uses deep learning to put the knowledge encoded in thousands of medical images and reports at radiologists’ fingertips, decreasing the time spent searching for case-relevant information during the diagnostic process while increasing confidence and reporting quality. Simply mark a region of interest in an image, and SEARCH instantly returns reference cases and associated knowledge plus medical literature necessary for differential diagnosis…within seconds. No more paging through books, guessing the right keyword or consulting various text-based reference search engines. Best of all, contextflow SEARCH integrates directly into your PACS.
We currently have integrations with the following PACS: Agfa, Medigration, Philips, Sectra
View sample integration
provides heatmaps (as shown above) displaying the distribution of disease patterns detected in each scan. We currently support the following patterns in lung CTs:
- airway wall thickening
- ground glass
- mosaic perfusion pattern
- nodular pattern
- pulmonary cavity
- reticular pattern
- tree in bud
- non-specific: includes image patterns not currently incorporated and patterns with no evidence of pathological changes
Furthermore, our flexible AI means that contextflow SEARCH can be extended to additional pathologies, organs and modalities. Brain MRIs are coming soon. contextflow SEARCH is currently being tested with 10+ proof of concept partners throughout Europe. (CE Marked, not for sale in the US)
Put the flow back in your work – spend less time researching difficult cases
Coming soon: contextflow SEARCH with Insights Screen
Simply open any case, and we immediately show lung coverage values and visualize spatial distributions of lung-specific image patterns. Furthermore, we automatically detect and visulize lung nodules, giving you an even bigger boost during the diagnostic process.
SEARCH with Insights Screen is investigational and not available for commercial use.
After using and advising several radiology AI software companies, I can say that what contextflow offers is actually the next generation of AI products to support the radiologist, not replace them. Their general approach means they recognize all relevant findings, not just one.
MD, Chief of Interventional Radiology, Providence Little Company of Mary Medical Centers
We’re very interested in using AI to improve the hospital experience for both doctors and patients; contextflow’s use of deep learning, particularly for lung diseases, is exactly the type of technology we want to evaluate. I very much look forward to the results.
Head of Radiology at Vienna General Hospital
At Dubrava University Hospital, we take pride in providing the best care possible to our patients. There are many AI radiology solutions, but we agreed to the proof of concept with contextflow because their solution provides real value, particularly for new residents.
President of the European Society of Radiology
We are very interested in using tools based on artificial intelligence like contextflow SEARCH to support the decision in the diagnostic process based on the image.
Lluís Donoso Bach
President of the International Society of Radiology