driving adoption of our analytics engine globally, where we see significant interest in countries that have a problematic ratio of radiologists per capita. Providing tools that assist them in delivering better care is critical and is the driving force behind our mission,” said Elad Benjamin, CEO of Zebra Medical Imaging. “We have already begun working with luminary university hospitals in Europe, and will continue to expand our footprint across the region.”

“Machine Learning will change the way radiology is practiced in the coming years,” said Professor Gabriel Krestin, MD, chairman of the department of radiology and nuclear medicine at Erasmus University Medical Centre (Rotterdam, the Netherlands). “I believe it will make radiologists more productive, and I also believe that new use cases and value propositions will emerge as the technology is implemented widely. It is a very exciting time for radiology.”

The Royal College of Radiologists has reported that growth of CT and MRI scans in the United Kingdom is outstripping the increase in available radiologists. While the number of radiologists increased by just 5% between 2012 and 2015, the number of CT and MRI scans increased by 29% and 26% (respectively) over the same time period.

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